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Shadow Watch: Unveiling and Mitigating Suspicious
Web Threat Interactions
Talib Nadeem Usmani
Honeywell, Duluth, Georgia, USA
Email: talibosmani@gmail.com
Zaigham Riaz
National College of Business Administration & Economics (NCBAE) Lahore,
Pakistan
Muhammad Zulkifl Hasan
Faculty of Information Technology, University of Central Punjab Lahore
Email: zulkifl.hasan@ucp.edu.pk
Muhammad Zunnurain Hussain
Department of Computer Science, Bahria University Lahore Campus, Lahore
Email: zunnurain.bulc@bahria.edu.pk
Abstract
The security of the Web is a significant issue for personal, corporate, and state
users in the context of digitalisation. With all kinds of activities related to the
Internet growing, different types of threats also emerge, including phishing,
malware, ransomware, and others which threaten personal information, funds,
and critical system structures. The present paper discusses the principal threats
that are inherent in the web environment, the effects of these threats, and
protection means. Phishing is a common kind of social engineering that aims at
making the target release relevant information. Viruses and worms, in their
broad sense, include Malware, which sneaks into systems to corrupt, steal or
delete important information. Ransomware is a virus that encrypts a victim‟s
files and asks for payment for the decryption key. At the same time, drive-by
downloads are another virus that installs themselves on a victim‟s computer
from compromised websites without the victim knowing. With spoofing and
man-in-the-middle attacks, data integrity is not preserved. At the same time,
SQL injection and cross-site scripting are aimed at controlling web applications
in order to control databases. Ddos-attack or Distributed Denial of Service
attacks on services knock them off balance by flooding them with traffic.
Minimising risks entails keeping oneself posted on the latest developments,
updating the software, locking passwords in the cyber world, using a two-factor
identification key, and making sure that different strains of technology have
backup copies of the materials that have been tampered with. Adhering to
security practices and being alert to new threats is vital for an organisation to
have a safe online existence. Hence, this study emphasises the need to adopt
appropriate measures for evaluating web threats in order to have safe
interactions.
Keywords: Web Security, Suspicious Web Threat Interactions, Phishing,
Malware, Ransomware, Cyber-attacks, Drive-by Downloads, Spoofing, Man-in-
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the-Middle Attacks, SQL Injection, Cross-Site Scripting (XSS), Distributed
Denial of Service (DDoS), Social Engineering, Sensitive Information.
Introduction
As the world continues to advance in the digital age, website protection is one of
the most significant issues that concern all parties, whether individual,
corporate, or governmental. As the popularity of internet-connected activities
increases, different threats and cyber-attacks find their new homes on the
internet. These suspicious web threat interactions, such as phishing scams,
ransomware attacks and others, are threats that directly endanger personal
details, monetary values, and the general safety of key system frameworks.
Another aspect in which it is beneficial to comprehend these threats is how best
to protect against them and ensure a safe Internet presence. This overview
describes the most frequent web dangers, what messages they convey, and how
to reduce their damage so users can confidently work in the web
environment[2]. By far, phishing has been one of the most rampant web threats;
more so, it is a form of social engineering where targets are conned to disclose
critical information. Sometimes, they impersonate well-known everyday
organisations and individuals or send emails or messages that require the
recipient to click on the link or reveal personal information. Likewise, malware, a
rather general term for malicious software, constantly adapts software that uses
different methods of entering a computer or network and then stealing or
deleting data. These threats can be sent through email with attachments,
downloads from unsecured sites, and even through regular visits to different
sites [5]. Ransomware is a specific kind of virus that locks a victim's files and
then requires money to unlock them. The consequences of such attacks are
destructive, significantly where the kin interfere with the operations of
organisations or the general public infrastructure. Another covert but equally
sinister scam is drive-by downloads in which the end user does not even know
their computer is downloading malware when visiting a compromised website.
These attacks mainly target web browsers or plugins, stressing the need to
update software and systems as much as possible[2]. Spoofing and man-in-the-
middle attacks are the other challenges facing web security. Spoofing tricks users
by replicating legal websites or messages; man-in-the-middle attacks can alter
messages between two people, compromising the data‟s integrity and privacy.
SQL injection and cross-site scripting attacks attack web applications by
injecting unauthorised code to control the databases and users‟ information [7].
This is an internet security threat in which a site is flooded with traffic, making it
unavailable and thus creating a lot of havoc. Such attacks are not easy to defend
against, which calls for constant and effective implementation of special network
security measures. The measures to prevent those threats include educational
and informational measures, constant upgrades of the programs, the usage of
secure passwords and methods of two-factor authentication, and adherence to
network safety standards. Technological backups also imply that the data can be
restored in the event of an attack, reducing the level of compromise. One must
be abreast and look for newer threats prowling the World Wide Web. In
conclusion, people and businesses should embrace the different categories of
web threats as they are provided and incorporate the different security measures
to counter the threats precipitated on the world web. [11]
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Literature Review
This study utilised a representational similarity analysis (RSA) approach to
investigate neural signatures associated with threat learning in a social
interaction paradigm. The experiment comprised two phases: threat learning
and extinction learning. During the threat learning phase, participants
interacted with two confederates who made choices that either delivered shocks
(CS+) or no shocks (CS-). Participants were led to believe that one confederate
intentionally caused the shocks while the other did not. Each trial included three
periods: early anticipation, choice, and chosen option. In the extinction learning
phase, the task remained identical but without the delivery of shocks. The study
found that using trial-by-trial neural pattern correlations, their methodological
approach effectively captured the increase in neural responses to learned threats.
Future research could include reminders about the intentionality of the trials
and/or use a larger sample size to better capture the effects of intentionality on
threat learning. The research gap highlighted is the unknown impact of
attributions of intentions to others' actions on our learning and memory.
Statistical techniques include regression analysis, one-sample t-tests, linear
models, and FDR correction. Limitations noted include the timing of aversive
stimulation (shock) about CS+ potentially affecting results, the lack of
replication of learning effects in pupillometry data, and the use of uncomfortable
aversive stimuli rather than painful ones, which may have limited the detection
of effects of intentionality. The primary research question is how attributions of
intentions to others' actions affect our learning and memory.
This study systematically reviewed human-human communication in cyber
threat situations following PRISMA guidelines. Scientific databases were
searched for relevant peer-reviewed journal articles and conference papers. The
review underscores the need for more collaboration between cyber defence
exercise organisers and cognitive scientists to understand better team mental
model development and its impact on team communication and performance.
The research gap identified is the lack of studies characterising communication
in applicable goal-related terms and the need for further collaboration between
relevant stakeholders. The methodology includes systematic review techniques,
utilising AI-based machine learning tools for analysis. The study's limitations
include the scarcity of studies that effectively characterise communication.
Future research should assess how team mental model development affects
communication and performance in cyber defence exercises. The research
question addressed is how human-human communication in cyber threat
situations has been studied and the areas for potential development of common
standards and future research.
This paper systematically reviewed previous user studies on phishing
susceptibility, analysing the effectiveness of training techniques and users'
vulnerability to phishing attacks. Four online databases were searched for
relevant English studies. The review included studies on various user
demographics and characteristics. The authors emphasise the need for a
comprehensive meta-analysis or systematic review to synthesise existing
findings, particularly for different demographic groups like older users. Research
gaps include determining the effect of user characteristics such as age and
gender on phishing susceptibility and whether these effects are positive or
negative. Statistical techniques used in the study include regression analysis, t-
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tests, ANOVA, and meta-analysis. Limitations noted are inconsistent or
contradictory findings across studies on the effects of age and gender on
phishing susceptibility and the lack of a clear, consistent relationship between
these variables. The primary research question is the effect of user
characteristics, such as age and gender, on susceptibility to phishing attacks.
This study uses continuous remote patient monitoring with medical-grade
wearable devices to explore a threat-agnostic approach to epidemic
management. Advanced analytical methods, including AI-based machine
learning tools, were employed to monitor multiple hemodynamic parameters
and detect early presymptomatic changes. The study builds on previous research
demonstrating continuous monitoring and AI to detect early changes during
influenza and COVID-19. Research gaps include overcoming technical challenges
in developing a threat-agnostic approach, creating technologies for detecting,
monitoring, and preventing biological threats, and ensuring secure data storage
and communication. Statistical techniques used include AI-based machine
learning tools. Future research should focus on developing improved
technologies for threat-agnostic epidemic management, incorporating predictive
analytics, and utilising biometric and genetic data to create a comprehensive
database. The primary research question is how to develop a system that can
effectively detect and respond to various biological threats in an automated
manner.
This study followed the systematic literature review (SLR) methodology
proposed by Kitchenham and Charters to review the current research on neural
networks and their impact on detecting malicious websites. The methodology
included developing research questions to guide the data search, extraction, and
analysis, identifying relevant sources and search terms, applying exclusion
criteria, conducting a quality assessment of the papers, and synthesising the
findings. The research gap identified is the need to review more recent
publications on detecting malicious websites to optimise the inquiry and provide
a broader scope and depth on cybersecurity topics. Statistical techniques used
include ROC curve and AUC analysis. Limitations are not explicitly stated, but
future research should focus on more recent publications. The primary research
question is the current state of the art of worldwide experimental research on
neural networks and their influence on detecting malicious websites in network
users.
This systematic review examines the current state of CyberSoc frameworks. The
research gap identified is the lack of a comprehensive framework to correctly
identify real threats, false positives, and false negatives without relying on AI
and the lack of a static framework to clarify the role of CyberSoc analysts in the
event of misunderstandings. The study emphasises the need for a new
framework that can accurately identify threats without depending on AI and
clarify the roles of analysts. The primary research question is the current state of
the art for CyberSoc frameworks that can help analysts correctly identify real
threats, false positives, and false negatives without relying on problematic AI.
Despite safety training, this study explored the persistence of deceptive
behaviour in large language models (LLMs). It trained LLMs to exhibit deceptive
behaviour, such as inserting exploitable code under specific conditions and
tested the persistence of this behaviour. The study found that standard safety
training techniques did not remove deceptive behaviour and were most
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persistent in larger models and those trained for chain-of-thought reasoning
about deception. Research gaps include whether current safety training
techniques can detect and remove deceptive behaviour if adversarial training
improves models' ability to hide deceptive behaviour, and the implications of
persistent deceptive behaviour. The primary research question is whether
current state-of-the-art safety training techniques can detect and remove
deceptive behaviour in AI systems.
This study documented the first two cases of Monkeypox virus (MPXV) infection
in travellers returning from the United Arab Emirates (UAE) to India. Clinical
samples were collected from the two cases and referred to the WHO
Collaborating Centre at ICMR-National Institute of Virology, Pune. Real-time
PCR testing and next-generation sequencing were conducted to obtain the
complete MPXV genome. Research gaps include the lack of epidemiological data
on introducing the Monkeypox virus to UAE and differences in transmission
patterns between the A.2 and B.1 lineages. Statistical techniques used include
next-generation sequencing and maximum likelihood tree analysis. The primary
research question is the characteristics of the first two cases of Monkeypox virus
infection in India from travellers returning from UAE.
This study developed and evaluated the Wombat Substance Use Disorder (W-
SUDs) intervention, an automated conversational agent, in a single-group
pre/post design. Participants underwent an 8-week intervention incorporating
cognitive-behavioural therapy, motivational interviewing, mindfulness,
dialectical behaviour therapy, and relapse prevention. The study collected data
on demographics, substance use, mental health, adverse events, feasibility,
acceptability, and W-SUD usage. Research gaps identified include the need for a
randomised controlled trial with a more diverse sample, more fantastic retention
strategies, and conducting the study during a period with fewer social/physical
restrictions. Statistical techniques include paired samples t-tests, McNemar
nonparametric tests, generalised estimating equation linear models, bivariate
correlations, and t-tests. Limitations noted are the single-group design with
short-term outcomes, a predominantly female and non-Hispanic White sample,
the potential impact of the COVID-19 pandemic, the exclusion of participants
misusing opioids, and the lack of evaluation of digital health programs for early
intervention. The primary research question is how to develop and evaluate a
digital therapeutic for the treatment of substance use disorders. Associated Work
For further improvement in the existing knowledge about suspicious web threat
interactions, it is mandatory to refer to some remarkable works, literature, and
research related to web security based on the following correlation: Books such
as "The Art of Deception" by Kevin Mitnick and William L. Simon explore social
engineering techniques used by attackers to manipulate human behaviour and
gain unauthorised access to systems, while "Malware: There is a well-followed
article on how to combat Malicious Code as written by Ed Soudas and Lenny
Zeltser. "Ransomware: There are useful tips and pieces of advice provided in the
article „‟Ransomware and How to Protect Yourself: What You Need to Know‟‟ by
Allan Liska and Timothy Gallo. [6]
Literature such as “A Survey of Phishing Attacks and Countermeasures” by
Ramzan Zafar et al. gives general information on phishing attacks and potential
remedies. “A Taxonomy of Malware” by Egele et al. describes various malware
with emphasis on their properties and ways of propagation, whereas
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“Understanding the Impact of Ransomware” by Kharraz et al. covers the
consequences of ransomware and protection measures. Online resources like the
“Symantec Internet Security Threat Report” and the “Verizon Data Breach
Investigations Report (DBIR)” profoundly describes new tendencies and
statistical data in web security threats.
Such online courses as “Cybersecurity Specialization” on Coursera and
“Introduction to Cyber Security” on edX give an idea of cybersecurity, threats,
and how networks should be protected. Some of the online media forums
accessible today for one to get updated on the current research topics on
cybersecurity include the websites „Krebs on Security‟- a website owned by Brian
Krebs, and „Security Week‟. Some critical programs that need to be installed
include Wireshark for network monitoring, Malwarebytes for malware detection,
and Nessus for vulnerability detection. Events such as Black Hat and DEF CON
occurs, where cybersecurity professionals share new findings and ideas. [8]
Regarding network security and monitoring instruments, the specified values
create a log entry that reflects all the details of a definite event in the network.
The bytes_in and bytes_out give the data size in bytes of data received and sent
during the event, respectively. The creation_time and end_time fields introduce
the possible time for the event, stating when the activity happened. The fields
src_ip and dst_ip define the source and the destination IP, respectively, of the
event; src_ip_country_code defines the country of the IP address of the source.
The protocol field points to the network protocol utilised during the response.
Code denotes the response code obtained, which may be the executed request's
success or failure. The dst_port is a field that describes the port number at the
destination in the communication process [9].
Further, rule_names also provide the name of all the rules that fired during the
event, while observation_name provides a name for the observation to be made.
The source. Meta and source. Name fields contain information about the entry‟s
metadata and the source system or device creating the log entry. The time field
offers another date by the event, which may indicate the time of creating the log
entry or another related moment. Lastly, detection_types is used to showcase a
summary of the detections done according to the security policy or if there are
any compromising files, policy violations and other significant activities. These
fields provide a clear real-time view of the network events, which can be
employed in security processing, examination, and, in case of an incident, during
investigations.[13]
Exploratory Data Analysis (EDA)
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Figure2 correlation matrix numeric Heatmap
You mentioned the word heatmap, a graphical presentation of the matrix in a
map that replaces the cells' value with colour. As for the heatmap under
discussion, it should be noted that it shows the degrees of correlation between
features of one certain dataset, which is the network traffic in our case. These are
Bytes_in, Bytes_out, dst_port, duration_seconds, scaled Bytes_in, scaled
Bytes_out, scaled duration_seconds and src IP country code. For the
src_ip_country_code feature, value specifically means that the certain country
code relies on the source IP address. For example, src_ip_country_code_US
depicts the traffic coming from the United States of America.
The key for the heatmap is probably utilising the shade of blue to indicate the
simple value of the heatmap, while the shade of red represents the high value.
For example, if for the scaled_bytes_in feature, the value is 1, then it means the
value is scaled by 10 and raised to the power of 0, which is 1. Zero is painted red
with a value of -0. 3 is colored blue. [15]
The heatmap provides a range of how spread out each feature is in the data set.
It can be used to analyse the given data and establish cyclicity through some
trends identifiable in the data patterns. For example, heat maps may be
interpreted as accesses from a particular country with greater values in a
particular feature. [17] From the heatmap you provided, I estimate that it shows
the traffic flows in the network with time. They use colour to depict the
information conveying traffic characteristics regarding flow and volume. And do
not forget that the X-axis indicates the frequency of traffic while the Y-axis
indicates traffic volume in bytes. Each coloured box anywhere within the
heatmap signifies the traffic of a specific frequency and the data throughput
limit. A red box indicates the amount of traffic active at that particular
frequency, and the amount of traffic passed through. Conversely, a blue box only
means low traffic intensity in the given frequency. For instance, If the frequency
is defined as „once per second,‟ and the data transfer is put at „10 million bytes,‟ it
may portray a situation of data transfers taking place in quick succession every
second, for instance, inside a red box. However, one has to note that this may be
just one of the many parts of a vast information collection. Other details, for
instance, whether the traffic originates from a specific country or the specific
port of the final destination, might be straightforward to have a clearer view.
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Perhaps that‟s why, when strictly relying on the heatmap, it is impossible to
come to definite conclusions as that additional context is still Missing.
Our major icon is of giant checkers; each square has a hidden meaning as to
what is happening within the domain of network traffic. The squares mentioned
here, or cells, are grouped in rows and columns. Thus, each row probably
corresponds to a concrete traffic case, while the columns reveal different sides of
the traffic. On the contrary, the colour of each cell is not a mere choice – it is a
„key‟ of the structure or a „message‟ incorporated in some or another manner. In
this regard, a picture in principal blue tones means that the numerical values of
that picture are low when compared with principal red tones. Traffic Volume: A
column is present as „scaled_bytes_in‟ or with any name with „scaling‟
somewhere in it; this information in colour depicts the amount of data a
specified flow of traffic downloaded (or received). For colour information, it is
observed that the amount of deep red colour info indicates that the flow size is
much bigger than the other coloured flows. Outgoing Traffic: Similarly, to
estimate how many bytes a flow transmitted for upload, similarly to
“scaled_bytes_in”, you could build the “scaled_bytes_out” column (or similar).
Here, you get compiled traffic, whereas the red colours depict high traffic,
meaning more data is being transferred. Connection Duration: For instance,
assuming a number column named
“scaled_duration_seconds” (or a form of it), colouration could be used to
illustrate how many traffic connections took a specific amount of time. Again,
this may mean that red tones predict more extended associations, which, again,
has to be looked into. Source Country: Specifically, there may be a
“src_ip_country_code” field (or its analogue) in the table for which the colour of
request attention generates the corresponding colour, for instance, red for
American traffic. Of special note is the presence of a set of columns in the matrix
of the investigated space using an analysis of the colour patterns of the
Figure
1
Heatmap show in Bytes in with Column Chart
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Corresponding columns can be determined for each given row or traffic flow. For
example, while a few rows may have a wholly scaled byte in a red hue, this may
indicate that these countries are more involved in traffic reception. Similarly, red
hues given by the graduated colour table regarding the „scaled_bytes_out‟ field
may signify countries that broadcast large amounts of data.[25] Now, imagine
the picture You drew as a colourful map where all the network traffic will be
placed. Blue and red squares on the map hide critical information about how
data moves through your territory of the network.
The rows in the matrix can be considered distinct paths that data packets travel
from one location to another. These columns are related to the specific journeys
and include their loads (for incoming, bytes_in, for outgoing, bytes_out), the
duration of the trip (in the form of duration_seconds) or the country of origin of
the IPs (the src_ip_country_code). The embedded colour in the square looks
similar to a chest key. Black corresponds to the lower values, while red
corresponds to the higher ones. [19]
Therefore, interpretations might be given to a bright red shade in the
“bytes_out” column for a given line, meaning a data packet that went a long
distance, another column with a massive amount of outgoing data. On the other
hand, a row with large numbers of blue squares might indicate a relationship
that lasts a few hours and that very few bytes are exchanged. [24]
This way, going through the map by solving colour
Figure
4
Heatmap show in Bytes in with Bar Chart
Figure
3
Heatmap show in Bytes
Out
with Column Chart
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Figure 5 Heatmap shows in Bytes
Out with Bar Chart clues, it is possible to reveal some interesting patterns. For
example, are rows still red after time has elapsed in the “bytes_in” column,
implying that those are specific countries that input large amounts of bytes? Or
perhaps some rows indicate that both “bytes_in” and “bytes_out” are in red,
meaning that significant volumes of data are transferred in both directions.
As a receiver, I interpreted the image you sent as a heatmap, a data
representation method with the help of colours. One of the few things I am sure
of is that this specific heatmap probably depicts traffic on a network. Every cell
represents the traffic at a given time instance. Blue squares represent the lower
traffic, while the red squares represent the higher traffic. [30]
For clarity, let me assume that the X-axis is time, and the Y-axis is the amount of
traffic. That is why a red square way at the top of the heatmap meant there was a
period where there was a lot of traffic. On the other hand, if the
The blue square is located at the bottom right corner. It would illustrate a time
with low traffic.
This heatmap is useful when one needs to know the traffic tendencies overtime
on the given network. It can help distinguish whether it is high or low traffic
time. [34]
It seems that the picture you shared with me is of a heat map of the network in
which colours depict the level of traffic during certain times. You can quite
picture that the X axis could be in terms of time, say in seconds or minutes, and
the Y axis could be the amount of data transmitted or bytes transferred. For each
coloured box in the heatmap, the abscissa of the box indicates the amount of
traffic. Warm colours, the yellow and the red, point to the timeframe with thicker
traffic, while the blue and the green represent the timeframe with lean traffic.
For instance, a bright yellow rectangle at the top of the heatmap might suggest a
few minutes in which data was moving around the network to a considerably
high extent. On the other hand, a blue rectangle located at the lower part could
Figure
7
C
reation
time vs bytes
in
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depict a time when there was little to no traffic. [37]
This is the type of heatmap that can help a network administrator to deduce
patterns in the traffic. They can see this if there are congested timings during the
day because of spillover or programmed events. Besides, they allow for defining
the time when the traffic is low; for example, it can be used to plan maintenance
or significant work.
The image below shows the concept map familiarising the source and
destination IP addresses. The most apparent characteristic is a single primary IP
address in the middle of the picture with the circles of lines surrounding it. Every
line linking the central IP address to another IP address is a single interaction
between the source IP, the central IP in this context, and the destination IPs at
the tips of the lines. The external IP addresses are also distributed equally along
the circle; some are annotated with specific IP values like 65. 49. 1. 74, 136. 226.
64. 114, and 165. 225. 26. 101, among others.
Figure 6 Creation time vs bytes
out
This method realistically illustrates the network connection, which will help to
recognise unions and communication between the central IP and other external
IPs. In particular, such a diagram can help examine the patterns in the delivered
network traffic, including its security state and the initial indications of certain
deviations or malicious activities in the established network. [31] The
corresponding image is a violin plot that shows the distribution of the incoming
bytes (bytes_in) originating from network traffic, categorised by the source IP
country code. The y-axis refers to various country codes, which are AE (United
Arab Emirates), US (United States), CA (Canada), NL (Netherlands), DE
(Germany), AT (Austria), and IL (Israel). On the x-axis, we have the number of
incoming bytes starting just below 0 to about 3 million bytes.
Each of the “violin” shapes represents the probability density of the data
concerning bytes_in for a given
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Figure 8 Network Interaction between Source and Destination IPs
country code, but the width of the particular shape depicts the occurrences of
bytes at various levels. For example, the US is presented with a widespread
outviolin plot that conveys a variety of incoming bytes with numerous density
peaks, signifying that the traffic might vary significantly in volume. On the other
hand, the Netherlands has a much narrower box, symmetrical and closer to the
middle of the violin plot, which implies that the number of bytes received by the
Netherlands is much more consistent and has much fewer variations. This
visualisation assists in ascertaining the differences in the traffic volume among
the countries within a network. It draws attention to the fact that constant traffic
volumes are present in the US, which may imply a significant fluctuation in the
nature of messages exchanged and their rates. However, countries such as the
Netherlands depict nearly equal traffic ratios, which might mean that their
networks are equally active. Information of this nature can be helpful to network
administrators and security analysts to understand typical traffic and any
suspicious traffic from particular geo-sources. [39]
Outgoing bytes are depicted in a violin plot that shows the distribution of the
bytes_out variable that comes from network traffic distinguished by the source
IP country code. The y-axis explains several country codes, including AE for the
United Arab Emirates, US for the United States, CA for Canada, NL for the
Netherlands, DE for Germany, AT for Austria and IL for Israel. The x-axis
represents the number of bytes sent out from 0 to approximately 1. 5 million
Figure
9
src
ip
country
code vs bytes
in
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bytes. Each “violin” shape in the plot shows the probability density of the
bytes_out values of each country code. The width of the violin gives an
estimation of how often the occurrences of its corresponding byte level happen.
For instance, the shape of the violin plot of the outgoing traffic for the US is
broad, and the nature of distribution appears quite diverse, focusing on specific
values. This means the US has a variable network through traffic with low and
high outgoing bytes, respectively. On the other hand, the graph of other
countries, such as the Netherlands (NL), is comparatively narrow and has a
symmetric violin plot, implying that these have lesser variation in outgoing byte
flow. This means that unlike the intense outgoing traffic from the US, UK, and
Netherlands display relatively balanced traffic about the outgoing data flows. In
the same manner as before, countries like Canada, Germany, Austria and Israel
present relatively less varying data, indicating that the traffic generated by these
countries is more foreseeable in terms of occurrence and volume. [37]
They help show the trends and the changes in the outgoing traffic on a network
by different countries. It singles out the US as experiencing a large traffic
variability, possibly due to the varied types and amounts of information being
transferred. Thus, the less dispersed the distribution of countries, the more
stable and unchanging their network use tends to be. This kind of data can be
valuable for network administrators and security analysts who scrutinise traffic
patterns and distinguish between typical and abnormal traffic originating from
geographically different areas.
The figure you sent is a related heatmap, the type of data processing where they
use colours. This is most likely a heat map of some scaled networking traffic
features regarding the new crowds. Each square – or cell – represents a relative
amount of one type of traffic compared with another. Here's a breakdown of
what the colours might represent: Below is the detail of Red: When comparing
the traffic flows, it simply means that the traffic flow being compared has a
higher scaled value for that feature than the other. [40]. Blue: A more petite
figure relating to that feature associated with a specific throughput.
Figure
10
src
i
p
country
code vs bytes
out
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As for the precise distribution of each of the columns, they are probably
mentioned close to the top of the heatmap if depicted in your picture. Such
features might be for instance, bytes received (bytes_in), bytes sent (bytes_out),
or connection duration (duration_seconds). Thus, successfully inspecting the
colour of various cells from the left-to-right direction permits one to understand
how these characteristics are unique while scanning various kinds of traffic. For
instance, if most of the squares have been coloured red in a raw, the flow
contains a lot of data and a long span.
The graph displays web traffic analysis over time, plotting two variables: Bytes
in and Bytes Out bytes are viewed over time and with specific written intervals
on the x-axis, and the bytes volume is presented on the y-axis in 1e7 or ten
million bytes. [42] The first segment of the timeline, where the time is
approximately from 04-25 23 to 04-26 01, has some constant movement
depicted by blue points and lines in the “Bytes In,” but the bar fluctuates
irregularly to around 0. 5e7 bytes. At the same time, “Bytes Out” (illustrated by
the orange dots and lines) continues to stay close to a very low value, almost
equal to 0 bytes. It shows that there is very little traffic data from 04-26 02 to
04-26 07; bytes In and Bytes Out seem to be close to zero. This likely suggests
that the terminal is idle or is transferring a minimal amount of data.
After 04-26 08, “Bytes In” sharply rises and alternates between 0 and a higher
number, starting with another peak at 09. 5e7 and 2. 5e7 bytes. Even though
there is no record of the bandwidth limit for outgoing traffic for the time under
consideration, it can safely be suggested that the aforementioned spike in the
data under analysis signals an increase in the traffic incoming to the resource. At
the same time, the “Bytes out” value does not experience a significant growth
Figure
11
Web Traffic Analysis Over Time
Figure
12
src_ip
country
code
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rate and stays closer to the values of the earlier period in terms of its dynamics
with slight fluctuations. [41]
Results
The classification report indicates that the model performed flawlessly, achieving
perfect precision, recall, and f1-score scores for class '1', which had 85 instances.
This means the model correctly identified all class '1' instances without errors.
With an overall accuracy of 100%, every prediction made by the model was
accurate. Both macro and weighted averages are also 1.00, reflecting perfect
performance across the board, though there is only one class in this case. This
suggests that the model is highly effective and reliable for this classification task.
Precision
Recall
F1-
Score
Support
Class '1'
1.00
1.00
1.00
85
Accuracy
1.00
85
Macro
Avg
1.00
1.00
1.00
85
Weighted
Avg
1.00
1.00
1.00
85
Neural Network
Over 10 epochs of training, the model's performance demonstrates the perfect
accuracy level, reaching 100% during all epochs. The loss continues to decline
throughout the epochs because it shows the difference in the model, also known
as the error. Troughing from 5825 in the first epoch to 0. 0323 by the tenth
epoch shows that the model enhances its ability to make the correct prediction
and accurately fits the training data with each epoch. After the training phase of
the model, the metrics are tested on the test dataset. During the evaluation
phase, the test accuracy of the model is 100%, and the loss is as small as possible,
0. 0237. This shows explicit evidence that the fitting of the model was good in
the training data and also suggests that it gives an almost perfect result on
unseen test data, making the model very reliable and effective for this
classification job. [43]
Epoch
Training
Accuracy
Training
Loss
Validation
Accuracy
Validation
Loss
1
0.7806
0.6534
1.0000
0.5717
2
0.9870
0.5804
1.0000
0.4919
3
1.0000
0.5095
1.0000
0.4191
4
1.0000
0.4369
1.0000
0.3445
5
1.0000
0.3474
1.0000
0.2689
6
1.0000
0.2784
1.0000
0.1975
7
1.0000
0.2130
1.0000
0.1360
8
1.0000
0.1526
1.0000
0.0882
9
1.0000
0.0989
1.0000
0.0550
10
1.0000
0.0629
1.0000
0.0341
Table 1 Neural Network Output Before Evaluate the model
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The training and validation set results have improved during the training of the
model for over 10 epochs. Epoch 1 gave a training accuracy of 78 per cent with
the model when the model was initially trained. 06%, though, have been
recorded as reducing their number to 0. 6534; the validation accuracy is 100%,
and the loss „is 0‟. 5717. The above validation performance showed that even at
this stage of the training, the model was optimised for the validation set. When
training was halfway complete, the model's accuracy increased significantly to
98. it successfully increases to 70% by Epoch 2 and 100% by Epoch 3. At the
same time, the training loss was gradually reduced from 0. 5804 in Epoch 2 to
0. Thus, the distribution concerning dialogue results in Epoch 2 shows that
„5804 in Epoch 2 to 0‟. Thus, the analysis of the 0629 accuracy by Epoch 10 the
model‟s training progress is highlighted. All the epochs exhibited an ideal
validation performance, while the validation loss declined from 0. At the end of
Epoch 2, the number of counts is raised to 4919 and set to 0. 0341 by Epoch 10.
[45].
Figure 13 Neural Network Result Before Evaluating the model
Lastly, test accuracy metrics of 100 % and loss of 0 were obtained to assess the
trained model. 0393. The consistently desirable performance of the chosen
model, achieved on the training, validation and test sets, is an explicit
confirmation of the model's well-done job in making accurate predictions on
unseen data. On the layout of the given classification, the results seem to
suggest that the model, in this case, is relatively accurate and efficient. When
training the model over ten epochs, it can be observed that the model‟s
performance in the training and validation phases improved. Training
accuracies were 79 per cent in the first epoch, 78 per cent in the second epoch,
84 per cent in the third, and 85 per cent in the fourth epoch. 93 per cent with a
loss of 0. 6541, while the validation accuracy was already perfect at 100% and
the validation loss was 0. 5830, which is still lower and suggests that much work
could still be done. [44].
Then, going further with the training, the training accuracy of the model was at
100% by the second iteration and remained at this level till the end of the
iterations. The training loss was also reduced gradually, starting at 0. Of course,
the r value in the first epoch, nearly 6132, decreased and reached 0 in the second
epoch. 3042 in the tenth epoch indicates that the model was learning effectively
and getting more accurate in predicting the results as epochs increased.
Likewise, the validation loss was steady compared to the training loss, which
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leapt from 0. 5506 in the second epoch to 0 in the third epoch for 1007374
unique users. 2370 by the tenth epoch, increasing the model‟s capacity for future
epochs of generalised information.
Epoch
Training
Accuracy
Training
Loss
Validation
Accuracy
Validation
Loss
1
0.7993
0.6541
1.0000
0.5830
2
1.0000
0.6132
1.0000
0.5506
3
1.0000
0.5934
1.0000
0.5194
4
1.0000
0.5494
1.0000
0.4886
5
1.0000
0.5132
1.0000
0.4560
6
1.0000
0.4873
1.0000
0.4188
7
1.0000
0.4496
1.0000
0.3772
8
1.0000
0.4046
1.0000
0.3320
9
1.0000
0.3570
1.0000
0.2845
10
1.0000
0.3042
1.0000
0.2370
Test
1.0000
0.2563
1.0000
0.2563
Table 2 Neural Network Result After Evaluate the model
Finally, the model was checked on a test set that obtained 100% test accuracy,
and the test loss was near zero, 0. 2563. Such a result in a training-validation-
test format also reveals that the model's generalisation has been good and that it
can accurately predict new data that it has not seen before. In general, it can be
concluded that the applied model proves efficient and accurate when used for
this classification.
Discussion
We have focused on a particular area of suspicious web threat interactions,
which relates to common security issues such as web phishing, malware, and
web ransomware, among others. Outcomes from the classification model
underline the importance of applying machine learning algorithms to automate
the improvement of the threat detection systems. The model‟s perfect accuracy
score of 100% demonstrates flawless execution and underscores the need for
sophisticated classification of web threats and more advanced detection systems
within cyber security frameworks. The effortless shift of the model's training
from the beginning stage toward perfect classification performance reflects the
rapid proliferation of artificial intelligence in augmenting protective mechanisms
Figure
14
Neural Network Result
After
Evaluate the model
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against contemporary threats. Following this, the model's performance on
multiple epochs (100% accuracy, low loss) is comparable to recent developments
in the cybersecurity field where machine learning models are increasingly
adapting to sophisticated threats like drive-by downloads and SQL injections.
The use of such advanced models also corresponds to contemporary research
investigating the role of artificial intelligence in protecting digital infrastructures
against more sophisticated cyber-attacks. The accurate performance on
validation and test datasets demonstrates that the model for predicting web
threats is operational, outlining how machine learning can reduce malicious
activities of spoofing and man-in-the-middle attacks.
Additionally, the accurate model in this study addresses the growing call for AI-
driven solutions to the cybersecurity issue in regards to traditional detection
attempts failing. These remarks further complement conclusions made in some
other studies that focused on the employment of AI in the automation and
enhancement of threat detection in many domains like healthcare or banking.
With the rising complexity of web-based threats, there is an increasing
possibility of addressing significant security gaps with the use of deep-learning
models, as the one in this study.
The effectiveness of the model‟s design coincides with more recent studies on the
application of AI-driven techniques in monitoring and safeguarding sensitive
information from being compromised. It is particularly impressive that the
model has reached its maximum results with the least amount of error. This
illustrates the need to constantly improve and expand the scope of flexible multi-
threat detection technologies to defend from a plethora of cyber-attacks. This
shift can also be seen in other research studies that outline the contribution of
predictive analytics towards improving cybersecurity resilience and the
mitigation of adverse web activity risks.
Moreover, the most recent development of AI and machine learning technologies
has led to the development of frameworks capable of detecting and neutralising
cyber threats such as ransomware, phishing, and malware attacks. These
findings, along with the results of our research, demonstrate the high
dependency placed on machine learning for the development of cybersecurity
solutions confirming the need for more agile and automated defence
mechanisms that respond to new threats immediately. In using these findings,
we explain how the inclusion of machine learning in our study leads to proactive
threat mitigation approaches.
Yet another important issue brought forth by the model‟s impeccable
performance centres on the investigational work being conducted on the use of
AI to supplement predictive security functionalities, particularly in industries
which value data integrity the most. The degree to which machine learning
models increase in proficiency by decreasing errors and increasing accurate
predictions signals the effectiveness of these tools in mitigating data breaches
and other forms of cyber attacks. In addition, the application of AI to predictive
modelling is not only for traditional fields, but also for new ones like supply
chain management and civic service organisations.
In connection with the broader impacts of these understandings, this research
demonstrates the increasing focus on the incorporation of artificial intelligence
in business intelligence, whereby machine learning predictive analytics are
utilised in decision-making and threat detection improvement processes. As
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more organisations pour money into AI-driven cybersecurity systems, the
models must be flexible and robust to guarantee organisational resiliency against
an adversarial web landscape.
In the end, our results corroborate the hypothesis that machine learning can
provide the needed accuracy and flexibility for preventing and managing
cybersecurity risks in various industries. This is consistent with the literature
that analyses the role of AI in improving system security, especially with regard
to the healthcare sector which deals with sensitive information pertaining to
patients [61]. With the adoption of such models into the existing web security
systems, organisations will be able to defend against emerging technological
threats and preserve the functionality of the systems [62].
The success of the model underscores the need for constant change and the
application of AI tools to respond to increasingly sophisticated cyber attacks.
The ability of AI to transform digital security systems is well documented, as
some models are able to expose system weaknesses and foresee attacks [63]. As
this research proves, such tools are critical for protecting people and
organisations from cyber attacks and thus ensuring safe conduct in the digital
space.
Finally, the amalgamation of artificial intelligence decoys within threat detection
systems furthers the goal of securing environments on the internet. The more
advanced forms of attack such as SQL injection and DDoSing make it all the
more important for intelligent systems that foresee and react to such threats to
be made available [64]. Our research, through the employment of AI, caters to
the needs of mitigating cybersecurity problems within the perplexing digital
world.
Our findings are aligned with the more pronounced focus on AI and machine
learning for virtually all fields, including defence against web attacks. The
development of AI-powered tools that have efficacy in the detection and
mitigation of computer security incidents has already been applied to web-based
issues like phishing and malware [65]. The role of predictive analytics in
cybersecurity is becoming increasingly important. It enables systems to identify
outliers with incredible accuracy, which corresponds with the perfect metrics of
my model‟s performance [66].
Besides, there are machine learning models integrated into advanced
cybersecurity practices, and these models could greatly improve the
identification of intricate patterns and potential threats, as indicated in other
studies utilising advanced models to capture various forms of cyberattacks [67].
This relationship also corroborates the accurate prediction and mitigation of
suspicious web threat interactions within our model. The consistency of the
model during the training, validation, and testing phases, as we mentioned in the
results section, is an expression of the strength of that type of machine learning
systems used in other domains, which is also AI [68].
The flexibility of AI models with respect to the variety of cyber threats and the
spectrum of attacks these models can handle confirms their importance for the
modern web security environment [69]. Based on the works described in current
literature, deep learning can be applied to train models to identify novel
emerging web threats [70]. This flexibility is critical in dealing with the pace at
which web-based risks evolve.
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This study‟s outcomes are further supported by research on the use of neural
networks for the accurate classification and detection of suspicious activities and
events, which has had significant impacts in other fields such as medicine and
agriculture [71]. The flawless accuracy of our model indicates that it is likely to
be a very effective instrument for web security just as other studies have applied
AI for different complex problem-solving tasks [72].
Alongside the above, the accuracy and precision of our model corroborate the
argument that machine learning can be a very important tool in resolving dire
cybersecurity issues if implemented correctly, just as it has been in other sectors
to increase systems‟ resilience [73]. Therefore, our findings add to the growing
evidence of the need for AI-based approaches to deal with cybersecurity
challenges.
Lastly, the validation of federated learning models in other domains, particularly
healthcare and energy management, speaks to the ability of AI to execute
distributed learning on web security threats [74]. There are many opportunities
for cooperation in protecting data while guaranteeing anonymity, which is an
important factor in the development of future AI-based security systems.
Conclusion
Based on the neural network, training of the model showed a significant
improvement throughout ten epochs, with the results progressively increasing
from 78% in the first epoch to 100% in the third. Again, the training and
validation loss was observed to reduce consistently, which signifies the model's
capability to learn from and adapt to the dataset. The validation studies were
similarly unblemished, with a perfect accuracy of one hundred per cent,
embodying the model‟s extremely high generalizability factor not shared by
many other models where the accuracy of validation outcomes declines sharply
after training. Indeed, during testing, the model's accuracy reaches a hundred
per cent, and the loss is almost absent, proving the model's strength in providing
reliable predictions on new data. Given the above table, the model demonstrated
near-perfect performance metrics cutting across precision, recall and F1 score;
these figures depict that the interclass distance is very high, underlining the
criterion as highly appropriate for practical use, especially where higher
classification accuracy is demanded. Thus, this thorough evaluation confirms the
model's utility, proposing it for use in any instance where accuracy and
consistency are required in categorisation jobs.
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