Qussai Yaseen

• Ph.D.
• Professor (Associate) at Ajman University

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN). As the number of connected vehicles continues to grow, the importance of ensuring security and privacy within VANETs becomes paramount. Robust authentication protocols are essential to safeguard vehicle-to-veh...

Enhancing the security of Wireless Sensor Networks (WSNs) improves the usability of their applications. Therefore, finding solutions to various attacks, such as the blackhole attack, is crucial for the success of WSN applications. This paper proposes an enhanced version of the AODV (Ad Hoc On-Demand Distance Vector) protocol capable of detecting bl...

In the published article [...]

The number of exploits of Docker images involving the injection of adversarial behaviors into the image’s layers is increasing immensely. Docker images are a fundamental component of Docker. Therefore, developing a machine learning classifier that effectively predicts and classifies whether a Docker image contains injected malicious behaviors is cr...

Several supervised machine learning models have been proposed and used to detect Android ransomware. These models were trained using different datasets from different sources. However, the age of the ransomware datasets was not considered when training and testing these models. Therefore, the detection accuracy for those models is inaccurate since...

This paper proposes a machine learning model based on the co-existence of static features for Android malware detection. The proposed model assumes that Android malware requests an abnormal set of co-existed permissions and APIs in comparing to those requested by benign applications. To prove this assumption, the paper created a new dataset of co-e...

The Android platform has become the most popular smartphone operating system, which makes it a target for malicious mobile apps. This paper proposes a machine learning-based approach for Android malware detection based on application features. Unlike many prior research that focused exclusively on API Calls and permissions features to improve detec...

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. This paper prese...

In the past decade, mobile devices became necessary for modern civilization and contributed directly to its development stages in defining mobile information access. Nonetheless, along with these rapid developments in modern mobile devices, security issues rise dramatically, and malware is the most concerning of all. Therefore, many studies and res...

The Internet of Things (IoT) applications are growing immensely. However, malicious IoT devices are major concerns that threaten the security of IoT applications. This paper proposes an intelligent reputation system for IoT devices using edge computing and cloud computing infrastructures. The proposed system can be used to mitigate the effect of ma...

Unsolicited emails such as phishing and spam emails cost businesses and individuals millions of dollars annually. Several models and techniques to automatically detect spam emails have been introduced and developed yet non showed 100% predicative accuracy. Among all proposed models both machine and deep learning algorithms achieved more success. Na...

Android still has the first rank in terms of market share in comparing to other operating systems. Due to its flexible publishing policy, companies are developing many applications in order to serve user needs. The official market of Android Google Play store is characterized by its support for the unofficial stores, and it does not impose many res...

Security of cloud computing is a major concern for both organisations and individuals. The cloud users want to make sure that their private data will be safe from disclosure of both outsiders of the cloud as well as from (probably malicious) insiders (cloud agents) of the cloud. Hence, insiders' threats of the cloud computing is a major issue that...

Security of cloud computing is a major concern for both organisations and individuals. The cloud users want to make sure that their private data will be safe from disclosure of both outsiders of the cloud as well as from (probably malicious) insiders (cloud agents) of the cloud. Hence, insiders’ threats of the cloud computing is a major issue that...

Traffic analysis has many purposes such as evaluating the performance and security of network operations and management. Therefore, network traffic analysis is considered vital for improving networks operation and security. This paper discusses different machine learning approaches for traffic analysis. Increased network traffic and the development...

The number of applications that blockchain era suggests are vastly growing each day in almost all industries. One of the most dialectical research areas using blockchain concepts is the aspects of intellectual property and copy rights. In this paper, we propose a novel framework of a hybrid model which combines consortium blockchain and private blo...

Classification and clustering techniques are used in different applications. Large‐scale big data applications such as social networks analysis applications need to process large data chunks in a short time. Classification and clustering tasks in such applications consume a lot of processing time. Improving the performance of classification and clu...

This paper discusses the envelopment analysis for selected cities. It uses the DEA (Data Envelopment Analysis) for both input and output oriented methods using a data set of 176 cities, and tests several classifiers in classifying the dataset using both cross-validation and percentage split.

Spams and spamming methods are increasing vastly and getting complicated due to the rapid growth in networks, communications and technologies. Therefore, spam filters need to be tested continuously to evaluate their capabilities and efficiency in detecting and preventing spams. This paper discusses spams filtering problem using Bayesian classifier....

Collusion attacks are among the major security concerns nowadays due to the growth exposure in networks and communications. Internet of Things (IoT) environments are an attractive target for such type attacks. This paper discusses the problem of collusion attacks in IoT environments and how mobility of IoT devices increases the difficulty of detect...

The request-response paradigm that consists of policy decision points (PDPs) and policy enforcement points (PEPs) is used for access control in Cloud computing. The model uses PEP-side caching to increase the availability and reduce the processing overhead on PDP. This paper shows that using PEP-side caching can be exploited by insiders to bypass c...

This paper discusses the problem of collusion attacks in Internet of Things (IoT) environments and how mobility of IoT devices increases the difficulty of detecting such types of attacks. It demonstrates how approaches used in detecting collusion attacks in WSNs are not applicable in IoT environments. To this end, the paper introduces a model based...

Selective forwarding is a major problem in wireless sensor networks (WSNs). The nature of sensor environments and the sensitivity of collected measurements in some fields such as war fields increase the need to prevent, detect, or mitigate the problem. One of the most used countermeasures for such problem is the use of voting system based on watchd...

he Map Reduce paradigm is now considered a standard platform that is used for large scale data processing and management. A major operation that the Map Reduce platform relies on greatly is tasks scheduling. Although many schedulers have been presented, task scheduling is still one of the major problems that face Map Reduce frameworks. Schedulers n...

The Map Reduce paradigm is now considered a standard platform that is used for large-scale data processing and management. A major operation that the Map Reduce platform relies on greatly is tasks scheduling. Although many schedulers have been presented, task scheduling is still one of the major problems that face Map Reduce frameworks. Schedulers...

Big data is a main problem for data mining methods. Fortunately, the rapid advances in affordable high performance computing platforms such as the Graphics Processing Unit (GPU) have helped researchers in reducing the execution time of many algorithms including data mining algorithms. This paper discusses the utilization of the parallelism capabili...

Insider threat poses huge loss to organizations since malicious insiders have enough knowledge to attack high sensitive information. Moreover, preventing and detecting insider attacks is a hard job because malicious insiders follow legal paths to launch attacks. This threat leads all kinds of attacks in banking systems in the amount of loss it caus...

Text classification is one of the fundamental tasks in information retrieval and text mining. A recent approach for classification is to employ a clustering algorithm to separate textual data to clusters. A very common algorithm for this purpose is the Fuzzy C-Means (FCM) algorithm. However, such algorithms face a serious problem when dealing with...

Intrusions detection is one of the major issues that worry organizations in wireless sensor networks (WSNs). Many researchers have dealt with this problem and have proposed many methods for detecting different kinds of intrusions such as selective forwarding, which is a serious attack that may obstruct communications in WSNs. However, as the applic...

Rapid development of wearable devices and mobile cloud computing technologies has led to new opportunities for large scale e-healthcare systems. In these systems, individuals’ health information are remotely detected using wearable sensors and forwarded through wireless devices to a dedicated computing system for processing and evaluation where a s...

Most security related research for cloud computing focuses on attacks generated outside the cloud system. However, insider attackers are more challenging and can cause severe impacts on the cloud system stability and quality of service. In this paper, we propose an insider threat model using a knowledgebase approach. Knowledgebase models were used...

Cloud security has become one of the emergent issues because of the immense growth of cloud services. A major concern in cloud security is the insider threat because of the harm that it poses. Therefore, defending cloud systems against insider attacks has become a key demand. This work deals with insider threat in cloud relational database systems....

Efficiently scheduling MapReduce tasks is considered as one of the major challenges that face MapReduce frameworks. Many algorithms were introduced to tackle this issue. Most of these algorithms are focusing on the data locality property for tasks scheduling. The data locality may cause less physical resources utilization in non-virtualized cluster...

Most of the security related research for cloud computing focuses on attacks that are generated outside the cloud system and aims to gain unauthorized access to the cloud resources and data. However, the insider attackers are more challenging and can cause a severe impact on the cloud system stability and quality of service. In this paper, we propo...

Most research in Arabic roots extraction focuses on removing affixes from Arabic words. This process adds processing overhead and may remove non-affix letters, which leads to the extraction of incorrect roots. This paper advises a new approach to dealing with this issue by introducing a new algorithm for extracting Arabic words' roots. The proposed...

Efficiently scheduling Map Reduce tasks is considered as one of the major challenges that face Map Reduce frameworks. Many algorithms were introduced to tackle this issue. Most of these algorithms are focusing on the data locality property for tasks scheduling. The data locality may cause less physical resources utilization in non-virtualized clust...

Map Reduce is a parallel and a distributed computing framework used to process datasets that have large scale nature on a cluster. Due to the nature of data that needs to be handled in the Map Reduce problem which involves huge amount of data, many problems came up that are of great importance. Scheduling tasks is considered one of these major prob...

PEP-side caching is used in request-response access control mechanisms to increase the availability and reduce the processing overhead on PDP. Nonetheless, this paper shows that using this approach may open an insider threat port that can be used to bypass access control models in cloud and distributed relational databases. Moreover, the paper prop...

We have developed a model to predict and prevent potential damage caused by malicious transactions in a database system. The model consists of a number of rules sets that constrain the relationships among data items and transactions. It uses a graph called Predictive Dependency Graph to determine data flow patterns among data items. The model offer...

Cloud security is one of the major issues that worry individuals and organizations about cloud computing. Therefore, defending cloud systems against attacks such asinsiders' attacks has become a key demand. This paper investigates insider threat in cloud relational database systems(cloud RDMS). It discusses some vulnerabilities in cloud computing s...

This paper investigates insider threat in relational database systems. It discusses the problem of inferring unauthorized information by insiders and proposes methods to prevent such threats. The paper defines various types of dependencies as well as constraints on dependencies that may be used by insiders to infer unauthorized information. It intr...

This paper investigates the issues of malicious transactions by insiders in database systems. It establishes a number of rule sets to constrain the relationship between data items and transactions. A type of graph, called Predictive Dependency Graph, has been developed to determine data flow patterns among data items. This helps in foretelling whic...

Insider threat is a critical problem due to the immense harm that it poses to organizations. This paper investigates this problem in relational database systems. Generally, defending systems against insider threat may require rejecting insiders' requests to access some data items. The paper focuses on preventing unauthorized knowledge acquisition b...

This paper demonstrates how to prevent or mitigate insider threats in relational databases. It shows how different order of accesses to the same data items may pose different levels of threat. Moreover, it states the conditions that are required to regard a data item as expired. In addition, it introduces the two different methods of executing insi...

This paper demonstrates how to mitigate insider threat in relational databases. Basically, it shows how the execution of the same operations in different orders poses different levels of threat. The model presented in this paper organizes accesses to data items in some sequence so that the expected threat is minimized to the lowest level. In additi...

This paper investigates the problem of malicious modifications by insiders in relational databases. It presents an algorithm that shows how to construct insiders' Modification Graphs, which demonstrate the authorized and unauthorized data items in which insiders can make changes. Two methods are provided to prevent modification attacks. The first m...

This paper investigates the problem of insider threat in relational database systems. It defines various types of dependencies as well as constraints on dependencies that may be used by insiders to infer unauthorized information. Furthermore, it introduces the Constraint and Dependency Graph (CDG), and the Dependency Matrix that are used to represe...

The performance of spatial queries depends mainly on the underlying index structure used to handle them. R-tree, a well-known spatial index structure, suffers largely from high overlap and high coverage resulting mainly from splitting the overflowed nodes. Assigning the remaining entries to the underflow node in order to meet the R-tree minimum fil...

This paper investigates the problem of knowledge acquisition by an unauthorized insider using dependencies between objects in relational databases. It defines various types of knowledge. In addition, it introduces the Neural Dependency and Inference Graph (NDIG), which shows dependencies among objects and the amount of knowledge that can be inferre...