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Snort is open source intrusion detection system based on signature detection. In the paper we present information about the second version of anomalydetection – preprocessor designed to log and analyse network traffic information. We also collect network traffic information from a few local area networks and made a few simply traffic statistical an...
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Snort® is an open source intrusion detection system based on signature detection. In the paper we present information about the third version of Snort AD – preprocessor designed to log and analyze network traffic information developed by us.
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... Standard intrusion detection systems made by network equipment developers such as Sophos (Sophos Labs), Fortinet (Fortiguard Labs), and others, store intelligent system parameters in private databases that are accessible only for prepaid users of products. Some open-source IPS providers like "Snort" store well-known anomalies detection preprocessing algorithm structures in public repositories (Szmit et al., 2007). ...
Traffic analysis is a common question for most of the production systems in various segments of computer networks. Attacks, configuration mistakes, and other factors can cause network increased accessibility and as a result danger for data privacy. Analyzing network flow and their single packets can be helpful for anomalies detection. Well-known network equipment has predeveloped network flow monitoring software. “NetFlow” data collector software “Nfsen” is an open-source way to collect information from agents. Also “Nfsen” is designed for data sorting and dataset for instruction detection system preparation. Prepared data can be split into fragments for artificial intelligent learning and testing. As AI unit can be used multilayer perceptron developed in a python programming language. This paper focused on real-world traffic dataset collection and multilayer perceptron deployment for TCP flood traffic detection.
... Moreover, sFlow-RT based on traffic measurements can be modified by other tools (e.g., NetFlow [52]) to classify the specific flows. Our current system does not solve all problems in SDN, but RAD can expand with other tools or methods [53][54][55] to support new features (e.g., detection against ...
... Moreover, sFlow-RT based on traffic measurements can be modified by other tools (e.g., NetFlow [52]) to classify the specific flows. Our current system does not solve all problems in SDN, but RAD can expand with other tools or methods [53][54][55] to support new features (e.g., detection against unknown anomalies). Provisioning interfaces to other tools for IDS and flow classification in RAD is a point for future study. ...
The main advantage of software defined networking (SDN) is that it allows intelligent control and management of networking though programmability in real time. It enables efficient utilization of network resources through traffic engineering, and offers potential attack defense methods when abnormalities arise. However, previous studies have only identified individual solutions for respective problems, instead of finding a more global solution in real time that is capable of addressing multiple situations in network status. To cover diverse network conditions, this paper presents a comprehensive reactive system for simultaneously monitoring failures, anomalies, and attacks for high availability and reliability. We design three main modules in the SDN controller for a robust and agile defense (RAD) system against network anomalies: a traffic analyzer, a traffic engineer, and a rule manager. RAD provides reactive flow rule generation to control traffic while detecting network failures, anomalies, high traffic volume (elephant flows), and attacks. The traffic analyzer identifies elephant flows, traffic anomalies, and attacks based on attack signatures and network monitoring. The traffic engineer module measures network utilization and delay in order to determine the best path for multi-dimensional routing and load balancing under any circumstances. Finally, the rule manager generates and installs a flow rule for the selected best path to control traffic. We implement the proposed RAD system based on Floodlight, an open source project for the SDN controller. We evaluate our system using simulation with and without the aforementioned RAD modules. Experimental results show that our approach is both practical and feasible, and can successfully augment an existing SDN controller in terms of agility, robustness, and efficiency, even in the face of link failures, attacks, and elephant flows.
... Tools included in the Anomaly Detection 3.0 allows analysis of movement, its forecasting with help of its advanced statistical algorithms, evaluation of created forecasts, real-time monitoring and verifying that the individual volumes of network traffic parameters do not exceed the forecasted value and in case of exceeding the norms to generate the appropriate messages for the administrator who should check each alarm for potential threats. Current (3.0) version (see e.g. [5] [6] ) of Anomaly Detection provides monitoring of following network traffic parameters: total number of TCP, UDP, and ICMP packets,number of outgoing TCP, UDP, and ICMP packets, number of incoming TCP, UDP, and ICMP packets, number of TCP, UDP, and ICMP packets from current subnet, number of TCP packets with SYN/ACK flags, number of outgoing and incoming WWW packets – TCP on port 80, number of outgoing and incoming DNS packets – UDP outgoing on port 53, number of ARP-request and ARP-reply packets, number of non TCP/IP stacks packets, total number of packets, TCP, WWW, UDP, and DNS upload and download speed [kBps]. Whole Anomaly Detection application consists of three parts: Snorts preprocessor, Profile Generator and Profile Evaluator. ...
This paper presents information about Anomaly-Detection - a Snort-based network traffic monitoring tool. The article concerns use of based on Holt-Winters forecasting method in real-time behavioral analysis of network traffic.
... Tools included in the Anomaly Detection 3.0 allows analysis of movement, its forecasting with help of its advanced statistical algorithms, evaluation of created forecasts, real-time monitoring and verifying that the individual volumes of network traffic parameters do not exceed the forecasted value and in case of exceeding the norms to generate the appropriate messages for the administrator who should check each alarm for potential threats. Current (3.0) version (see e.g. [5], [6]) of AnomalyDetection provides monitoring of following network traffic parameters: total number of TCP, UDP, and ICMP packets, number of outgoing TCP, UDP, and ICMP packets, number of incoming TCP, UDP, and ICMP packets, number of TCP, UDP, and ICMP packets from current subnet, number of TCP packets with SYN/ACK flags, number of outgoing and incoming WWW packets – TCP on port 80, number of outgoing and incoming DNS packets – UDP outgoing on port 53, number of ARP-request and ARP-reply packets, number of non TCP/IP stacks packets, total number of packets, TCP, WWW, UDP, and DNS upload and download speed [kBps]. Whole Anomaly Detection application consists of three parts: Snorts preprocessor, Profile Generator and Profile Evaluator. ...
This paper presents results of analysis of few kinds of network traffic using Holt-Winters methods and Multilayer Perceptron. It also presents Anomaly Detection – a Snort-based network traffic monitoring tool which implements a few models of traffic prediction. Povzetek: Predstavljena je metoda za modeliranje in iskanje anomalij v omrežju.
Structured Query Language (SQL) is a common database language. SQL Slammer is so named because it exploits a vulnerability in the database and then reproduces automatically through scanning for other databases to exploit.
Cyber espionage campaigns and cyber attacks make use of data exfiltration on a regular basis causing damages for billions of dollars. Nowadays, they represent one of the primary threats, and they are performed by criminals, companies and states. Normally, data exfiltration uses classic application-layer protocols (e.g. FTP or HTTP) in combination with very basic obfuscation mechanisms. Even though in most cases these techniques are effective enough, this paper describes how instead they can be detected using properly configured IDSs. Moreover, we introduce a novel approach named polymorphic blending exfiltration that serves to avoid detection from signature-based as well as anomaly-based IDSs. This technique permits to blend the exfiltrated data in the normal and legitimate traffic. We show how IDSs can be easily improved in order to be able to detect such exfiltration. Finally, we conclude presenting different evasion techniques that can be included in the polymorphic blending exfiltration to keep providing a safe undetectable exfiltration.
Serangan atau intrusi yang masuk ke dalam sebuah sistem adalah sesuatu yang hampir pasti terjadi dalam dunia teknologi informasi saat ini. Untuk mengatasi hal tersebut ada beberapa teknologi yang dapat digunakan, seperti firewall atau sistem deteksi intrusi (intrusion detection system/IDS). Tidak seperti firewall yang hanya menyeleksi paket yang masuk berdasarkan alamat IP dan port, IDS bekerja dengan cara memantau isi paket yang masuk ke dalam sebuah komputer untuk kemudian memutuskan apakah rangkaian paket yang masuk tersebut merupakan sebuah serangan atau tidak. Salah satu aplikasi open sources dari IDS adalah Snort yang menggunakan pencocokan string untuk mengambil keputusan. Kelemahan dari IDS yang berbasis pencocokan string adalah kemunculan string dalam sebuah paket harus sama persis, sehingga sulit untuk mendeteksi serangan yang mirip tetapi memiliki pola string yang berbeda. Oleh karena itu paper ini mengusulkan sebuah metode deteksi intrusi menggunakan n-gram dan cosine similarity untuk mencari kemiripan dari serangkaian paket, sehingga yang dicari bukan yang sama persis, tetapi seberapa mirip dengan signature yang ada. Berbeda dengan Snort, paket tidak dicocokkan dengan pola serangan, tetapi dengan pola pengaksesan sebuah halaman web oleh pengguna yang sesungguhnya, sehingga yang memiliki kemiripan tinggi akan dianggap sebagai paket yang sah, sedangkan yang rendah akan dianggap sebagai serangan. Dari hasil ujicoba dengan berbagai variasi nilai ambang batas, maka didapatkan nilai 0.8 dengan n = 3 memberikan akurasi yang terbaik. Sistem deteksi intrusi ini juga mampu mendeteksi berbagai jenis serangan tanpa harus mendefinisikan serangan yang ada sebelumnya, sehingga lebih tahan terhadap zero-day attack.
This paper considers the problem of a choice of algorithms and data structures to achieve the effective processing of events generated by intrusion detection systems. The proposed approach is based on balanced binary trees and speeds up the operations of adding and searching records in the structure. The paper provides the theoretical and experimental confirmation of the efficiency of the developed approach
