Academic literature on the topic 'Detekce síťového provozu'

This semester project presents an analysis of network traffic and detection of anomalies in network traffic by several various means. In the first part of the paper there is an explanation of the methods aiming at denial of service. Then in the second part an implementation of protection by means of selected solutions is presented. The intent is to compare these means which are supposed to detect cyber attacks aiming at denial of service. Another intent is to choose the best solutions from the categories of open-source and commercial solutions. The target of the master thesis was to work out a comparison between actual solutions for detection of DoS and DDoS attacks.

The semestral thesis deals with the analysis of the modern open-source NIDPS tools for monitoring and analyzing the network traffic. The work rates these instruments in terms of their network location and functions. Also refers about more detailed analysis of detecting and alerting mechanisms. Further analyzes the possibilities of detection of anomalies, especially in terms of statistical analysis and shows the basics of other approaches, such as approaches based on data mining and machine learning. The last section presents specific open-source tools, deals with comparison of their activities and the proposal allowing monitoring and traffic analysis, classification, detection of anomalies and (D)DoS attacks.

This thesis deals with the principles of network traffic obfuscation, in order to avoid its detection by the Intrusion Detection System installed in the network. At the beginning of the work, reader is familiarized with the fundamental principle of the basic types of IDS and introduced into the matter of obfuscation techniques, that serve as stepping stone in order to create our own library, whose design is described in the last part of the work. The outcome of the work is represented by a library, that provides all the implemented techniques for further use. The library can be well utilized in penetration testing of the new systems or used by the attacker.

This thesis focuses on description of the current state of research in the detection of network attacks and subsequently on the improvement of detection capabilities of specific attacks by establishing a formal definition of network metrics. These metrics approximate the progress of network connection and create a signature, based on behavioral characteristics of the analyzed connection. The aim of this work is not the prevention of ongoing attacks, or the response to these attacks. The emphasis is on the analysis of connections to maximize information obtained and definition of the basis of detection system that can minimize the size of data collected from the network, leaving the most important information for subsequent analysis. The main goal of this work is to create the concept of the detection system by using defined metrics for reduction of the network traffic to signatures with an emphasis on the behavioral aspects of the communication. Another goal is to increase the autonomy of the detection system by developing an expert knowledge of honeypot system, with the condition of independence to the technological aspects of analyzed data (e.g. encryption, protocols used, technology and environment). Defining the concept of honeypot system's expert knowledge in the role of the teacher of classification algorithms creates autonomy of the~system for the detection of unknown attacks. This concept also provides the possibility of independent learning (with no human intervention) based on the knowledge collected from attacks on these systems. The thesis describes the process of creating laboratory environment and experiments with the defined network connection signature using collected data and downloaded test database. The results are compared with the state of the art of the network detection systems and the benefits of the proposed approximation methods are highlighted.

This diploma thesis is focused on the detection and protection against Slow DoS and DDoS attacks using computer network traffic analysis. The reader is introduced to the basic issues of this specific category of sophisticated attacks, and the characteristics of several specific attacks are clarified. There is also a set of methods for detecting and protecting against these attacks. The proposed methods are used to implement custom intrusion prevention system that is deployed on the border filtering server of computer network in order to protect Web servers against attacks from the Internet. Then created system is tested in the laboratory network. Presented results of the testing show that the system is able to detect attacks Slow GET, Slow POST, Slow Read and Apache Range Header and then protect Web servers from affecting provided services.

The thesis investigates APT attacks, which are professional targeted attacks that are characterised by long-term duration and use of advanced techniques. The thesis summarises current knowledge about APT attacks and suggests seven symptoms that can be used to check, whether an organization is under an APT attack. Thesis suggests a system for detection of APT attacks based on interaction of those symptoms. This system is elaborated further for detection of attacks in computer networks, where it uses user behaviour modelling for anomaly detection. The detector uses k-nearest neighbors (k-NN) method. The APT attack recognition ability in network environment is verified by implementing and testing this detector.

Tato práce se zabývá problematikou anomální detekce síťových útoků s využitím technik strojového učení. Nejdříve jsou prezentovány state-of-the-art datové kolekce určené pro ověření funkčnosti systémů detekce útoků a také práce, které používají statistickou analýzu a techniky strojového učení pro nalezení síťových útoků. V další části práce je prezentován návrh vlastní kolekce metrik nazývaných Advanced Security Network Metrics (ASNM), který je součástí konceptuálního automatického systému pro detekci průniků (AIPS). Dále jsou navrženy a diskutovány dva různé přístupy k obfuskaci - tunelování a modifikace síťových charakteristik - sloužících pro úpravu provádění útoků. Experimenty ukazují, že použité obfuskace jsou schopny předejít odhalení útoků pomocí klasifikátoru využívajícího metriky ASNM. Na druhé straně zahrnutí těchto obfuskací do trénovacího procesu klasifikátoru může zlepšit jeho detekční schopnosti. Práce také prezentuje alternativní pohled na obfuskační techniky modifikující síťové charakteristiky a demonstruje jejich použití jako aproximaci síťového normalizéru založenou na vhodných trénovacích datech.

This work studies systems and methods for anomaly detection in computer networks. At first, basic categories of network security systems and number of methods used for anomaly detection are briefly described. The core of the work is an optimization of the method based on detection of changes in distributions of packet features originally proposed by Lakhina et al. This method is described in detail and two optimizations of it are proposed -- first is focused to speed and memory efficiency, second improves its detection capabilities. Next, a software created to test these optimizations is briefly described and results of experiments on real data with artificially generated and also real anomalies are presented.

This paper deals with a problem of detection of network traffic anomaly and classification of network flows. Based on existing methods, paper describes proposal and implementaion of a tool, which can automatically classify network flows. The tool uses CUDA platform for network data processing and computation of network flow metrics using graphics processing unit. Processed flows are subsequently classified by proposed methods for network anomaly detection.

Publication aims to propose and apply new metrics for intrusion detection in network traffic according to analysis of existing metrics, analysis of network traffic and behavioral characteristics of known attacks. The main goal of the thesis is to propose and implement new collection of metrics which will be capable to detect zero day attacks.