Auswahl der wissenschaftlichen Literatur zum Thema „Detekce anomálie“

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.

This thesis serves as a theoretical basis for a practical solution to the issue of the use of statistical methods for detecting anomalies in data traffic. The basic focus of anomaly detection data traffic is on the data attacks. Therefore, the main focus is the analysis of data attacks. Within the solving are data attacks sorted by protocols that attackers exploit for their own activities. Each section describes the protocol itself, its usage and behavior. For each protocol is gradually solved description of the attacks, including the methodology leading to the attack and penalties on an already compromised system or station. For the most serious attacks are outlined procedures for the detection and the potential defenses against them. These findings are summarized in the theoretical analysis, which should serve as a starting point for the practical part, which will be the analysis of real data traffic. The practical part is divided into several sections. The first of these describes the procedures for obtaining and preparing the samples to allow them to carry out further analysis. Further described herein are created scripts that are used for obtaining needed data from the recorded samples. These data are were analyzed in detail, using statistical methods such as time series and descriptive statistics. Subsequently acquired properties and monitored behavior is verified using artificial and real attacks, which is the original clean operation modified. Using a new analysis of the modified traffics compared with the original samples and an evaluation of whether it has been some kind of anomaly detected. The results and tracking are collectively summarized and evaluated in a separate chapter with a description of possible further attacks, which were not directly part of the test analysis.

This thesis describes the design and implementation of system for effective detection of network anomaly using DNS data. Effective detection is accomplished by combination and cooperation of detectors and detection techniques. Flow data in NetFlow and IPFIX formats are used as input for detection. Also packets in pcap format can be used. Main focus is put on detection of DNS tunneling. Thesis also describes Domain Name System (DNS) and anomalies associated with DNS.

The diploma thesis is focuses on technologies and security of the wireless networks in standard IEEE 802.11, describes the most used standards, definition of physical layer, MAC layer and specific technologies for wireless networks. The diploma thesis is focused on description of selected security protocols, their technologies as well as weaknesses. Also, in the thesis, there are described security threats and vectors of attacks towards wireless networks 802.11. Selected threats were simulated in established experimental network, for these threats were designed detection methods. For testing and implementing designed detection methods, IDS system Zeek is used together with network scripts written in programming language Python. In the end there were trained and tested models of machine learning both supervised and unsupervised machine learning.

There have been lots of new drone applications in recent years. Drones are also often used in the field of national security forces. The aim of this work is to design and implement a tool intended for crowd behavior analysis in drone videodata. This tool ensures identification of suspicious behavior of persons and facilitates its localization. The main benefits include the design of a suitable video stabilization algorithm to stabilize small jitters, as well as trace back of the lost scene. Furthermore, two anomaly detectors were proposed, differing in the method of feature vector extraction and background modeling. Compared to the state of the art approaches, they achieved comparable results, but at the same time they brought the possibility of online data processing.

Tato diplomová práce se zabývá detekcí škodlivých domén za pomoci analýzy pasivního DNS provozu, návrhem a implementací vlastního systému detekce. Provoz DNS se stává terčem mnoha útočníků, kteří využívají toho, že služba DNS je nezbytná pro fungování Internetu. Téměř každá internetová komunikace totiž začíná DNS dotazem a odpovědí. Zneužívání služby DNS nebo využívání slabin této služby se projevuje anomálním chováním DNS provozu. Tato práce obsahuje popis různých metod používaných pro odhalování anomálií a škodlivých domén v DNS datech. Hlavní částí práce je návrh a implementace systému pro detekci škodlivých domén. Implementovaný systém byl testován na DNS datech získaných z reálného provozu.

This master thesis deals with detecting anomalies methods in network traffic. First of all this thesis analyzes the basic concepts of anomaly detection and already using technology. Next, there are also described in more detail three methods for anomalies search and some types of anomalies. In the second part of this thesis there is described implementation of all three methods and there are presented the results of experimentation using real data.

Due to higher requirements of computational power and safety, or functional safety ofequipments intended for the use in the industrial domain, embedded systems containing areal-time operating system are still the active area of research. This thesis addresses thehardware-assisted control module that is based on the runtime model-based verificationof a target application. This subsystem is intended to increase the diagnostic coverage,particularly, the detection of the execution errors. After the specification of the architecture,the formal model is defined and implemented into hardware using FPGA technology.This thesis also discuss some other aspects and embodies new approaches in the area ofembedded flow control, e.g. the integration of the design patterns. Using the simulation,the created module was tested using the created scenarios, which follow the real programexecution record. The results suggest that the error detection time is lower than usingstandard techniques, such a watchdog.

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.

The goal of the thesis was an analysis of IoT communication protocols, their vulnerabilities and the creation of a suitable anomaly detector. It must be possible to run the detector on routers with the OpenWRT system. To create the final solution, it was necessary to analyze the communication protocols BLE and Z-Wave with a focus on their security and vulnerabilities. Furthermore, it was necessary to analyze the possibilities of anomaly detection, design and implement the detection system. The result is a modular detection system based on the NEMEA framework. The detection system is able to detect re-pairing of BLE devices representing a potential pairing attack. The system allows interception of Z-Wave communication using SDR, detection of Z-Wave network scanning and several attacks on network routing. The system extends the existing detector over IoT statistical data with more detailed statistics with a broader view of the network. The original solution had only Z-Wave statistics with a limited view of the network obtained from the Z-Wave controller. The modular solution of the system provides deployment flexibility and easy system scalability. The functionality of the solution was verified by experiments and a set of automated tests. The system was also successfully tested on a router with OpenWRT and in the real world enviroment. The results of the thesis were used within the SIoT project.