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Eluding and Evasion of IDS Systems
Černý, Marek ; Tobola, Jiří (referee) ; Žádník, Martin (advisor)
This paper analyzes network security devices called intrusion detection (ID) systems. In order to point out possible flaws, especially ID systems using signature analysis are examined. Based on this, methods to exploit possible vulnerabilities of these systems were designed. These methods were implemented into a simple program for ID systems efficiency evaluation. It can be used in a way entirely independent of particular network attack used in the test.
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Implementation Methodology of Network Security in the Software Company
Tomaga, Jakub ; Sopuch, Zbyněk (referee) ; Sedlák, Petr (advisor)
This thesis deals with network security and its deployment in the real environment of the software company. The thesis describes information management framework with a specific concentration on computer networks. Network security policy is designed as well as network infrastructure modifications in order to increase the level of security. All parts of the solution are also analyzed from financial point of view.
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Detection of Malicious Domain Names
Setinský, Jiří ; Perešíni, Martin (referee) ; Tisovčík, Peter (advisor)
The bachelor thesis deals with the detection of artificially generated domain names (DGA). The generated addresses serve as a means of communication between the attacker and the infected computer. By detection, we can detect and track infected computers on the network. The detection itself is preceded by the study of machine learning techniques, which will then be applied in the creation of the detector. To create the final classifier in the form of a decision tree, it was necessary to analyze the principle of DGA addresses. Based on their characteristics, the attributes were extracted, according to which the final classifier will be decided. After learning the classification model on the training set, the classifier was implemented in the target platform NEMEA as a detection module. After final optimizations and testing, we achieved a accuracy of the classifier of 99%, which is a very positive result. The NEMEA module is ready for real-world deployment to detect security incidents. In addition to the NEMEA module, another model was created to predict the accuracy of datasets with domain names. The model is trained based on the characteristics of the dataset and the accuracy of the DGA detector, whose behavior we want to predict.
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Reputation of Malicious Traffic Sources
Bartoš, Václav ; Lhotka,, Ladislav (referee) ; Vozňák, Miroslav (referee) ; Kořenek, Jan (advisor)
An important part of maintaining network security is collecting and processing information about cyber threats, both from network operator's own detection tools and from third parties. A commonly used type of such information are lists of network entities (IP addresses, domains, URLs, etc.) which were identified as malicious. However, in many cases, the simple binary distinction between malicious and non-malicious entities is not sufficient. It is beneficial to keep other supplementary information for each entity, which describes its malicious activities, and also a summarizing score, which evaluates its reputation numerically. Such a score allows for quick comprehension of the level of threat the entity poses and allows to compare and sort entities. The goal of this work is to design a method for such summarization. The resulting score, called Future Maliciousness Probability (FMP score), is a value between 0 and 1, assigned to each suspicious network entity, expressing the probability that the entity will do some kind of malicious activity in a near future. Therefore, the scoring is based of prediction of future attacks. Advanced machine learning methods are used to perform the prediction. Their input is formed by previously received alerts about security events and other relevant data related to the entity. The method of computing the score is first described in a general way, usable for any kind of entity and input data. Then a more concrete version is presented for scoring IPv4 address by utilizing alerts from an alert sharing system and supplementary data from a reputation database. This variant is then evaluated on a real world dataset. In order to get enough amount and quality of data for this dataset, a part of the work is also dedicated to the area of security analysis of network data. A framework for analysis of flow data, NEMEA, and several new detection methods are designed and implemented. An open reputation database, NERD, is also implemented and described in this work. Data from these systems are then used to evaluate precision of the predictor as well as to evaluate selected use cases of the scoring method.
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Network Anomaly Detection
Bartoš, Václav ; Kořenek, Jan (referee) ; Žádník, Martin (advisor)
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.
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Case Study of Selected Network Vulnerabilities
Kolajová, Jana ; Kačic, Matej (referee) ; Homoliak, Ivan (advisor)
The main goal of this thesis is to deal with databases of vulnerable code bases and vulnerable applications, and to implement a tool for autonomous search and saving data from those databases to a local one. The thesis is divided into theoretical and practical parts. The theoretical part deals with my current knowledge of the main topic and creates a foundation for the implementation. Various kinds of vulnerabilities and network attacks are described in detail in this part. The practical part describes implementation of the tool and its real use.
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Active security equipment in computer networks
Škrdla, Vojtěch ; Martinásek, Zdeněk (referee) ; Malina, Lukáš (advisor)
The main object of this bachelor's thesis is to describe problematics of active security devices. The thesis is mainly focused on firewalls. Thesis is divided into several topics, in which we describe types of security devices, division of firewalls, description of their advantages and disadvantages, types of VPN. The practical part describes the security design of mid-sized computer network, description of software that was used to test this network and the last part focuses on designing laboratory tasks with firewalls Cisco ASA5520 and Checkpoint Gaia R77.
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