This diploma thesis deals with the topic of proxy servers and firewalls and considers other associated technologies and network techniques. It systematically describes the general issues of firewalls, with a special focus on proxy firewalls and their safety. Additional systems mentioned in this document are intrusion detection systems (IDS), antivirus systems and content control filters – as these are also connected with safety of networks, servers and workstations or with limiting various Internet sources. IDS systems can be typically supplemented with various additional applications or tools that enrich them and increase their potential – including graphic additions. This part is remembered too. Some systems can communicate with each other, which is successfully utilised (FW & IDS co-operation, for example). The purpose of the first large chapter is to present firewall technologies, to list firewall types, their basic functionality and to present the final comparison. It marginally mentions firewall applications in practice. Chapter two explains the theory of network address translation (NAT), deals with its functionality, safety and with limiting the NAT mechanism. Chapter three brings a comprehensive presentation of proxy servers. It explains their principle from the point of view of functionality and the specification of application areas. The chapter is complete with a clear list of proxy server types and their descriptions. The last chapter named Linux Proxy Firewall is the key part of the work. It deals generally with the Linux platform, the Debian GNU/Linux distribution, principles of safety policy, network configuration, network server safety, Linux firewalls (Netfilter framework, Iptables tool) and with the Squid proxy server. The following subchapters respect the previous structure: they describe the theories of intrusion detection systems, antivirus checks and content filtering based on different methods. All this is presented similarly to the previous chapters. A proxy firewall solution built on the Linux operating system has been proposed in the practical part. The Debian GNU/Linux distribution has been chosen, being very suitable for server use due to its features. This environment is also used for additional safety software contained in the proxy firewall: antivirus protection, content filtering and an intrusion detection system. The priority is the most comprehensive computer network security, which requires detection abilities with the broadest possible coverage in the area of network safety. The purpose of this diploma thesis is not only to describe the principle of operation of proxy servers and to compare them with other types and other systems, but it also brings my own proposed free solution, which increases network safety and has the ambition of comparing it with clearly commercial products available on the market.

This project compare several well-known PHP frameworks and their main characteristics. Creating the content management system with using sophisticated administration interface and access rights. Realization module for ordering and managing smart card for public transport company. Using modern payment methods using 3D secure. Dynamically change the price lists, zones and types of cards. It is created checking neighbor zones that can not be chosen meaningless combinations zones.

This master's thesis is aimed how can be network traffic monitored using IP flows. The description of NEMEA framework that can be used to build complex intrusion detection system. Following chapters describes port scanning methods and SSH protocol which can be used for remote login to the system, which can be exploited by an attacker. These two areas are intended to be detected in a slow attack manner, when attacker using low attack speed, which he can evade multiple detection methods. Proposed method for detection such attacks is using information from the last few connections. Finally, proposed detection method results are further described.

We investigate deep learning based omni intrusion detection system (IDS) for supervisory control and data acquisition (SCADA) networks that are capable of detecting both temporally uncorrelated and correlated attacks. Regarding the IDSs developed in this paper, a feedforward neural network (FNN) can detect temporally uncorrelated attacks at an F1 of 99.967±0.005% but correlated attacks as low as 58±2%. In contrast, long-short term memory (LSTM) detects correlated attacks at 99.56±0.01% while uncorrelated attacks at 99.3±0.1%. Combining LSTM and FNN through an ensemble approach further improves the IDS performance with F1 of 99.68±0.04% regardless the temporal correlations among the data packets. / Graduate

The evolution in networking coupled with an increasing demand to improve user experience has led to different proposals to extend the standard TCP. Multipath TCP (MPTCP) is one such extension that has the potential to overcome few inherent limitations in the standard TCP. While MPTCP's design and deployment progresses, most of the focus has been on its compatibility. The security aspect is confined to making sure that the MPTCP protocol itself offers the same security level as the standard TCP. The topic of this thesis is to investigate the unexpected security implications raised by using MPTCP in the traditional networking environment. The Internet of today has security middle-boxes that perform traffic analysis to detect intrusions and attacks. Such middle-boxes make use of different assumptions about the traffic, e.g., traffic from a single connection always arrives along the same path. This along with many other assumptions may not be true anymore with the advent of MPTCP as traffic can be fragmented and sent over multiple paths simultaneously. We investigate how practical it is to evade a security middle-box by fragmenting and sending traffic across multiple paths using MPTCP. Realistic attack traffic is used to evaluate such attacks against Snort IDS to show that these attacks are feasible. We then go on to propose possible solutions to detect such attacks and implement them in an MPTCP proxy. The proxy aims to extend the MPTCP performance advantages to servers that only support standard TCP, while ensuring that intrusions can be detected as before. Finally, we investigate the potential MPTCP scenario where security middle-boxes only have access to some of the traffic. We propose and implement an algorithm to perform intrusion detection in such situations and achieve a nearly 90% detection accuracy. Another contribution of this work is a tool, that converts IDS rules into equivalent attack traffic to automate the evaluation of a middle-box. / Multipath TCP (MPTCP) is an extension to standard TCP that is close to being standardized. The design of the protocol is progressing, but most of the focus has so far been on its compatibility. The security aspect is confined to making sure that the MPTCP protocol itself offers the same security level as standard TCP. The topic of this thesis is to investigate the unexpected security implications raised by using MPTCP in a traditional networking environment. Today, the security middleboxes make use of different assumptions that may not be true anymore with the advent of MPTCP.We investigate how practical it is to evade a security middlebox by fragmenting and sending traffic across multiple paths using MPTCP. Realistic attack traffic generated from a tool that is also presented in this thesis is used to show that these attacks are feasible. We then go on to propose possible solutions to detect such attacks and implement them in an MPTCP proxy. The proxy aims to extend secure MPTCP performance advantages. We also investigate the MPTCP scenario where security middleboxes can only observe some of the traffic. We propose and implement an algorithm to perform intrusion detection in such situations and achieve a high detection accuracy. / HITS

Network attacks remain a constant threat to organizations around the globe. Intrusion detection systems provide a vital piece of the protection needed in order to fend off these attacks. Machine learning has become a popular method for developing new anomaly-based intrusion detection systems, and in recent years, deep learning has followed suit. Additionally, data fusion is often applied to intrusion detection systems in research, most often in the form of feature reduction, which can improve the accuracy and training times of classifiers. Another less common form of data fusion is decision fusion, where the outputs of multipe classifiers are fused into a more reliable result. Recent research has produced some contradictory results regarding the efficiency of traditional machine learning algorithms compared to deep learning algorithms. This study aims to investigate this problemand provide some clarity about the relative performance of a selection of classifier algorithms, namely artificial neural network, long short-term memory and random forest. Furthermore, two feature selection methods, namely correlation coefficient method and principal component analysis, as well as one decision fusion method in D-S evidence theory are tested. The majority of the feature selection methods fail to increase the accuracy of the implemented models, although the accuracy is not drastically reduced. Among the individual classifiers, random forest shows the best performance, obtaining an accuracy of 87,87%. Fusing the results with D-S evidence theory further improves this result, obtaining an accuracy of 88,56%, and proves particularly useful for reducing the number of false positives.

In recent years, a new generation of architecture has emerged in the world of computer networks, known as software-defined networking (SDN), that aims to improve and remove the limitations of traditional networks. Although SDN provides viable benefits, it has faced many security threats and vulnerability-related issues. To solve security issues in the SDN, one of the most vital solutions is employing an intrusion detection system (IDS). Merging IDS into the SDN network remains efficient due to the unique features of SDN, such as high manageability, flexibility, and programmability. In this paper, we propose a new approach as a kangaroo-based intrusion detection system (KIDS), which is an SDN-based architecture for attack detection and malicious behaviors in the data plane. Designing a zone-based architecture in the KIDS assists us in achieving a distributed architecture which is scalable in both area and anomaly detection. In the KIDS architecture, the IDS module supplies the flow-based and packet-based intrusion detection components based on monitoring packet parser and Flow tables of the SDN switches. In the proposed approach, the IDS uses consecutive jumps like a kangaroo for announcing the attacks both to the SDN controller and other IDSs, contributing to improved scalability and efficiency. The evaluation of the proposed approach shows an enhanced performance against that of peer approaches in detecting malicious packets.

Denna literaturöversikt går igenom två olika system inom IT-säkerhet och hur de fungerar tillsammans med maskinlärningstekniker till en relativt ytlig nivå.Syftet med denna rapport är att kunna sammanfatta dessa system och se hur de kan hjälpa med en systemadministratörs uppgifter, hur det kan användas för automatisera och vad för positiva och negativa förändringar det kan ha på en infrastruktur.Maskinlärning kan vara ett kraftigt verktyg för systemadministratörer för att lätta på arbetsmängden som kan förekomma inom en organisation, vilket är också varför det är viktigt att diskutera när och var man ska utplacera en lösning. Den här studien ska diskutera användningen av maskinlärning och när och var det kan användas. / This literature review discusses two different systems within IT-security and how they work within machine learning to a relatively surface-level degree.The purpose of this paper is to be able to summarize these systems and see how they can help a system administrator’s assignments. how it can be used for automation and the positives and negatives.Machine learning can be a powerful tool for system administrators to alleviate the workload which can exist within an organization, which is why it is important to discuss when and where to deploy a solution.

Industrial Control Systems (ICS) play a critical part in our world’s economy, supply chain and critical infrastructure. Securing the various types of ICS is of the utmost importance and has been a focus of much research for the last several years. At the heart of many defense in depth strategies is the signature based intrusion detection system (IDS). The signatures that define an IDS determine the effectiveness of the system. Existing methods for IDS signature creation do not leverage the information contained within the PLC ladder logic file. The ladder logic file is a rich source of information about the PLC control system. This thesis describes a method for parsing PLC ladder logic to extract address register information, data types and usage that can be used to better define the normal operation of the control system which will allow for rules to be created to detect abnormal activity.

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