The accuracy of current intrusion detection systems (IDSes) is hindered by the limited capability of regular expressions (REs) to express the exact vulnerability. Recent advances have proposed vulnerability-based IDSes that parse traffic and retrieve protocol semantics to describe the vulnerability. Such a description of attacks is analogous to subscriptions that specify events of interest in event processing systems. However, the matching engine of state-of-the-art IDSes lacks efficient matching algorithms that can process many signatures simultaneously. In this work, we place event processing in the core of the IDS and propose novel algorithms to efficiently parse and match vulnerability signatures. Also, we are among the first to detect complex attacks such as the Conficker worm which requires correlating multiple protocol data units (MPDUs) while maintaining a small memory footprint. Our approach incurs neglibile overhead when processing clean traffic, is resilient to attacks, and is faster than existing systems.

The malicious intrusions to computer systems result in the loss of money, time and hidden information which require deployment of intrusion detection systems. Existing intrusion detection methods analyze packet payload to search for certain strings and to match them with a rule database which takes a long time in large size packets. Because of buffer limits, packets may be dropped or the system may stop working due to high CPU load. In this thesis, we investigate signature based intrusion detection with signatures that only depend on the packet header information without payload inspection. To this end, we analyze the well-known DARPA 1998 dataset to manually extract such signatures and construct a new rule set to detect the intrusions. We implement our rule set in a popular intrusion detection software tool, Snort. Furthermore we enhance our rule set with the existing rules of Snort which do not depend on payload inspection. We test our rule set on DARPA data set as well as a new data set that we collect using attack generator tools. Our results show around 30% decrease in detection time with a tolerable decrease in the detection rate. We believe that our method can be used as a complementary component to speed up intrusion detection systems.

<p>With the possibility of connecting several computers and networks the necessity of protecting the whole data and machines from attackers (hackers) that try to get some confident information to use for their own benefit or just destroy or modify valuable information was born. At this point IDS appears to help users, companies or institutions to detect when they are getting compromised. This thesis will cover two main parts: the first one consists of an intense research study about the world of IDS and its environment. Subsequently, we will conclude this part with some points where IDS still needs to be questioned and show up desirable requirements for “the perfect” intrusion detection system. This “perfect” adjective can of course be discussed variously. The second part of the thesis approaches the implementation of the most used open source IDS: Snort. Some basic attacks on the machine where Snort is installed will be performed in order to make the future user see what kind of protection it ensures and the usability of this. There is a brief discussion about two of the main challenges in IDS will follow: analyzing big amounts of packets and encrypted traffic. Finally there are conclusions for a safe computer environment as well as the suggestion that some skilled programmer should give Snort a more friendly interface for every kind of users and a built in programme package which includes webserver, database and other libraries that are needed to run it properly with all its features.</p>

Intrusion Detection Systems (IDS) tools are deployed within networks to monitor data that is transmitted to particular destinations such as MySQL,Oracle databases or log files. The data is normally dumped to these destinations without a forensic standard structure. When digital evidence is needed, forensic specialists are required to analyse a very large volume of data. Even though forensic tools can be utilised, most of this process has to be done manually, consuming time and resources. In this research, we aim to address this issue by combining several existing tools to archive the original IDS data into a new container (Digital Evidence Bag) that has a structure based upon standard forensic processes. The aim is to develop a method to improve the current IDS database function in a forensic manner. This database will be optimised for future, forensic, analysis. Since evidence validity is always an issue, a secondary aim of this research is to develop a new monitoring scheme. This is to provide the necessary evidence to prove that an attacker had surveyed the network prior to the attack. To achieve this, we will set up a network that will be monitored by multiple IDSs. Open source tools will be used to carry input validation attacks into the network including SQL injection. We will design a new tool to obtain the original data in order to store it within the proposed DEB. This tool will collect the data from several databases of the different IDSs. We will assume that the IDS will not have been compromised.

As the bandwidth of present networks gets larger than the past, the demand of Network Intrusion Detection Systems (NIDS) that function in real time becomes the major requirement for high-speed networks. If these systems are not fast enough to process all network traffic passing, some malicious security violations may take role using this drawback. In order to make that kind of applications schedulable, some concurrency mechanism is introduced to the general flowchart of their algorithm. The principal aim is to fully utilize each resource of the platform and overlap the independent parts of the applications. In the sense of this context, a generic multi-threaded infrastructure is designed and proposed. The concurrency metrics of the new system is analyzed and compared with the original ones.

With the possibility of connecting several computers and networks the necessity of protecting the whole data and machines from attackers (hackers) that try to get some confident information to use for their own benefit or just destroy or modify valuable information was born. At this point IDS appears to help users, companies or institutions to detect when they are getting compromised. This thesis will cover two main parts: the first one consists of an intense research study about the world of IDS and its environment. Subsequently, we will conclude this part with some points where IDS still needs to be questioned and show up desirable requirements for “the perfect” intrusion detection system. This “perfect” adjective can of course be discussed variously. The second part of the thesis approaches the implementation of the most used open source IDS: Snort. Some basic attacks on the machine where Snort is installed will be performed in order to make the future user see what kind of protection it ensures and the usability of this. There is a brief discussion about two of the main challenges in IDS will follow: analyzing big amounts of packets and encrypted traffic. Finally there are conclusions for a safe computer environment as well as the suggestion that some skilled programmer should give Snort a more friendly interface for every kind of users and a built in programme package which includes webserver, database and other libraries that are needed to run it properly with all its features.

Context. Cloud computing (CC) is developed as a Human-centered computing model to facilitate its users to access resources anywhere on the globe. The resources can be shared among any cloud user which mainly questions the security in cloud computing. There are Denial of Service and Distributed Denial of Service attacks which are generated by the attackers to challenge the security of CC. The Next-Generation Intrusion Prevention Systems (sometimes referred as Non-Traditional Intrusion Prevention Systems (NGIPS) are being used as a measure to protect users against these attacks. This research is concerned with the NGIPS techniques that are implemented in the cloud computing environment and their evaluation. Objectives. In this study, the main objective is to investigate the existing techniques of the NGIPS that can be deployed in the cloud environment and to provide an empirical comparison of source mode and destination mode in Snort IPS technique based on the metrics used for evaluation of the IPS systems. Methods. In this study, a systematic literature review is used to identify the existing NGIPS techniques. The library databases used to search the literature are Inspec, IEEE Xplore, ACM Digital Library, Wiley, Scopus and Google scholar. The articles are selected based on an inclusion and exclusion criteria. The experiment is selected as a research method for the empirical comparison of Source mode and destination mode of Snort NGIPS found through literature review. The testbed is designed and implemented with the Snort filter techniques deployed in the virtual machine. Results. Snort is one of the mostly used NGIPS against DoS and DDoS attacks in the cloud environment. Some common metrics used for evaluating the NGIPS techniques are CPU load, Memory usage, bandwidth availability, throughput, true positive rate, false positive rate, true negative rate, false negative rate, and accuracy. From the experiment, it was found that Destination mode performs better than source mode in Snort. When compared with the CPU load, Bandwidth, Latency, Memory Utilization and rate of packet loss metrics. Conclusions. It was concluded that many NGIPS of the cloud computing model are related to each other and use similar techniques to prevent the DoS and DDoS attacks. The author also concludes that using of source based and destination based intrusion detection modes in Snort has some difference in the performance measures.

Context. The intrusion detection systems are being widely used for detecting the malicious traffic in many industries and they use a variety of technologies. Each IDs had different architecture and are deployed for detecting malicious activity. Intrusion detection system has a different set of rules which can defined based on requirement. Therefore, choosing intrusion detection system for and the appropriate environment is not an easy task. Objectives. The goal of this research is to evaluate three most used open source intrusion detection systems in terms of performance. And we give details about different types of attacks that can be detected using intrusion detection system. The tools that we select are Snort, Suricata, OSSEC. Methods. The experiment is conducted using TCP, SCAN, ICMP, FTP attack. Each experiment was run in different traffic rates under normal and malicious traffics all rule are active. All these tests are conducted in a virtual environment. Results. We can calculate the performance of IDS by using CPU usage, memory usage, packet loss and a number of alerts generated. These results are calculated for both normal and malicious traffic. Conclusions. We conclude that results vary in different IDS for different traffic rates. Specially snort showed better performance in alerts identification and OSSEC in the performance of IDS. These results indicated that alerts are low when the traffic rates high are which indicates this is due to the packet loss. Overall OSSEC provides better performance. And Snort provides better performance and accuracy for alert detection.

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.

Orientador: Kelton Augusto Pontara da Costa / Resumo: À medida que a Internet cresce com o passar dos anos, é possível observar um aumento na quantidade de dados que trafegam nas redes de computadores do mundo todo. Em um contexto onde o volume de dados encontra-se em constante renovação, sob a perspectiva da área de Segurança de Redes de Computadores torna-se um grande desafio assegurar, em termos de eficácia e eficiência, os sistemas computacionais da atualidade. Dentre os principais mecanismos de segurança empregados nestes ambientes, destacam-se os Sistemas de Detecção de Intrusão em Rede. Muito embora a abordagem de detecção por assinatura seja suficiente no combate de ataques conhecidos nessas ferramentas, com a eventual descoberta de novas vulnerabilidades, faz-se necessário a utilização de abordagens de detecção por anomalia para amenizar o dano de ataques desconhecidos. No campo acadêmico, diversos trabalhos têm explorado o desenvolvimento de abordagens híbridas com o intuito de melhorar a acurácia dessas ferramentas, com o auxílio de técnicas de Aprendizado de Máquina. Nesta mesma linha de pesquisa, o presente trabalho propõe a aplicação destas técnicas para a detecção de intrusão em um ambiente tempo real mediante uma ferramenta popular e amplamente utilizada, o Snort. Os resultados obtidos mostram que em determinados cenários de ataque, a abordagem de detecção baseada em anomalia pode se sobressair em relação à abordagem de detecção baseada em assinatura, com destaque às técnicas AdaBoost, Florestas Aleatórias, Árvor... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: As the Internet grows over the years, it is possible to observe an increase in the amount of data that travels on computer networks around the world. In a context where data volume is constantly being renewed, from the perspective of the Network Security area it becomes a great challenge to ensure, in terms of effectiveness and efficiency, today’s computer systems. Among the main security mechanisms employed in these environments, stand out the Network Intrusion Detection Systems. Although the signature-based detection approach is sufficient to combat known attacks in these tools, with the eventual discovery of new vulnerabilities, it is necessary to use anomaly-based detection approaches to mitigate the damage of unknown attacks. In the academic field, several works have explored the development of hybrid approaches in order to improve the accuracy of these tools, with the aid of Machine Learning techniques. In this same line of research, the present work proposes the application of these techniques for intrusion detection in a real time environment using a popular and widely used tool, the Snort. The obtained results shows that in certain attack scenarios, the anomaly-based detection approach may outperform the signature-based detection approach, with emphasis on the techniques AdaBoost, Random Forests, Decision Tree and Linear Support Vector Machine. / Mestre