The rampant growth of the Internet has been coupled with an equivalent growth in cyber crime over the Internet. With our increased reliance on the Internet for commerce, social networking, information acquisition, and information exchange, intruders have found financial, political, and military motives for their actions. Network Intrusion Detection Systems (NIDSs) intercept the traffic at an organization’s periphery and try to detect intrusion attempts. Signature-based NIDSs compare the packet to a signature database consisting of known attacks and malicious packet fingerprints. The signatures use regular expressions to model these intrusion activities. This thesis presents a memory efficient pattern matching system for the class of regular expressions appearing frequently in the NIDS signatures. Proposed Cascaded Automata Architecture is based on two stage automata. The first stage recognizes the sub-strings and character classes present in the regular expression. The second stage consumes symbol generated by the first stage upon receiving input traffic symbols. The basic idea is to utilize the research done on string matching problem for regular expression pattern matching. We formally model the class of regular expressions mostly found in NIDS signatures. The challenges involved in using string matching algorithms for regular expression matching has been presented. We introduce length-bound transitions, counter-based states, and associated counter arrays in the second stage automata to address these challenges. The system uses length information along with counter arrays to keep track of overlapped sub-strings and character class based transition. We present efficient implementation techniques for counter arrays. The evaluation of the architecture on practical expressions from Snort rule set showed compression in number of states between 50% to 85%. Because of its smaller memory footprint, our solution is suitable for both software based implementations on network chips as well as FPGA based designs.

Network-based attacks against computer systems are a common and increasing problem. Attackers continue to increase the sophistication and complexity of their attacks with the goal of removing sensitive data or disrupting operations. Attack detection technology works very well for the detection of known attacks using a signature-based intrusion detection system. However, attackers can utilize attacks that are undetectable to those signature-based systems whether they are truly new attacks or modified versions of known attacks. Anomaly-based intrusion detection systems approach the problem of attack detection by detecting when traffic differs from a learned baseline. In the case of this research, the focus was on a relatively new area known as payload anomaly detection. In payload anomaly detection, the system focuses exclusively on the payload of packets and learns the normal contents of those payloads. When a payload's contents differ from the norm, an anomaly is detected and may be a potential attack. A risk with anomaly-based detection mechanisms is they suffer from high false positive rates which reduce their effectiveness. This research built upon previous research in payload anomaly detection by combining multiple techniques of detection in a layered approach. The layers of the system included a high-level navigation layer, a request payload analysis layer, and a request-response analysis layer. The system was tested using the test data provided by some earlier payload anomaly detection systems as well as new data sets. The results of the experiments showed that by combining these layers of detection into a single system, there were higher detection rates and lower false positive rates.

O objetivo principal do presente trabalho é apresentar uma proposta que permita a combinação entre uma solução de captura de pacotes já existente e não muito flexível (sniffer) e o conceito de agentes móveis para aplicação em redes segmentadas. Essa pesquisa possui como foco principal a aplicação da técnica captura de pacotes em SDIs network based, utilizando para isso o modelo desenvolvido no ICMC (Cansian, 1997) e posteriormente adequado ao ambiente de agentes móveis (Bernardes, 1999). Assim sendo, foi especificada a camada base do ambiente desenvolvido em (Bernardes, 1999) visando as interações entre seus agentes e o agente de captura de pacotes. / The main objective of the current work is to present a proposal that allows the combination between an existent and not so flexible packet capture solution (sniffer) and the concept of mobile agents for application in switched networks. This research focuses the application of the packet capture technique in IDSs network-based, using for this purpose the model developed at ICMC (Cansian, 1997) and later adjusted to the mobile agents environment (Bernardes, 1999). Therefore, the base layer of the developed environment (Bernardes, 1999} was specified focusing the interactions between its agents and the packet capture agent.

Em decorrência do aumento do número de ataques de origem interna, a utilização de mecanismos de proteção, como o firewall, deve ser ampliada. Visto que este tipo de ataque, ocasionado pelos usuários internos ao sistema, não permite a localização imediata, torna-se necessário o uso integrado de diversas tecnologias para aumentar a capacidade de defesa de um sistema. Desta forma, a introdução de agentes móveis em apoio a segurança computacional apresenta-se como uma solução natural, uma vez que permitirá a distribuição de tarefas de monitoramento do sistema e automatização do processo de tomada de decisão, no caso de ausência do administrador humano. Este trabalho apresenta uma avaliação do uso do mecanismo de agentes móveis para acrescentar características de mobilidade ao processo de monitoração de intrusão em sistemas computacionais. Uma abordagem modular é proposta, onde agentes pequenos e independentes monitoram o sistema. Esta abordagem apresenta significantes vantagens em termos de overhead, escalabilidade e flexibilidade. / The use of protection mechanisms must be improved due the increase of attacks from internal sources. As this kind of attack, made by internal users do not allow its immediate localization, it is necessary the integrated use of several technologies to enhance the defense capabilities of a system. Therefore, the introduction of mobile agents to provide security appears to be a natural solution. It will allow the distribution of the system monitoring tasks and automate the decision making process, in the absence of a human administrator. This project presents an evaluation of the use of mobile agents to add mobile capabilities to the process of intrusion detection in computer systems. A finer-grained approach is proposed, where small and independent agents monitor the system. This approach has significant advantages in terms of overhead, scalability and flexibility.

La sécurité informatique, aussi connue comme la cyber-sécurité, est toujours un sujet d'actualité dans la recherche en sciences informatiques. Comme les cyber-attaques grandissent de plus en plus en volume et en sophistication, la protection des systèmes ou réseaux d'information devient une tâche difficile. Les chercheurs dans la communauté de recherche prêtent une attention constante à la sécurité, en particulier ils s'orientent vers deux directions principales: (i) - la conception des infrastructures sécurisées avec des protocoles de communication sécurisés et (ii) - surveillance / supervision des systèmes ou des réseaux afin de trouver et de remédier des vulnérabilités. La dernière vérifie que tout ce qui a été conçu dans la première fonctionne correctement et en toute sécurité, ainsi détectant les violations de sécurité. Ceci étant le sujet principal de cette thèse.Cette dissertation présente un cadre de surveillance de la sécurité en tenant en compte des différents types de jeu de données d'audit y compris le trafic de réseaux et les messages échangés dans les applications. Nous proposons également des approches innovantes fondées sur l'apprentissage statistique, la théorie de l'information et de l'apprentissage automatique pour prétraiter et analyser l'entrée de données. Notre cadre est validé dans une large gamme des études de cas, y compris la surveillance des réseaux traditionnels TCP / IP (v4) (LAN, WAN, la surveillance de l'Internet), la supervision des réseaux de objets connectés utilisant la technologie 6LoWPAN (IPv6), et également, l’analyse des logs d'autres applications. Enfin, nous fournissons une étude sur la tolérance d’intrusion par conception et proposons une approche basée sur l’émulation pour détecter et tolérer l’intrusion simultanément.Dans chaque étude de cas, nous décrivons comment nous collectons les jeux de données d'audit, extrayons les attributs pertinents, traitons les données reçues et décodons leur signification de sécurité. Pour attendre ces objectifs, l'outil MMT est utilisé comme le cœur de notre approche. Nous évaluons également la performance de la solution et sa possibilité de marcher dans les systèmes “à plus grande échelle” avec des jeux de données plus volumineux / Computer security, also known as cyber-security or IT security, is always an emerging topic in computer science research. Because cyber attacks are growing in both volume and sophistication, protecting information systems or networks becomes a difficult task. Therefore, researchers in research community give an ongoing attention in security including two main directions: (i)-designing secured infrastructures with secured communication protocols and (ii)-monitoring/supervising the systems or networks in order to find and re-mediate vulnerabilities. The former assists the later by forming some additional monitoring-supporting modules. Whilst, the later verifies whether everything designed in the former is correctly and securely functioning as well as detecting security violations. This is the main topic of this thesis.This dissertation presents a security monitoring framework that takes into consideration different types of audit dataset including network traffic and application logs. We propose also some novel approaches based on supervised machine learning to pre-process and analyze the data input. Our framework is validated in a wide range of case studies including traditional TCP/IPv4 network monitoring (LAN, WAN, Internet monitoring), IoT/WSN using 6LoWPAN technology (IPv6), and other applications' logs. Last but not least, we provide a study regarding intrusion tolerance by design and propose an emulation-based approach to simultaneously detect and tolerate intrusion.In each case study, we describe how we collect the audit dataset, extract the relevant attributes, handle received data and decode their security meaning. For these goals, the tool Montimage Monitoring Tool (MMT) is used as the core of our approach. We assess also the solution's performance and its possibility to work in "larger scale" systems with more voluminous dataset

<div><div><div><p>The presence of attacks in day-to-day traffic flow in connected networks is considerably less compared to genuine traffic flow. Yet, the consequences of these attacks are disastrous. It is very important to identify if the network is being attacked and block these attempts to protect the network system. Failure to block these attacks can lead to loss of confidential information and reputation and can also lead to financial loss. One of the strategies to identify these attacks is to use machine learning algorithms that learn to identify attacks by looking at previous examples. But since the number of attacks is small, it is difficult to train these machine learning algorithms. This study aims to use generative techniques to create new attack samples that can be used to train the machine learning based intrusion detection systems to identify more attacks. Two metrics are used to verify that the training has improved and a binary classifier is used to perform a two-sample test for verifying the generated attacks.</p></div></div></div>

Detection of insider threats is vital within cybersecurity. Techniques for detection include honeytokens, which most often are resources that, through deception, seek to expose intruders. One kind of insider that is detectable via honeytokens is the masquerader. This project proposes implementing a masquerader detection technique where honeytokens are placed within users’ filesystems in such a way that they also provide Two Factor Authentication(2fa) functionality. If a user’s second factor – the honeytoken –is not accessed within a specified timeframe after login, this indicates a potential intrusion, and only a “fake” filesystem will remain available. An alert is also triggered. The intention is to deter insiders from masquerading since they are aware that they must access a uniquely located honeytokena fter logging in to the legitimate user’s account. The technique was evaluated via user-testing that included interviews, a checklist with requirements for feasibility, and a cyber-security expert’s opinion on the technique’s feasibility. The main question evaluated during the project was the feasibility of adding the proposed technique to a computer system’s protective capabilities. The results of the project indicated that the proposed technique is feasible. The project’s results were also compared with the results of prior related research. The project’s scope was limited to a Linux system accessed via SSH into a Bash terminal(non-GUI-compatible), and the implemented technique was also evaluated within such an environment.

The subject of the master's thesis is a design of security measures for securing of an industrial control system. It includes an analysis of characteristics of communication environment and specifics of industrial communication systems, a comparison of available technological means and a design of a solution according to investor's requirements.

No description available.

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