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.

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

Thesis (M.B.A.)--Boston University

Telehealth is an online health care system that is extensively used in the current pandemic situation. Our proposed technique is considered a fog computing-based attack detection architecture to protect IoT Telehealth Networks. As for IoT Telehealth Networks, the sensor/actuator edge devices are considered the weakest link in the IoT system and are obvious targets of attacks such as botnet attacks. In this thesis, we introduce a novel framework that employs several machine learning and data analysis techniques to detect those attacks. We evaluate the effectiveness of the proposed framework using two publicly available datasets from real-world scenarios. These datasets contain a variety of attacks with different characteristics. The robustness of the proposed framework and its ability, to detect and distinguish between the existing IoT attacks that are tested by combining the two datasets for cross-evaluation. This combination is based on a novel technique for generating supplementary data instances, which employs GAN (generative adversarial networks) for data augmentation and to ensure that the number of samples and features are balanced. / Graduate

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.

<div>Unmanned aerial vehicles (UAVs) have gained more attention in recent years because of their ability to execute various missions. However, recent works have identified vulnerabilities in UAV systems that make them more readily prone to cyberattacks. In this work, the vulnerabilities in the communication channel between the UAV and ground control station are exploited to implement cyberattacks, specifically, the denial of service and false data injection attacks. Unlike other related studies that implemented attacks in simulations, we demonstrate the actual implementation of these attacks on a Holybro S500 quadrotor with PX4 autopilot firmware and MAVLink communication protocol.</div><div><br></div><div>The goal was to create a lightweight intrusion detection system (IDS) that leverages recurrent neural networks (RNNs) to accurately detect cyberattacks, even when implemented on a resource-constrained platform. Different types of RNNs, including simple RNNs, long short-term memory, gated recurrent units, and simple recurrent units, were trained and tested on actual experimental data. A recursive feature elimination approach was carried out on selected features to remove redundant features and to create a lighter RNN IDS model. We also studied the resource consumption of these RNNs on an Arduino Uno board, the lowest-cost companion computer that can be implemented with PX4 autopilot firmware and Pixhawk autopilot boards. The results show that a simple RNN has the best accuracy while also satisfying the constraints of the selected computer.<br></div>

Computer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal -- firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other hand, anomaly-based ID systems determine what is normal traffic within a network and reports abnormal traffic behavior. This paper will describe a methodology towards developing a more-robust Intrusion Detection System through the use of data-mining techniques and anomaly detection. These data-mining techniques will dynamically model what a normal network should look like and reduce the false positive and false negative alarm rates in the process. We will use classification-tree techniques to accurately predict probable attack sessions. Overall, our goal is to model network traffic into network sessions and identify those network sessions that have a high-probability of being an attack and can be labeled as a "suspect session." Subsequently, we will use these techniques inclusive of signature detection methods, as they will be used in concert with known signatures and patterns in order to present a better model for detection and protection of networks and systems.

Artificial Intelligence (AI) has found increasing application in various domains, revolutionizing problem-solving and data analysis. However, in decision-sensitive areas like Intrusion Detection Systems (IDS), trust and reliability are vital, posing challenges for traditional black box AI systems. These black box IDS, while accurate, lack transparency, making it difficult to understand the reasons behind their decisions. This dissertation explores the concept of eXplainable Intrusion Detection Systems (X-IDS), addressing the issue of trust in X-IDS. It explores the limitations of common black box IDS and the complexities of explainability methods, leading to the fundamental question of trusting explanations generated by black box explainer modules. To address these challenges, this dissertation presents the concept of white box explanations, which are innately explainable. While white box algorithms are typically simpler and more interpretable, they often sacrifice accuracy. However, this work utilized white box Competitive Learning (CL), which can achieve competitive accuracy in comparison to black box IDS. We introduce Rule Extraction (RE) as another white box technique that can be applied to explain black box IDS. It involves training decision trees on the inputs, weights, and outputs of black box models, resulting in human-readable rulesets that serve as global model explanations. These white box techniques offer the benefits of accuracy and trustworthiness, which are challenging to achieve simultaneously. This work aims to address gaps in the existing literature, including the need for highly accurate white box IDS, a methodology for understanding explanations, small testing datasets, and comparisons between white box and black box models. To achieve these goals, the study employs CL and eclectic RE algorithms. CL models offer innate explainability and high accuracy in IDS applications, while eclectic RE enhances trustworthiness. The contributions of this dissertation include a novel X-IDS architecture featuring Self-Organizing Map (SOM) models that adhere to DARPA’s guidelines for explainable systems, an extended X-IDS architecture incorporating three CL-based algorithms, and a hybrid X-IDS architecture combining a Deep Neural Network (DNN) predictor with a white box eclectic RE explainer. These architectures create more explainable, trustworthy, and accurate X-IDS systems, paving the way for enhanced AI solutions in decision-sensitive domains.

Several algorithms have been proposed to implement intrusion detection systems (IDS) based on the idea that anomalies in the behavior of a system might be produced by a set of actions of an intruder or by a system fault. Almost no previous research has been conducted in the area of anomaly detection for high performance clusters. The research reported in this thesis demonstrates that the analysis of sequences of function calls issued by one or more processes can be used to verify the correct execution of parallel programs written in C/C++ with the Message Passing Interface (MPI) in a cluster of Linux workstations. The functions calls were collected via library interposition. Two anomaly detection algorithms previously reported to be effective methods for anomaly detection in sequences of system calls, Hidden Markov Model and sequence matching, were implemented and tested. In general, the simpler sequence matching algorithm out-performed the Hidden Markov Model.