SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis

In this paper, we propose a novel approach, called SENATUS, for joint traffic anomaly detection and root-cause analysis. Inspired from the concept of a senate, the key idea of the proposed approach is divided into three stages: election, voting and decision. At the election stage, a small number of \nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{, which are used} to represent approximately the total (usually huge) set of traffic flows. In the voting stage, anomaly detection is applied on the senator flows and the detected anomalies are correlated to identify the most possible anomalous time bins. Finally in the decision stage, a machine learning technique is applied to the senator flows of each anomalous time bin to find the root cause of the anomalies. We evaluate SENATUS using traffic traces collected from the Pan European network, GEANT, and compare against another approach which detects anomalies using lossless compression of traffic histograms. We show the effectiveness of SENATUS in diagnosing anomaly types: network scans and DoS/DDoS attacks

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

Performance Evaluation of Network Anomaly Detection Systems

Nowadays, there is a huge and growing concern about security in information and communication technology (ICT) among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. Attacks, problems, and internal failures when not detected early may badly harm an entire Network system. Thus, this thesis presents an autonomous profile-based anomaly detection system based on the statistical method Principal Component Analysis (PCADS-AD). This approach creates a network profile called Digital Signature of Network Segment using Flow Analysis (DSNSF) that denotes the predicted normal behavior of a network traffic activity through historical data analysis. That digital signature is used as a threshold for volume anomaly detection to detect disparities in the normal traffic trend. The proposed system uses seven traffic flow attributes: Bits, Packets and Number of Flows to detect problems, and Source and Destination IP addresses and Ports, to provides the network administrator necessary information to solve them. Via evaluation techniques, addition of a different anomaly detection approach, and comparisons to other methods performed in this thesis using real network traffic data, results showed good traffic prediction by the DSNSF and encouraging false alarm generation and detection accuracy on the detection schema. The observed results seek to contribute to the advance of the state of the art in methods and strategies for anomaly detection that aim to surpass some challenges that emerge from the constant growth in complexity, speed and size of today’s large scale networks, also providing high-value results for a better detection in real time.Atualmente, existe uma enorme e crescente preocupação com segurança em tecnologia da informação e comunicação (TIC) entre a comunidade científica. Isto porque qualquer ataque ou anomalia na rede pode afetar a qualidade, interoperabilidade, disponibilidade, e integridade em muitos domínios, como segurança nacional, armazenamento de dados privados, bem-estar social, questões econômicas, e assim por diante. Portanto, a deteção de anomalias é uma ampla área de pesquisa, e muitas técnicas e abordagens diferentes para esse propósito surgiram ao longo dos anos. Ataques, problemas e falhas internas quando não detetados precocemente podem prejudicar gravemente todo um sistema de rede. Assim, esta Tese apresenta um sistema autônomo de deteção de anomalias baseado em perfil utilizando o método estatístico Análise de Componentes Principais (PCADS-AD). Essa abordagem cria um perfil de rede chamado Assinatura Digital do Segmento de Rede usando Análise de Fluxos (DSNSF) que denota o comportamento normal previsto de uma atividade de tráfego de rede por meio da análise de dados históricos. Essa assinatura digital é utilizada como um limiar para deteção de anomalia de volume e identificar disparidades na tendência de tráfego normal. O sistema proposto utiliza sete atributos de fluxo de tráfego: bits, pacotes e número de fluxos para detetar problemas, além de endereços IP e portas de origem e destino para fornecer ao administrador de rede as informações necessárias para resolvê-los. Por meio da utilização de métricas de avaliação, do acrescimento de uma abordagem de deteção distinta da proposta principal e comparações com outros métodos realizados nesta tese usando dados reais de tráfego de rede, os resultados mostraram boas previsões de tráfego pelo DSNSF e resultados encorajadores quanto a geração de alarmes falsos e precisão de deteção. Com os resultados observados nesta tese, este trabalho de doutoramento busca contribuir para o avanço do estado da arte em métodos e estratégias de deteção de anomalias, visando superar alguns desafios que emergem do constante crescimento em complexidade, velocidade e tamanho das redes de grande porte da atualidade, proporcionando também alta performance. Ainda, a baixa complexidade e agilidade do sistema proposto contribuem para que possa ser aplicado a deteção em tempo real

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enables the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as optimized automation via outsourced cloud analytics and increased building-grid integrations. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber-secure resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. The impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber-secure resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.Comment: 38 pages, 7 figures, 6 tables, submitted to Annual Reviews in Contro

USING A K-NEAREST NEIGHBORS MACHINE LEARNING APPROACH TO DETECT CYBERATTACKS ON THE NAVY SMART GRID

In 2019, the Naval Facilities Engineering Command (NAVFAC) deployed the Navy smart grid across multiple bases in the United States. The smart grid can improve the reliability, availability, and efficiency of electricity supply. While this brings about immense benefit, placing the grid on a network connected to the internet increases the threat of cyberattacks aimed at intelligence collection, disruption, and destruction. In this thesis, we propose an Intrusion Detection System (IDS) for the NAVFAC smart grid. This IDS comprises a feature extractor, classifier, anomaly detector, and response manager. We use the K-Nearest Neighbors machine learning algorithm to show that various attacks (web attacks, FTP/SSH attacks, DOS, DDOS and port scanning) can be grouped into broader attack classes of Active, Denial, and Probe for appropriate response management. We also show that in order to reduce the load on the security operations center (SOC), the accuracy of the classifier can be maximized by optimizing the value of k, which is the number of data points nearest to the sample under consideration that decides the class assigned.http://archive.org/details/usingaknearestne1094566054Outstanding ThesisCommander, Republic of Singapore NavyApproved for public release. distribution is unlimite

A Multi Agent System for Flow-Based Intrusion Detection

The detection and elimination of threats to cyber security is essential for system functionality, protection of valuable information, and preventing costly destruction of assets. This thesis presents a Mobile Multi-Agent Flow-Based IDS called MFIREv3 that provides network anomaly detection of intrusions and automated defense. This version of the MFIRE system includes the development and testing of a Multi-Objective Evolutionary Algorithm (MOEA) for feature selection that provides agents with the optimal set of features for classifying the state of the network. Feature selection provides separable data points for the selected attacks: Worm, Distributed Denial of Service, Man-in-the-Middle, Scan, and Trojan. This investigation develops three techniques of self-organization for multiple distributed agents in an intrusion detection system: Reputation, Stochastic, and Maximum Cover. These three movement models are tested for effectiveness in locating good agent vantage points within the network to classify the state of the network. MFIREv3 also introduces the design of defensive measures to limit the effects of network attacks. Defensive measures included in this research are rate-limiting and elimination of infected nodes. The results of this research provide an optimistic outlook for flow-based multi-agent systems for cyber security. The impact of this research illustrates how feature selection in cooperation with movement models for multi agent systems provides excellent attack detection and classification

Distributed reflection denial of service attack: A critical review

As the world becomes increasingly connected and the number of users grows exponentially and “things” go online, the prospect of cyberspace becoming a significant target for cybercriminals is a reality. Any host or device that is exposed on the internet is a prime target for cyberattacks. A denial-of-service (DoS) attack is accountable for the majority of these cyberattacks. Although various solutions have been proposed by researchers to mitigate this issue, cybercriminals always adapt their attack approach to circumvent countermeasures. One of the modified DoS attacks is known as distributed reflection denial-of-service attack (DRDoS). This type of attack is considered to be a more severe variant of the DoS attack and can be conducted in transmission control protocol (TCP) and user datagram protocol (UDP). However, this attack is not effective in the TCP protocol due to the three-way handshake approach that prevents this type of attack from passing through the network layer to the upper layers in the network stack. On the other hand, UDP is a connectionless protocol, so most of these DRDoS attacks pass through UDP. This study aims to examine and identify the differences between TCP-based and UDP-based DRDoS attacks

Towards Protection Against Low-Rate Distributed Denial of Service Attacks in Platform-as-a-Service Cloud Services

Nowadays, the variety of technology to perform daily tasks is abundant and different business and people benefit from this diversity. The more technology evolves, more useful it gets and in contrast, they also become target for malicious users. Cloud Computing is one of the technologies that is being adopted by different companies worldwide throughout the years. Its popularity is essentially due to its characteristics and the way it delivers its services. This Cloud expansion also means that malicious users may try to exploit it, as the research studies presented throughout this work revealed. According to these studies, Denial of Service attack is a type of threat that is always trying to take advantage of Cloud Computing Services. Several companies moved or are moving their services to hosted environments provided by Cloud Service Providers and are using several applications based on those services. The literature on the subject, bring to attention that because of this Cloud adoption expansion, the use of applications increased. Therefore, DoS threats are aiming the Application Layer more and additionally, advanced variations are being used such as Low-Rate Distributed Denial of Service attacks. Some researches are being conducted specifically for the detection and mitigation of this kind of threat and the significant problem found within this DDoS variant, is the difficulty to differentiate malicious traffic from legitimate user traffic. The main goal of this attack is to exploit the communication aspect of the HTTP protocol, sending legitimate traffic with small changes to fill the requests of a server slowly, resulting in almost stopping the access of real users to the server resources during the attack. This kind of attack usually has a small time window duration but in order to be more efficient, it is used within infected computers creating a network of attackers, transforming into a Distributed attack. For this work, the idea to battle Low-Rate Distributed Denial of Service attacks, is to integrate different technologies inside an Hybrid Application where the main goal is to identify and separate malicious traffic from legitimate traffic. First, a study is done to observe the behavior of each type of Low-Rate attack in order to gather specific information related to their characteristics when the attack is executing in real-time. Then, using the Tshark filters, the collection of those packet information is done. The next step is to develop combinations of specific information obtained from the packet filtering and compare them. Finally, each packet is analyzed based on these combinations patterns. A log file is created to store the data gathered after the Entropy calculation in a friendly format. In order to test the efficiency of the application, a Cloud virtual infrastructure was built using OpenNebula Sandbox and Apache Web Server. Two tests were done against the infrastructure, the first test had the objective to verify the effectiveness of the tool proportionally against the Cloud environment created. Based on the results of this test, a second test was proposed to demonstrate how the Hybrid Application works against the attacks performed. The conclusion of the tests presented how the types of Slow-Rate DDoS can be disruptive and also exhibited promising results of the Hybrid Application performance against Low-Rate Distributed Denial of Service attacks. The Hybrid Application was successful in identify each type of Low-Rate DDoS, separate the traffic and generate few false positives in the process. The results are displayed in the form of parameters and graphs.Actualmente, a variedade de tecnologias que realizam tarefas diárias é abundante e diferentes empresas e pessoas se beneficiam desta diversidade. Quanto mais a tecnologia evolui, mais usual se torna, em contraposição, essas empresas acabam por se tornar alvo de actividades maliciosas. Computação na Nuvem é uma das tecnologias que vem sendo adoptada por empresas de diferentes segmentos ao redor do mundo durante anos. Sua popularidade se deve principalmente devido as suas características e a maneira com o qual entrega seus serviços ao cliente. Esta expansão da Computação na Nuvem também implica que usuários maliciosos podem tentar explorá-la, como revela estudos de pesquisas apresentados ao longo deste trabalho. De acordo também com estes estudos, Ataques de Negação de Serviço são um tipo de ameaça que sempre estão a tentar tirar vantagens dos serviços de Computação na Nuvem. Várias empresas moveram ou estão a mover seus serviços para ambientes hospedados fornecidos por provedores de Computação na Nuvem e estão a utilizar várias aplicações baseadas nestes serviços. A literatura existente sobre este tema chama atenção sobre o fato de que, por conta desta expansão na adopção à serviços na Nuvem, o uso de aplicações aumentou. Portanto, ameaças de Negação de Serviço estão visando mais a camada de aplicação e também, variações de ataques mais avançados estão sendo utilizadas como Negação de Serviço Distribuída de Baixa Taxa. Algumas pesquisas estão a ser feitas relacionadas especificamente para a detecção e mitigação deste tipo de ameaça e o maior problema encontrado nesta variante é diferenciar tráfego malicioso de tráfego legítimo. O objectivo principal desta ameaça é explorar a maneira como o protocolo HTTP trabalha, enviando tráfego legítimo com pequenas modificações para preencher as solicitações feitas a um servidor lentamente, tornando quase impossível para usuários legítimos aceder os recursos do servidor durante o ataque. Este tipo de ataque geralmente tem uma janela de tempo curta mas para obter melhor eficiência, o ataque é propagado utilizando computadores infectados, criando uma rede de ataque, transformando-se em um ataque distribuído. Para este trabalho, a ideia para combater Ataques de Negação de Serviço Distribuída de Baixa Taxa é integrar diferentes tecnologias dentro de uma Aplicação Híbrida com o objectivo principal de identificar e separar tráfego malicioso de tráfego legítimo. Primeiro, um estudo é feito para observar o comportamento de cada tipo de Ataque de Baixa Taxa, a fim de recolher informações específicas relacionadas às suas características quando o ataque é executado em tempo-real. Então, usando os filtros do programa Tshark, a obtenção destas informações é feita. O próximo passo é criar combinações das informações específicas obtidas dos pacotes e compará-las. Então finalmente, cada pacote é analisado baseado nos padrões de combinações feitos. Um arquivo de registo é criado ao fim para armazenar os dados recolhidos após o cálculo da Entropia em um formato amigável. A fim de testar a eficiência da Aplicação Híbrida, uma infra-estrutura Cloud virtual foi construída usando OpenNebula Sandbox e servidores Apache. Dois testes foram feitos contra a infra-estrutura, o primeiro teste teve o objectivo de verificar a efectividade da ferramenta proporcionalmente contra o ambiente de Nuvem criado. Baseado nos resultados deste teste, um segundo teste foi proposto para verificar o funcionamento da Aplicação Híbrida contra os ataques realizados. A conclusão dos testes mostrou como os tipos de Ataques de Negação de Serviço Distribuída de Baixa Taxa podem ser disruptivos e também revelou resultados promissores relacionados ao desempenho da Aplicação Híbrida contra esta ameaça. A Aplicação Híbrida obteve sucesso ao identificar cada tipo de Ataque de Negação de Serviço Distribuída de Baixa Taxa, em separar o tráfego e gerou poucos falsos positivos durante o processo. Os resultados são exibidos em forma de parâmetros e grafos

Deteção de ataques de negação de serviços distribuídos na origem

From year to year new records of the amount of traffic in an attack are established, which demonstrate not only the constant presence of distributed denialof-service attacks, but also its evolution, demarcating itself from the other network threats. The increasing importance of resource availability alongside the security debate on network devices and infrastructures is continuous, given the preponderant role in both the home and corporate domains. In the face of the constant threat, the latest network security systems have been applying pattern recognition techniques to infer, detect, and react more quickly and assertively. This dissertation proposes methodologies to infer network activities patterns, based on their traffic: follows a behavior previously defined as normal, or if there are deviations that raise suspicions about the normality of the action in the network. It seems that the future of network defense systems continues in this direction, not only by increasing amount of traffic, but also by the diversity of actions, services and entities that reflect different patterns, thus contributing to the detection of anomalous activities on the network. The methodologies propose the collection of metadata, up to the transport layer of the osi model, which will then be processed by the machien learning algorithms in order to classify the underlying action. Intending to contribute beyond denial-of-service attacks and the network domain, the methodologies were described in a generic way, in order to be applied in other scenarios of greater or less complexity. The third chapter presents a proof of concept with attack vectors that marked the history and a few evaluation metrics that allows to compare the different classifiers as to their success rate, given the various activities in the network and inherent dynamics. The various tests show flexibility, speed and accuracy of the various classification algorithms, setting the bar between 90 and 99 percent.De ano para ano são estabelecidos novos recordes de quantidade de tráfego num ataque, que demonstram não só a presença constante de ataques de negação de serviço distribuídos, como também a sua evolução, demarcando-se das outras ameaças de rede. A crescente importância da disponibilidade de recursos a par do debate sobre a segurança nos dispositivos e infraestruturas de rede é contínuo, dado o papel preponderante tanto no dominio doméstico como no corporativo. Face à constante ameaça, os sistemas de segurança de rede mais recentes têm vindo a aplicar técnicas de reconhecimento de padrões para inferir, detetar e reagir de forma mais rápida e assertiva. Esta dissertação propõe metodologias para inferir padrões de atividades na rede, tendo por base o seu tráfego: se segue um comportamento previamente definido como normal, ou se existem desvios que levantam suspeitas sobre normalidade da ação na rede. Tudo indica que o futuro dos sistemas de defesa de rede continuará neste sentido, servindo-se não só do crescente aumento da quantidade de tráfego, como também da diversidade de ações, serviços e entidades que refletem padrões distintos contribuindo assim para a deteção de atividades anómalas na rede. As metodologias propõem a recolha de metadados, até á camada de transporte, que seguidamente serão processados pelos algoritmos de aprendizagem automática com o objectivo de classificar a ação subjacente. Pretendendo que o contributo fosse além dos ataques de negação de serviço e do dominio de rede, as metodologias foram descritas de forma tendencialmente genérica, de forma a serem aplicadas noutros cenários de maior ou menos complexidade. No quarto capítulo é apresentada uma prova de conceito com vetores de ataques que marcaram a história e, algumas métricas de avaliação que permitem comparar os diferentes classificadores quanto à sua taxa de sucesso, face às várias atividades na rede e inerentes dinâmicas. Os vários testes mostram flexibilidade, rapidez e precisão dos vários algoritmos de classificação, estabelecendo a fasquia entre os 90 e os 99 por cento.Mestrado em Engenharia de Computadores e Telemátic

Scalable schemes against Distributed Denial of Service attacks

Defense against Distributed Denial of Service (DDoS) attacks is one of the primary concerns on the Internet today. DDoS attacks are difficult to prevent because of the open, interconnected nature of the Internet and its underlying protocols, which can be used in several ways to deny service. Attackers hide their identity by using third parties such as private chat channels on IRC (Internet Relay Chat). They also insert false return IP address, spoofing, in a packet which makes it difficult for the victim to determine the packet\u27s origin. We propose three novel and realistic traceback mechanisms which offer many advantages over the existing schemes. All the three schemes take advantage of the Autonomous System topology and consider the fact that the attacker\u27s packets may traverse through a number of domains under different administrative control. Most of the traceback mechanisms make wrong assumptions that the network details of a company under an administrative control are disclosed to the public. For security reasons, this is not the case most of the times. The proposed schemes overcome this drawback by considering reconstruction at the inter and intra AS levels. Hierarchical Internet Traceback (HIT) and Simple Traceback Mechanism (STM) trace back to an attacker in two phases. In the first phase the attack originating Autonomous System is identified while in the second phase the attacker within an AS is identified. Both the schemes, HIT and STM, allow the victim to trace back to the attackers in a few seconds. Their computational overhead is very low and they scale to large distributed attacks with thousands of attackers. Fast Autonomous System Traceback allows complete attack path reconstruction with few packets. We use traceroute maps of real Internet topologies CAIDA\u27s skitter to simulate DDoS attacks and validate our design