A composable approach to design of newer techniques for large-scale denial-of-service attack attribution

Since its early days, the Internet has witnessed not only a phenomenal growth, but also a large number of security attacks, and in recent years, denial-of-service (DoS) attacks have emerged as one of the top threats. The stateless and destination-oriented Internet routing combined with the ability to harness a large number of compromised machines and the relative ease and low costs of launching such attacks has made this a hard problem to address. Additionally, the myriad requirements of scalability, incremental deployment, adequate user privacy protections, and appropriate economic incentives has further complicated the design of DDoS defense mechanisms. While the many research proposals to date have focussed differently on prevention, mitigation, or traceback of DDoS attacks, the lack of a comprehensive approach satisfying the different design criteria for successful attack attribution is indeed disturbing. Our first contribution here has been the design of a composable data model that has helped us represent the various dimensions of the attack attribution problem, particularly the performance attributes of accuracy, effectiveness, speed and overhead, as orthogonal and mutually independent design considerations. We have then designed custom optimizations along each of these dimensions, and have further integrated them into a single composite model, to provide strong performance guarantees. Thus, the proposed model has given us a single framework that can not only address the individual shortcomings of the various known attack attribution techniques, but also provide a more wholesome counter-measure against DDoS attacks. Our second contribution here has been a concrete implementation based on the proposed composable data model, having adopted a graph-theoretic approach to identify and subsequently stitch together individual edge fragments in the Internet graph to reveal the true routing path of any network data packet. The proposed approach has been analyzed through theoretical and experimental evaluation across multiple metrics, including scalability, incremental deployment, speed and efficiency of the distributed algorithm, and finally the total overhead associated with its deployment. We have thereby shown that it is realistically feasible to provide strong performance and scalability guarantees for Internet-wide attack attribution. Our third contribution here has further advanced the state of the art by directly identifying individual path fragments in the Internet graph, having adopted a distributed divide-and-conquer approach employing simple recurrence relations as individual building blocks. A detailed analysis of the proposed approach on real-life Internet topologies with respect to network storage and traffic overhead, has provided a more realistic characterization. Thus, not only does the proposed approach lend well for simplified operations at scale but can also provide robust network-wide performance and security guarantees for Internet-wide attack attribution. Our final contribution here has introduced the notion of anonymity in the overall attack attribution process to significantly broaden its scope. The highly invasive nature of wide-spread data gathering for network traceback continues to violate one of the key principles of Internet use today - the ability to stay anonymous and operate freely without retribution. In this regard, we have successfully reconciled these mutually divergent requirements to make it not only economically feasible and politically viable but also socially acceptable. This work opens up several directions for future research - analysis of existing attack attribution techniques to identify further scope for improvements, incorporation of newer attributes into the design framework of the composable data model abstraction, and finally design of newer attack attribution techniques that comprehensively integrate the various attack prevention, mitigation and traceback techniques in an efficient manner

Flow-oriented anomaly-based detection of denial of service attacks with flow-control-assisted mitigation

Flooding-based distributed denial-of-service (DDoS) attacks present a serious and major threat to the targeted enterprises and hosts. Current protection technologies are still largely inadequate in mitigating such attacks, especially if they are large-scale. In this doctoral dissertation, the Computer Network Management and Control System (CNMCS) is proposed and investigated; it consists of the Flow-based Network Intrusion Detection System (FNIDS), the Flow-based Congestion Control (FCC) System, and the Server Bandwidth Management System (SBMS). These components form a composite defense system intended to protect against DDoS flooding attacks. The system as a whole adopts a flow-oriented and anomaly-based approach to the detection of these attacks, as well as a control-theoretic approach to adjust the flow rate of every link to sustain the high priority flow-rates at their desired level. The results showed that the misclassification rates of FNIDS are low, less than 0.1%, for the investigated DDOS attacks, while the fine-grained service differentiation and resource isolation provided within the FCC comprise a novel and powerful built-in protection mechanism that helps mitigate DDoS attacks

Packet filter performance monitor (anti-DDOS algorithm for hybrid topologies)

DDoS attacks are increasingly becoming a major problem. According to Arbor Networks, the largest DDoS attack reported by a respondent in 2015 was 500 Gbps. Hacker News stated that the largest DDoS attack as of March 2016 was over 600 Gbps, and the attack targeted the entire BBC website. With this increasing frequency and threat, and the average DDoS attack duration at about 16 hours, we know for certain that DDoS attacks will not be going away anytime soon. Commercial companies are not effectively providing mitigation techniques against these attacks, considering that major corporations face the same challenges. Current security appliances are not strong enough to handle the overwhelming traffic that accompanies current DDoS attacks. There is also a limited research on solutions to mitigate DDoS attacks. Therefore, there is a need for a means of mitigating DDoS attacks in order to minimize downtime. One possible solution is for organizations to implement their own architectures that are meant to mitigate DDoS attacks. In this dissertation, we present and implement an architecture that utilizes an activity monitor to change the states of firewalls based on their performance in a hybrid network. Both firewalls are connected inline. The monitor is mirrored to monitor the firewall states. The monitor reroutes traffic when one of the firewalls become overwhelmed due to a HTTP DDoS flooding attack. The monitor connects to the API of both firewalls. The communication between the rewalls and monitor is encrypted using AES, based on PyCrypto Python implementation. This dissertation is structured in three parts. The first found the weakness of the hardware firewall and determined its threshold based on spike and endurance tests. This was achieved by flooding the hardware firewall with HTTP packets until the firewall became overwhelmed and unresponsive. The second part implements the same test as the first, but targeted towards the virtual firewall. The same parameters, test factors, and determinants were used; however a different load tester was utilized. The final part was the implementation and design of the firewall performance monitor. The main goal of the dissertation is to minimize downtime when network firewalls are overwhelmed as a result of a DDoS attack

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

Using honeypots to trace back amplification DDoS attacks

In today’s interconnected world, Denial-of-Service attacks can cause great harm by simply rendering a target system or service inaccessible. Amongst the most powerful and widespread DoS attacks are amplification attacks, in which thousands of vulnerable servers are tricked into reflecting and amplifying attack traffic. However, as these attacks inherently rely on IP spoofing, the true attack source is hidden. Consequently, going after the offenders behind these attacks has so far been deemed impractical. This thesis presents a line of work that enables practical attack traceback supported by honeypot reflectors. To this end, we investigate the tradeoffs between applicability, required a priori knowledge, and traceback granularity in three settings. First, we show how spoofed attack packets and non-spoofed scan packets can be linked using honeypot-induced fingerprints, which allows attributing attacks launched from the same infrastructures as scans. Second, we present a classifier-based approach to trace back attacks launched from booter services after collecting ground-truth data through self-attacks. Third, we propose to use BGP poisoning to locate the attacking network without prior knowledge and even when attack and scan infrastructures are disjoint. Finally, as all of our approaches rely on honeypot reflectors, we introduce an automated end-to-end pipeline to systematically find amplification vulnerabilities and synthesize corresponding honeypots.In der heutigen vernetzten Welt können Denial-of-Service-Angriffe große Schäden verursachen, einfach indem sie ihr Zielsystem unerreichbar machen. Zu den stärksten und verbreitetsten DoS-Angriffen zählen Amplification-Angriffe, bei denen tausende verwundbarer Server missbraucht werden, um Angriffsverkehr zu reflektieren und zu verstärken. Da solche Angriffe jedoch zwingend gefälschte IP-Absenderadressen nutzen, ist die wahre Angriffsquelle verdeckt. Damit gilt die Verfolgung der Täter bislang als unpraktikabel. Diese Dissertation präsentiert eine Reihe von Arbeiten, die praktikable Angriffsrückverfolgung durch den Einsatz von Honeypots ermöglicht. Dazu untersuchen wir das Spannungsfeld zwischen Anwendbarkeit, benötigtem Vorwissen, und Rückverfolgungsgranularität in drei Szenarien. Zuerst zeigen wir, wie gefälschte Angriffs- und ungefälschte Scan-Datenpakete miteinander verknüpft werden können. Dies ermöglicht uns die Rückverfolgung von Angriffen, die ebenfalls von Scan-Infrastrukturen aus durchgeführt wurden. Zweitens präsentieren wir einen Klassifikator-basierten Ansatz um Angriffe durch Booter-Services mittels vorher durch Selbstangriffe gesammelter Daten zurückzuverfolgen. Drittens zeigen wir auf, wie BGP Poisoning genutzt werden kann, um ohne weiteres Vorwissen das angreifende Netzwerk zu ermitteln. Schließlich präsentieren wir einen automatisierten Prozess, um systematisch Schwachstellen zu finden und entsprechende Honeypots zu synthetisieren

Intrusion Detection System against Denial of Service attack in Software-Defined Networking

Das exponentielle Wachstum der Online-Dienste und des über die Kommunikationsnetze übertragenen Datenvolumens macht es erforderlich, die Struktur traditioneller Netzwerke durch ein neues Paradigma zu ersetzen, das sich den aktuellen Anforderungen anpasst. Software-Defined Networking (SDN) ist hierfür eine fortschrittliche Netzwerkarchitektur, die darauf abzielt, das traditionelle Netzwerk in ein flexibleres Netzwerk umzuwandeln, das sich an die wachsenden Anforderungen anpasst. Im Gegensatz zum traditionellen Netzwerk ermöglicht SDN die Entkopplung von Steuer- und Datenebene, um Netzwerkressourcen effizient zu überwachen, zu konfigurieren und zu optimieren. Es verfügt über einen zentralisierten Controller mit einer globalen Netzwerksicht, der seine Ressourcen über programmierbare Schnittstellen verwaltet. Die zentrale Steuerung bringt jedoch neue Sicherheitsschwachstellen mit sich und fungiert als Single Point of Failure, den ein böswilliger Benutzer ausnutzen kann, um die normale Netzwerkfunktionalität zu stören. So startet der Angreifer einen massiven Datenverkehr, der als Distributed-Denial-of-Service Angriff (DDoSAngriff) von der SDN-Infrastrukturebene in Richtung des Controllers bekannt ist. Dieser DDoS-Angriff führt zu einer Sättigung der Steuerkanal-Bandbreite und belegt die Ressourcen des Controllers. Darüber hinaus erbt die SDN-Architektur einige Angriffsarten aus den traditionellen Netzwerken. Der Angreifer fälscht beispielweise die Pakete, um gutartig zu erscheinen, und zielt dann auf die traditionellen DDoS-Ziele wie Hosts, Server, Anwendungen und Router ab. In dieser Arbeit wird das Verhalten von böswilligen Benutzern untersucht. Anschließend wird ein Intrusion Detection System (IDS) zum Schutz der SDN-Umgebung vor DDoS-Angriffen vorgestellt. Das IDS berücksichtigt dabei drei Ansätze, um ausreichendes Feedback über den laufenden Verkehr durch die SDN-Architektur zu erhalten: die Informationen von einem externen Gerät, den OpenFlow-Kanal und die Flow-Tabelle. Daher besteht das vorgeschlagene IDS aus drei Komponenten. Das Inspector Device verhindert, dass böswillige Benutzer einen Sättigungsangriff auf den SDN-Controller starten. Die Komponente Convolutional Neural Network (CNN) verwendet eindimensionale neuronale Faltungsnetzwerke (1D-CNN), um den Verkehr des Controllers über den OpenFlow-Kanal zu analysieren. Die Komponente Deep Learning Algorithm(DLA) verwendet Recurrent Neural Networks (RNN), um die vererbten DDoS-Angriffe zu erkennen. Sie unterstützt auch die Unterscheidung zwischen bösartigen und gutartigen Benutzern als neue Gegenmaßnahme. Am Ende dieser Arbeit werden alle vorgeschlagenen Komponenten mit dem Netzwerkemulator Mininet und der Programmiersprache Python modelliert, um ihre Machbarkeit zu testen. Die Simulationsergebnisse zeigen hierbei, dass das vorgeschlagene IDS im Vergleich zu mehreren Benchmarking- und State-of-the-Art-Vorschlägen überdurchschnittliche Leistungen erbringt.The exponential growth of online services and the data volume transferred over the communication networks raises the need to change the structure of traditional networks to a new paradigm that adapts to the development’s demands. Software- Defined Networking (SDN) is an advanced network architecture aiming to evolve and transform the traditional network into a more flexible network that responds to the new requirements. In contrast to the traditional network, SDN allows decoupling of the control and data planes functionalities to monitor, configure, and optimize network resources efficiently. It has a centralized controller with a global network view to manage its resources using programmable interfaces. The central control brings new security vulnerabilities and acts as a single point of failure, which the malicious user might exploit to disrupt the network functionality. Thus, the attacker launches massive traffic known as Distributed Denial of Service (DDoS) attack from the SDN infrastructure layer towards the controller. This DDoS attack leads to saturation of control channel bandwidth and destroys the controller resources. Furthermore, the SDN architecture inherits some attacks types from the traditional networks. Therefore, the attacker forges the packets to appear benign and then targets the traditional DDoS objectives such as hosts, servers, applications, routers. This work observes the behavior of malicious users. It then presents an Intrusion Detection System (IDS) to safeguard the SDN environment against DDoS attacks. The IDS considers three approaches to obtain sufficient feedback about the ongoing traffic through the SDN architecture: the information from an external device, the OpenFlow channel, and the flow table. Therefore, the proposed IDS consists of three components; Inspector Device prevents the malicious users from launching the saturation attack towards the SDN controller. Convolutional Neural Network (CNN) Component employs the One- Dimensional Convolutional Neural Networks (1D-CNN) to analyze the controller’s traffic through the OpenFlow Channel. The Deep Learning Algorithm (DLA) component employs Recurrent Neural Networks (RNN) to detect the inherited DDoS attacks. The IDS also supports distinguishing between malicious and benign users as a new countermeasure. At the end of this work, the network emulator Mininet and the programming language python model all the proposed components to test their feasibility. The simulation results demonstrate that the proposed IDS outperforms compared several benchmarking and state-of-the-art suggestions

Dynamic Shifting of Virtual Network Topologies for Network Attack Prevention

Computer networks were not designed with security in mind, making research into the subject of network security vital. Virtual Networks are similar to computer networks, except the components of a Virtual Network are in software rather than hardware. With the constant threat of attacks on networks, security is always a big concern, and Virtual Networks are no different. Virtual Networks have many potential attack vectors similar to physical networks, making research into Virtual Network security of great importance. Virtual Networks, since they are composed of virtualized network components, have the ability to dynamically change topologies. In this paper, we explore Virtual Networks and their ability to quickly shift their network topology. We investigate the potential use of this flexibility to protect network resources and defend against malicious activities. To show the ability of reactively shifting a Virtual Network’s topology to se- cure a network, we create a set of four experiments, each with a different dynamic topology shift, or “dynamic defense”. These four groups of experiments are called the Server Protection, Isolated Subnet, Distributed Port Group, and Standard Port Group experiments. The Server Protection experiments involve detecting an attack against a server and shifting the server behind a protected subnet. The other three sets of experiments, called Attacker Prevention experiments, involve detecting a malicious node in the internal network and initiating a dynamic de- fense to move the attacker behind a protected subnet. Each Attacker Prevention experiment utilizes a different dynamic defense to prevent the malicious node from attacking the rest of the Virtual Network. For each experiment, we run 6 different network attacks to validate the effectiveness of the dynamic defenses. The network attacks utilized for each experiment are ICMP Flooding, TCP Syn Flooding, Smurf attack, ARP Spoofing, DNS Spoofing, and NMAP Scanning. Our validation shows that our dynamic defenses, outside of the standard port group, are very effective in stopping each attack, consistently lowering the at- tacks’ success rate significantly. The Standard Port Group was the one dynamic defense that is ineffective, though there are also a couple of experiments that could benefit from being run with more attackers and with different situations to fully understand the effectiveness of the defenses. We believe that, as Virtual Networks become more common and utilized outside of data centers, the ability to dynamically shift topology can be used for network security purposes

Intrusion Detection and Countermeasure of Virtual Cloud Systems - State of the Art and Current Challenges

Clouds are distributed Internet-based platforms that provide highly resilient and scalable environments to be used by enterprises in a multitude of ways. Cloud computing offers enterprises technology innovation that business leaders and IT infrastructure managers can choose to apply based on how and to what extent it helps them fulfil their business requirements. It is crucial that all technical consultants have a rigorous understanding of the ramifications of cloud computing as its influence is likely to spread the complete IT landscape. Security is one of the major concerns that is of practical interest to decision makers when they are making critical strategic operational decisions. Distributed Denial of Service (DDoS) attacks are becoming more frequent and effective over the past few years, since the widely publicised DDoS attacks on the financial services industry that came to light in September and October 2012 and resurfaced in the past two years. In this paper, we introduce advanced cloud security technologies and practices as a series of concepts and technology architectures, from an industry-centric point of view. This is followed by classification of intrusion detection and prevention mechanisms that can be part of an overall strategy to help understand identify and mitigate potential DDoS attacks on business networks. The paper establishes solid coverage of security issues related to DDoS and virtualisation with a focus on structure, clarity, and well-defined blocks for mainstream cloud computing security solutions and platforms. In doing so, we aim to provide industry technologists, who may not be necessarily cloud or security experts, with an effective tool to help them understand the security implications associated with cloud adoption in their transition towards more knowledge-based systems

Enhancing the security of wireless sensor network based home automation systems

Home automation systems (HASs)seek to improve the quality of life for individuals through the automation of household devices. Recently, there has been a trend, in academia and industry, to research and develop low-cost Wireless Sensor Network (WSN) based HASs (Varchola et al. 2007). WSNs are designed to achieve a low-cost wireless networking solution, through the incorporation of limited processing, memory, and power resources. Consequently, providing secure and reliable remote access for resource limited WSNs, such as WSN based HASs, poses a significant challenge (Perrig et al. 2004). This thesis introduces the development of a hybrid communications approach to increase the resistance of WSN based HASs to remote DoS flooding attacks targeted against a third party. The approach is benchmarked against the dominant GHS remote access approach for WSN based HASs (Bergstrom et al. 2001), on a WSN based HAS test-bed, and shown to provide a minimum of a 58.28%, on average 59.85%, and a maximum of 61.45% increase in remote service availability during a DoS attack. Additionally, a virtual home incorporating a cryptographic based DoS detection algorithm, is developed to increase resistance to remote DoS flooding attacks targeted directly at WSN based HASs. The approach is benchmarked against D-WARD (Mirkovic 2003), the most effective DoS defence identified from the research, and shown to provide a minimum 84.70%, an average 91.13% and a maximum 95.6% reduction in packets loss on a WSN based HAS during a DoS flooding attack. Moreover, the approach is extended with the integration of a virtual home, hybrid communication approach, and a distributed denial of defence server to increase resistance to remote DoS attacks targeting the home gateway. The approach is again benchmarked against the D-WARD defence and shown to decrease the connection latency experienced by remote users by a minimum of 90.14%, an average 90.90%, and a maximum 91.88%.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics