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

An Artificial Intelligence (AI) Framework for Detection of Distributed Reflection Denial of Service Attacks

In the contemporary digital world, cyber space is growing continuously witnessing amalgamation of different technologies associated with telecommunications, networking and sensing to mention few. This has enabled Service Oriented Architecture (SOA) to realize distributed applications that cater to the needs of enterprises in the real world. With the advantages of such environments, there has been increased number of instances of cyber-attacks. Distributed Denial of Service (DDoS) is the large-scale attack targeting critical digital infrastructure to make it useless for certain amount of time. Such attacks have several implications and lead to collapse of businesses unless there are countermeasures to detect it and handle it properly. Distributed Reflection Denial of Service (DRDoS) is a variant of such attacks which is more destructive in nature. It is more so in the presence of Internet of Things (IoT) devices deployed in cyber space in large scale. The existing DDoS countermeasures do not work to solve the problem of DRDoS directly. We propose an Artificial Intelligence (AI) framework for detection of DRDoS attacks. We propose an algorithm known as Machine Learning based DRDoS Attack Detection (ML-DAD) for effective detection of attacks. The prototype service built in Python monitors such attacks and take necessary steps to defeat it. The empirical results revealed that the proposed framework has superior performance improvement over the stat of the art. The research in this paper leads to new ideas in the area of detection and prevention of DRDoS attacks

Accountable infrastructure and its impact on internet security and privacy

The Internet infrastructure relies on the correct functioning of the basic underlying protocols, which were designed for functionality. Security and privacy have been added post hoc, mostly by applying cryptographic means to different layers of communication. In the absence of accountability, as a fundamental property, the Internet infrastructure does not have a built-in ability to associate an action with the responsible entity, neither to detect or prevent misbehavior. In this thesis, we study accountability from a few different perspectives. First, we study the need of having accountability in anonymous communication networks as a mechanism that provides repudiation for the proxy nodes by tracing back selected outbound traffic in a provable manner. Second, we design a framework that provides a foundation to support the enforcement of the right to be forgotten law in a scalable and automated manner. The framework provides a technical mean for the users to prove their eligibility for content removal from the search results. Third, we analyze the Internet infrastructure determining potential security risks and threats imposed by dependencies among the entities on the Internet. Finally, we evaluate the feasibility of using hop count filtering as a mechanism for mitigating Distributed Reflective Denial-of-Service attacks, and conceptually show that it cannot work to prevent these attacks.Die Internet-Infrastrutur stützt sich auf die korrekte Ausführung zugrundeliegender Protokolle, welche mit Fokus auf Funktionalität entwickelt wurden. Sicherheit und Datenschutz wurden nachträglich hinzugefügt, hauptsächlich durch die Anwendung kryptografischer Methoden in verschiedenen Schichten des Protokollstacks. Fehlende Zurechenbarkeit, eine fundamentale Eigenschaft Handlungen mit deren Verantwortlichen in Verbindung zu bringen, verhindert jedoch, Fehlverhalten zu erkennen und zu unterbinden. Diese Dissertation betrachtet die Zurechenbarkeit im Internet aus verschiedenen Blickwinkeln. Zuerst untersuchen wir die Notwendigkeit für Zurechenbarkeit in anonymisierten Kommunikationsnetzen um es Proxyknoten zu erlauben Fehlverhalten beweisbar auf den eigentlichen Verursacher zurückzuverfolgen. Zweitens entwerfen wir ein Framework, das die skalierbare und automatisierte Umsetzung des Rechts auf Vergessenwerden unterstützt. Unser Framework bietet Benutzern die technische Möglichkeit, ihre Berechtigung für die Entfernung von Suchergebnissen nachzuweisen. Drittens analysieren wir die Internet-Infrastruktur, um mögliche Sicherheitsrisiken und Bedrohungen aufgrund von Abhängigkeiten zwischen den verschiedenen beteiligten Entitäten zu bestimmen. Letztlich evaluieren wir die Umsetzbarkeit von Hop Count Filtering als ein Instrument DRDoS Angriffe abzuschwächen und wir zeigen, dass dieses Instrument diese Art der Angriffe konzeptionell nicht verhindern kann

DDoS Never Dies? An IXP Perspective on DDoS Amplification Attacks

DDoS attacks remain a major security threat to the continuous operation of Internet edge infrastructures, web services, and cloud platforms. While a large body of research focuses on DDoS detection and protection, to date we ultimately failed to eradicate DDoS altogether. Yet, the landscape of DDoS attack mechanisms is even evolving, demanding an updated perspective on DDoS attacks in the wild. In this paper, we identify up to 2608 DDoS amplification attacks at a single day by analyzing multiple Tbps of traffic flows at a major IXP with a rich ecosystem of different networks. We observe the prevalence of well-known amplification attack protocols (e.g., NTP, CLDAP), which should no longer exist given the established mitigation strategies. Nevertheless, they pose the largest fraction on DDoS amplification attacks within our observation and we witness the emergence of DDoS attacks using recently discovered amplification protocols (e.g., OpenVPN, ARMS, Ubiquity Discovery Protocol). By analyzing the impact of DDoS on core Internet infrastructure, we show that DDoS can overload backbone-capacity and that filtering approaches in prior work omit 97% of the attack traffic.Comment: To appear at PAM 202

Um novo método baseado no modelo de Potts para detecção de intrusão em rede

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2020.Sistemas de Detecção de Intrusão em Rede (NIDS, do inglês Network Intrusion Detec- tion Systems) desempenham um importante papel como ferramentas para identificação de potenciais ameaças a redes de computadores. No contexto de crescentes volumes de tráfego de internet em redes de computadores, NIDS baseados em fluxos constituem boas soluções para o monitoramento de tráfego em tempo real. Nos últimos anos, diferentes classificadores de tráfego baseados em fluxos foram propostos utilizando aprendizagem de máquina. Entretanto, algoritmos de aprendizagem de máquina possuem algumas lim- itações. Além de requerer grandes quantidades de exemplos categorizados, que podem ser difíceis de obter, a maioria desses algoritmos não consegue se adaptar bem a difer- entes domínios, i.e., após serem treinados em um conjunto de dados específico, não são facilmente generalizáveis para outros conjuntos de dados. Por fim, muitos dos modelos inferidos por esses algoritmos são não interpretáveis. Para contornar essas limitações, é proposto um novo classificador de fluxos, chamado Energy-based Flow Classifier (EFC). EFC é um classificador baseado em anomalias que utiliza estatística inversa para inferir um modelo estatístico utilizando apenas exemplos benignos. É mostrado que o EFC é ca- paz de realizar classificação de fluxos de forma precisa e é mais adaptável a novos domínios do que algoritmos clássicos baseados em aprendizagem de máquina. Dados os bons resul- tados obtidos considerando três conjuntos de dados diferentes (CIDDS-001, CICIDS17 e CICDDoS19), o EFC se mostra como um algoritmo promissor para classificação robusta de fluxos de rede.Network Intrusion Detection Systems (NIDS) play an important role as tools for identify- ing potential network threats. In the context of ever-increasing traffic volume on computer networks, flow-based NIDS arise as good solutions for real-time traffic classification. In recent years, different flow-based classifiers have been proposed using machine learning algorithms. Nevertheless, the classical machine learning algorithms have some limita- tions. For instance, they require large amounts of labeled data, which might be difficult to obtain. Additionally, most machine learning algorithms are not capable of domain adaptation, i.e., after being trained on a specific dataset, they are not general enough to be applied to other related data distributions. And, finally, many of the models inferred by this algorithms are uninterpretable. To overcome these limitations, we propose a new flow-based classifier, called Energy-based Flow Classifier (EFC). This anomaly-based clas- sifier uses inverse statistics to infer a statistical model based on labeled benign examples. We show that EFC is capable of accurately performing a one-class flow classification and is more adaptable to new domains than classical machine learning algorithms. Given the positive results obtained on three different datasets (CIDDS-001, CICIDS17 and CICD- DoS19), we consider EFC to be a promising algorithm to perform robust flow-based traffic classification

Analysis of bandwidth attacks in a bittorrent swarm

The beginning of the 21st century saw a widely publicized lawsuit against Napster. This was the first Peer-to-Peer software that allowed its users to search for and share digital music with other users. At the height of its popularity, Napster boasted 80 million registered users. This marked the beginning of a Peer-to-Peer paradigm and the end of older methods of distributing cultural possessions. But Napster was not entirely rooted in a Peer-to-Peer paradigm. Only the download of a file was based on Peer-to-Peer interactions; the search process was still based on a central server. It was thus easy to shutdown Napster. Shortly after the shutdown, Bram Cohen developed a new Peer-to-Peer protocol called BitTorrent. The main principle behind BitTorrent is an incentive mechanism, called a choking algorithm, which rewards peers that share. Currently, BitTorrent is one of the most widely used protocols on the Internet. Therefore, it is important to investigate the security of this protocol. While significant progress has been made in understanding the Bit- Torrent choking mechanism, its security vulnerabilities have not yet been thoroughly investigated. This dissertation provides a security analysis of the Peer-to-Peer protocol BitTorrent on the application and transport layer. The dissertation begins with an experimental analysis of bandwidth attacks against different choking algorithms in the BitTorrent seed state. I reveal a simple exploit that allows malicious peers to receive a considerably higher download rate than contributing leechers, thereby causing a significant loss of efficiency for benign peers. I show the damage caused by the proposed attack in two different environments—a lab testbed comprised of 32 peers and a global testbed called PlanetLab with 300 peers. Our results show that three malicious peers can degrade the download rate by up to 414.99 % for all peers. Combined with a Sybil attack with as many attackers as leechers, it is possible to degrade the download rate by more than 1000 %. I propose a novel choking algorithm which is immune against bandwidth attacks and a countermeasure against the revealed attack. This thesis includes a security analysis of the transport layer. To make BitTorrent more Internet Service Provider friendly, BitTorrent Inc. invented the Micro Transport Protocol. It is based on User Datagram Protocol with a novel congestion control called Low Extra Delay Background Transport. This protocol assumes that the receiver always provides correct feedback, otherwise this deteriorates throughput or yields to corrupted data. I show through experimental evaluation, that a misbehaving Micro Transport Protocol receiver which is not interested in data integrity, can increase the bandwidth of the sender by up to five times. This can cause a congestion collapse and steal a large share of a victim’s bandwidth. I present three attacks, which increase bandwidth usage significantly. I have tested these attacks in real world environments and demonstrate their severity both in terms of the number of packets and total traffic generated. I also present a countermeasure for protecting against these attacks and evaluate the performance of this defensive strategy. In the last section, I demonstrate that the BitTorrent protocol family is vulnerable to Distributed Reflective Denial-of-Service attacks. Specifically, I show that an attacker can exploit BitTorrent protocols (Micro Transport Protocol, Distributed Hash Table, Message Stream Encryption and BitTorrent Sync to reflect and amplify traffic from Bit- Torrent peers to any target on the Internet. I validate the efficiency, robustness, and the difficulty of defence of the exposed BitTorrent vulnerabilities in a Peer-to-Peer lab testbed. I further substantiate lab results by crawling more than 2.1 million IP addresses over Mainline Distributed Hash Table and analyzing more than 10,000 BitTorrent handshakes. The experiments suggest that an attacker is able to exploit BitTorrent peers to amplify traffic by a factor of 50, and in the case of BitTorrent Sync 120. Additionally, I observe that the most popular BitTorrent clients are the most vulnerable ones

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

A self-healing framework to combat cyber attacks. Analysis and development of a self-healing mitigation framework against controlled malware attacks for enterprise networks.

Cybercrime costs a total loss of about $338 billion annually which makes it one of the most profitable criminal activities in the world. Controlled malware (Botnet) is one of the most prominent tools used by cybercriminals to infect, compromise computer networks and steal important information. Infecting a computer is relatively easy nowadays with malware that propagates through social networking in addition to the traditional methods like SPAM messages and email attachments. In fact, more than 1/4 of all computers in the world are infected by malware which makes them viable for botnet use. This thesis proposes, implements and presents the Self-healing framework that takes inspiration from the human immune system. The designed self-healing framework utilises the key characteristics and attributes of the nature’s immune system to reverse botnet infections. It employs its main components to heal the infected nodes. If the healing process was not successful for any reason, it immediately removes the infected node from the Enterprise’s network to a quarantined network to avoid any further botnet propagation and alert the Administrators for human intervention. The designed self-healing framework was tested and validated using different experiments and the results show that it efficiently heals the infected workstations in an Enterprise network

An OSINT Approach to Automated Asset Discovery and Monitoring

The main objective of this thesis is to improve the efficiency of security operations centersthrough the articulation of different publicly open sources of security related feeds. This ischallenging because of the different abstraction models of the feeds that need to be madecompatible, of the range of control values that each data source can have and that will impactthe security events, and of the scalability of computational and networking resources that arerequired to collect security events.Following the industry standards proposed by the literature (OSCP guide, PTES andOWASP), the detection of hosts and sub-domains using an articulation of several sources isregarded as the first interaction in an engagement. This first interaction often misses somesources that could allow the disclosure of more assets. This became important since networkshave scaled up to the cloud, where IP address range is not owned by the company, andimportant applications are often shared within the same IP, like the example of Virtual Hoststo host several application in the same server.We will focus on the first step of any engagement, the enumeration of the target network.Attackers often use several techniques to enumerate the target to discover vulnerable services.This enumeration could be improved by the addition of several other sources and techniquesthat are often left aside from the literature. Also, by creating an automated process it ispossible for security operation centers to discover these assets and map the applicationsin use to keep track of said vulnerabilities using OSINT techniques and publicly availablesolutions, before the attackers try to exploit the service. This gives a vision of the Internetfacing services often seen by attackers without querying the service directly evading thereforedetection. This research is in frame with the complete engagement process and should beintegrate in already built solutions, therefore the results should be able to connect to additionalapplications in order to reach forward in the engagement process.By addressing these challenges we expect to come in great aid of sysadmin and securityteams, helping them with the task of securing their assets and ensuring security cleanlinessof the enterprise resulting in a better policy compliance without ever connecting to the clienthosts

Fine-grained, Content-agnostic Network Traffic Analysis for Malicious Activity Detection

The rapid evolution of malicious activities in network environments necessitates the development of more effective and efficient detection and mitigation techniques. Traditional traffic analysis (TA) approaches have demonstrated limited efficacy and performance in detecting various malicious activities, resulting in a pressing need for more advanced solutions. To fill the gap, this dissertation proposes several new fine-grained network traffic analysis (FGTA) approaches. These approaches focus on (1) detecting previously hard-to-detect malicious activities by deducing fine-grained, detailed application-layer information in privacy-preserving manners, (2) enhancing usability by providing more explainable results and better adaptability to different network environments, and (3) combining network traffic data with endpoint information to provide users with more comprehensive and accurate protections. We begin by conducting a comprehensive survey of existing FGTA approaches. We then propose CJ-Sniffer, a privacy-aware cryptojacking detection system that efficiently detects cryptojacking traffic. CJ-Sniffer is the first approach to distinguishing cryptojacking traffic from user-initiated cryptocurrency mining traffic, allowing for fine-grained traffic discrimination. This level of fine-grained traffic discrimination has proven challenging to accomplish through traditional TA methodologies. Next, we introduce BotFlowMon, a learning-based, content-agnostic approach for detecting online social network (OSN) bot traffic, which has posed a significant challenge for detection using traditional TA strategies. BotFlowMon is an FGTA approach that relies only on content-agnostic flow-level data as input and utilizes novel algorithms and techniques to classify social bot traffic from real OSN user traffic. To enhance the usability of FGTA-based attack detection, we propose a learning-based DDoS detection approach that emphasizes both explainability and adaptability. This approach provides network administrators with insightful explanatory information and adaptable models for new network environments. Finally, we present a reinforcement learning-based defense approach against L7 DDoS attacks, which combines network traffic data with endpoint information to operate. The proposed approach actively monitors and analyzes the victim server and applies different strategies under different conditions to protect the server while minimizing collateral damage to legitimate requests. Our evaluation results demonstrate that the proposed approaches achieve high accuracy and efficiency in detecting and mitigating various malicious activities, while maintaining privacy-preserving features, providing explainable and adaptable results, or providing comprehensive application-layer situational awareness. This dissertation significantly advances the fields of FGTA and malicious activity detection. This dissertation includes published and unpublished co-authored materials