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

Forensic Evidence Identification and Modeling for Attacks against a Simulated Online Business Information System

Forensic readiness of business information systems can support future forensics investigation or auditing on external/internal attacks, internal sabotage and espionage, and business fraud. To establish forensics readiness, it is essential for an organization to identify which fingerprints are relevant and where they can be located, to determine whether they are logged in a forensically sound way and whether all the needed fingerprints are available to reconstruct the events successfully. Also, a fingerprint identification and locating mechanism should be provided to guide potential forensics investigation in the future. Furthermore, mechanisms should be established to automate the security incident tracking and reconstruction processes. In this research, external and internal attacks are first modeled as augmented attack trees based on the vulnerabilities of business information systems. Then, modeled attacks are conducted against a honeynet that simulates an online business information system, and a forensic investigation follows each attack. Finally, an evidence tree, which is expected to provide the necessary contextual information to automate the attack tracking and reconstruction process in the future, is built for each attack based on fingerprints identified and located within the system

Mitigation of DDoS Attack using a Probabilistic Approach & End System based Strategy

From the very begining of Internet Technology, the attacks are the undetatchable element of it. This Distributed Denial-of-Service attack is very much harmful as our systems are vulnerable to this. Many mitigation strategies were proposed but these all need the help of network services for mitigation. Also these need the administrative support to continue the mitigation technique beyond own network if the attacker is outsider of own network. But it is very hard to meet all these constraint at a time. That's why we need the help of "end system" based mitigation strategy. Very rare researches have been carried out on this type of method. In this thesis, we have followed the end system based mitigation strategy to solve the problem of DDoS attack. Here neither we need the support of network services nor any administration facility. Here we proposed one probabilistic approach to find out the number of packets being malicious among the massive number of packets. Also we have proposed one algorithm to mitigate the attack based on the number of packets being malicious. We also analyzed our approach in a simulation environment against one existing end system based mitigation strategy. The result shows that our approach solves the problem DDoS and saves the computational resource in terms of CPU time

Amber : a aero-interaction honeypot with distributed intelligence

For the greater part, security controls are based on the principle of Decision through Detection (DtD). The exception to this is a honeypot, which analyses interactions between a third party and itself, while occupying a piece of unused information space. As honeypots are not located on productive information resources, any interaction with it can be assumed to be non-productive. This allows the honeypot to make decisions based simply on the presence of data, rather than on the behaviour of the data. But due to limited resources in human capital, honeypots’ uptake in the South African market has been underwhelming. Amber attempts to change this by offering a zero-interaction security system, which will use the honeypot approach of decision through Presence (DtP) to generate a blacklist of third parties, which can be passed on to a network enforcer. Empirical testing has proved the usefulness of this alternative and low cost approach in defending networks. The functionality of the system was also extended by installing nodes in different geographical locations, and streaming their detections into the central Amber hive

A survey of denial-of-service and distributed denial of service attacks and defenses in cloud computing

Cloud Computing is a computingmodel that allows ubiquitous, convenient and on-demand access to a shared pool of highly configurable resources (e.g., networks, servers, storage, applications and services). Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are serious threats to the Cloud services’ availability due to numerous new vulnerabilities introduced by the nature of the Cloud, such as multi-tenancy and resource sharing. In this paper, new types of DoS and DDoS attacks in Cloud Computing are explored, especially the XML-DoS and HTTP-DoS attacks, and some possible detection and mitigation techniques are examined. This survey also provides an overview of the existing defense solutions and investigates the experiments and metrics that are usually designed and used to evaluate their performance, which is helpful for the future research in the domain

Adaptive Response System for Distributed Denial-of-Service Attacks

The continued prevalence and severe damaging effects of the Distributed Denial of Service (DDoS) attacks in today’s Internet raise growing security concerns and call for an immediate response to come up with better solutions to tackle DDoS attacks. The current DDoS prevention mechanisms are usually inflexible and determined attackers with knowledge of these mechanisms, could work around them. Most existing detection and response mechanisms are standalone systems which do not rely on adaptive updates to mitigate attacks. As different responses vary in their “leniency” in treating detected attack traffic, there is a need for an Adaptive Response System. We designed and implemented our DDoS Adaptive ResponsE (DARE) System, which is a distributed DDoS mitigation system capable of executing appropriate detection and mitigation responses automatically and adaptively according to the attacks. It supports easy integrations for both signature-based and anomaly-based detection modules. Additionally, the design of DARE’s individual components takes into consideration the strengths and weaknesses of existing defence mechanisms, and the characteristics and possible future mutations of DDoS attacks. These components consist of an Enhanced TCP SYN Attack Detector and Bloom-based Filter, a DDoS Flooding Attack Detector and Flow Identifier, and a Non Intrusive IP Traceback mechanism. The components work together interactively to adapt the detections and responses in accordance to the attack types. Experiments conducted on DARE show that the attack detection and mitigation are successfully completed within seconds, with about 60% to 86% of the attack traffic being dropped, while availability for legitimate and new legitimate requests is maintained. DARE is able to detect and trigger appropriate responses in accordance to the attacks being launched with high accuracy, effectiveness and efficiency. We also designed and implemented a Traffic Redirection Attack Protection System (TRAPS), a stand-alone DDoS attack detection and mitigation system for IPv6 networks. In TRAPS, the victim under attack verifies the authenticity of the source by performing virtual relocations to differentiate the legitimate traffic from the attack traffic. TRAPS requires minimal deployment effort and does not require modifications to the Internet infrastructure due to its incorporation of the Mobile IPv6 protocol. Experiments to test the feasibility of TRAPS were carried out in a testbed environment to verify that it would work with the existing Mobile IPv6 implementation. It was observed that the operations of each module were functioning correctly and TRAPS was able to successfully mitigate an attack launched with spoofed source IP addresses