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

Assessing Internet-wide Cyber Situational Awareness of Critical Sectors

In this short paper, we take a first step towards empirically assessing Internet-wide malicious activities generated from and targeted towards Internet-scale business sectors (i.e., financial, health, education, etc.) and critical infrastructure (i.e., utilities, manufacturing, government, etc.). Facilitated by an innovative and a collaborative large-scale effort, we have conducted discussions with numerous Internet entities to obtain rare and private information related to allocated IP blocks pertaining to the aforementioned sectors and critical infrastructure. To this end, we employ such information to attribute Internet-scale maliciousness to such sectors and realms, in an attempt to provide an in-depth analysis of the global cyber situational posture. We draw upon close to 16.8 TB of darknet data to infer probing activities (typically generated by malicious/infected hosts) and DDoS backscatter, from which we distill IP addresses of victims. By executing week-long measurements, we observed an alarming number of more than 11,000 probing machines and 300 DDoS attack victims hosted by critical sectors. We also generate rare insights related to the maliciousness of various business sectors, including financial, which typically do not report their hosted and targeted illicit activities for reputation-preservation purposes. While we treat the obtained results with strict confidence due to obvious sensitivity reasons, we postulate that such generated cyber threat intelligence could be shared with sector/critical infrastructure operators, backbone networks and Internet service providers to contribute to the overall threat remediation objective

Adversarial behaviours knowledge area

The technological advancements witnessed by our society in recent decades have brought improvements in our quality of life, but they have also created a number of opportunities for attackers to cause harm. Before the Internet revolution, most crime and malicious activity generally required a victim and a perpetrator to come into physical contact, and this limited the reach that malicious parties had. Technology has removed the need for physical contact to perform many types of crime, and now attackers can reach victims anywhere in the world, as long as they are connected to the Internet. This has revolutionised the characteristics of crime and warfare, allowing operations that would not have been possible before. In this document, we provide an overview of the malicious operations that are happening on the Internet today. We first provide a taxonomy of malicious activities based on the attacker’s motivations and capabilities, and then move on to the technological and human elements that adversaries require to run a successful operation. We then discuss a number of frameworks that have been proposed to model malicious operations. Since adversarial behaviours are not a purely technical topic, we draw from research in a number of fields (computer science, criminology, war studies). While doing this, we discuss how these frameworks can be used by researchers and practitioners to develop effective mitigations against malicious online operations.Published versio

A Macroscopic Study of Network Security Threats at the Organizational Level.

Defenders of today's network are confronted with a large number of malicious activities such as spam, malware, and denial-of-service attacks. Although many studies have been performed on how to mitigate security threats, the interaction between attackers and defenders is like a game of Whac-a-Mole, in which the security community is chasing after attackers rather than helping defenders to build systematic defensive solutions. As a complement to these studies that focus on attackers or end hosts, this thesis studies security threats from the perspective of the organization, the central authority that manages and defends a group of end hosts. This perspective provides a balanced position to understand security problems and to deploy and evaluate defensive solutions. This thesis explores how a macroscopic view of network security from an organization's perspective can be formed to help measure, understand, and mitigate security threats. To realize this goal, we bring together a broad collection of reputation blacklists. We first measure the properties of the malicious sources identified by these blacklists and their impact on an organization. We then aggregate the malicious sources to Internet organizations and characterize the maliciousness of organizations and their evolution over a period of two and half years. Next, we aim to understand the cause of different maliciousness levels in different organizations. By examining the relationship between eight security mismanagement symptoms and the maliciousness of organizations, we find a strong positive correlation between mismanagement and maliciousness. Lastly, motivated by the observation that there are organizations that have a significant fraction of their IP addresses involved in malicious activities, we evaluate the tradeoff of one type of mitigation solution at the organization level --- network takedowns.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116714/1/jingzj_1.pd

1000 days of UDP amplification DDoS attacks

Distributed Denial of Service (DDoS) attacks employing reflected UDP amplification are regularly used to disrupt networks and systems. The amplification allows one rented server to generate significant volumes of data, while the reflection hides the identity of the attacker. Consequently this is an attractive, low risk, strategy for criminals bent on vandalism and extortion. To measure the uptake of this strategy we analyse the results of running a network of honeypot UDP reflectors (median size 65 nodes) from July 2014 onwards. We explore the life cycle of attacks that use our reflectors, from the scanning phase used to detect our honeypot machines, through to their use in attacks. We see a median of 1450 malicious scanners per day across all UDP protocols, and have recorded details of 5.18 million subsequent attacks involving in excess of 3.31 trillion packets. Using a capture-recapture statistical technique, we estimate that our reflectors can see between 85.1% and 96.6% of UDP reflection attacks over our measurement period.We are extremely grateful to the organisations and individuals who have hosted Hopscotch nodes, and in particular the ShadowServer Foundation and Digital Ocean Inc. Daniel R. Thomas is supported by a grant from ThreatSTOP Inc. Richard Clayton is supported by the Department of Homeland Security (DHS) Science and Technology Directorate, Cyber Security Division (DHSS\&T/CSD) Broad Agency Announcement 11.02, the Government of Australia and SPAWAR Systems Center Pacific [contract number N66001-13-C-0131]; and the EPSRC [grant number EP/M020320/1]. Alastair R. Beresford is partly supported by the EPSRC [grant number EP/M020320/1]. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect those of any of the funders

Enlightening the Darknets: Augmenting Darknet Visibility with Active Probes

Darknets collect unsolicited traffic reaching unused address spaces. They provide insights into malicious activities, such as the rise of botnets and DDoS attacks. However, darknets provide a shallow view, as traffic is never responded. Here we quantify how their visibility increases by responding to traffic with interactive responders with increasing levels of interaction. We consider four deployments: Darknets, simple, vertical bound to specific ports, and, a honeypot that responds to all protocols on any port. We contrast these alternatives by analyzing the traffic attracted by each deployment and characterizing how traffic changes throughout the responder lifecycle on the darknet. We show that the deployment of responders increases the value of darknet data by revealing patterns that would otherwise be unobservable. We measure Side-Scan phenomena where once a host starts responding, it attracts traffic to other ports and neighboring addresses. uncovers attacks that darknets and would not observe, e.g. large-scale activity on non-standard ports. And we observe how quickly senders can identify and attack new responders. The “enlightened” part of a darknet brings several benefits and offers opportunities to increase the visibility of sender patterns. This information gain is worth taking advantage of, and we, therefore, recommend that organizations consider this option

More Amazon than Mafia: analysing a DDoS stresser service as organised cybercrime

The internet mafia trope has shaped our knowledge about organised crime groups online, yet the evidence is largely speculative and the logic often flawed. This paper adds to current knowledge by exploring the development, operation and demise of an online criminal group as a case study. In this article we analyse a DDoS (Distributed Denial of Service) stresser (also known as booter) which sells its services online to enable offenders to launch attacks. Using Social Network Analysis to explore the service operations and payment systems, our findings show a central business model that is similar to legitimate e-commerce websites in the way product, price and costumers are differentiated. It also illustrates that its organisation is distributed and not hierarchical and the overall income yield is comparatively low, requiring further organisational activity to make it pay. Finally, we show that the users of the service (mainly offenders) are not only a mixed group of actors, but that it is also possible to discriminate between different levels of seriousness of offending according to the particular service they purchased

Blocking DDoS attacks at the network level

Denial of service (DDoS) is a persistent and continuously growing problem. These attacks are based on methods that flood the victim with messages that it did not request, effectively exhausting its computational or bandwidth resources. The variety of attack approaches is overwhelming and the current defense mechanisms are not completely effective. In today’s internet, a multitude of DDoS attacks occur everyday, some even degrading the availability of critical or governmental services. In this dissertation, we propose a new network level DDoS mitigation protocol that iterates on previous attempts and uses proven mechanisms such as cryptographic challenges and packet-tagging. Our analysis of the previous attempts to solve this problem led to a ground-up design of the protocol with adaptability in mind, trying to minimize deployment and adoption barriers. With this work we concluded that with software changes only on the communication endpoints, it is possible to mitigate the most used DDoS attacks with results up to 25 times more favourable than standard resource rate limiting (RRL) methods

BGPeek-a-Boo: Active BGP-based Traceback for Amplification DDoS Attacks

Amplification DDoS attacks inherently rely on IP spoofing to steer attack traffic to the victim. At the same time, IP spoofing undermines prosecution, as the originating attack infrastructure remains hidden. Researchers have therefore proposed various mechanisms to trace back amplification attacks (or IP-spoofed attacks in general). However, existing traceback techniques require either the cooperation of external parties or a priori knowledge about the attacker. We propose BGPeek-a-Boo, a BGP-based approach to trace back amplification attacks to their origin network. BGPeek-a-Boo monitors amplification attacks with honeypots and uses BGP poisoning to temporarily shut down ingress traffic from selected Autonomous Systems. By systematically probing the entire AS space, we detect systems forwarding and originating spoofed traffic. We then show how a graph-based model of BGP route propagation can reduce the search space, resulting in a 5x median speed-up and over 20x for 1/4 of all cases. BGPeek-a-Boo achieves a unique traceback result 60% of the time in a simulation-based evaluation supported by real-world experiments