Towards IP traceback based defense against DDoS attacks.

Lau Nga Sin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 101-110).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Research Motivation --- p.2Chapter 1.2 --- Problem Statement --- p.3Chapter 1.3 --- Research Objectives --- p.4Chapter 1.4 --- Structure of the Thesis --- p.6Chapter 2 --- Background Study on DDoS Attacks --- p.8Chapter 2.1 --- Distributed Denial of Service Attacks --- p.8Chapter 2.1.1 --- DDoS Attack Architecture --- p.9Chapter 2.1.2 --- DDoS Attack Taxonomy --- p.11Chapter 2.1.3 --- DDoS Tools --- p.19Chapter 2.1.4 --- DDoS Detection --- p.21Chapter 2.2 --- DDoS Countermeasure: Attack Source Traceback --- p.23Chapter 2.2.1 --- Link Testing --- p.23Chapter 2.2.2 --- Logging --- p.24Chapter 2.2.3 --- ICMP-based traceback --- p.26Chapter 2.2.4 --- Packet marking --- p.28Chapter 2.2.5 --- Comparison of various IP Traceback Schemes --- p.31Chapter 2.3 --- DDoS Countermeasure: Packet Filtering --- p.33Chapter 2.3.1 --- Ingress Filtering --- p.33Chapter 2.3.2 --- Egress Filtering --- p.34Chapter 2.3.3 --- Route-based Packet Filtering --- p.35Chapter 2.3.4 --- IP Traceback-based Packet Filtering --- p.36Chapter 2.3.5 --- Router-based Pushback --- p.37Chapter 3 --- Domain-based IP Traceback Scheme --- p.40Chapter 3.1 --- Overview of our IP Traceback Scheme --- p.41Chapter 3.2 --- Assumptions --- p.44Chapter 3.3 --- Proposed Packet Marking Scheme --- p.45Chapter 3.3.1 --- IP Markings with Edge Sampling --- p.46Chapter 3.3.2 --- Domain-based Design Motivation --- p.48Chapter 3.3.3 --- Mathematical Principle --- p.49Chapter 3.3.4 --- Marking Mechanism --- p.51Chapter 3.3.5 --- Storage Space of the Marking Fields --- p.56Chapter 3.3.6 --- Packet Marking Integrity --- p.57Chapter 3.3.7 --- Path Reconstruction --- p.58Chapter 4 --- Route-based Packet Filtering Scheme --- p.62Chapter 4.1 --- Placement of Filters --- p.63Chapter 4.1.1 --- At Sources' Networks --- p.64Chapter 4.1.2 --- At Victim's Network --- p.64Chapter 4.2 --- Proposed Packet Filtering Scheme --- p.65Chapter 4.2.1 --- Classification of Packets --- p.66Chapter 4.2.2 --- Filtering Mechanism --- p.67Chapter 5 --- Performance Evaluation --- p.70Chapter 5.1 --- Simulation Setup --- p.70Chapter 5.2 --- Experiments on IP Traceback Scheme --- p.72Chapter 5.2.1 --- Performance Metrics --- p.72Chapter 5.2.2 --- Choice of Marking Probabilities --- p.73Chapter 5.2.3 --- Experimental Results --- p.75Chapter 5.3 --- Experiments on Packet Filtering Scheme --- p.82Chapter 5.3.1 --- Performance Metrics --- p.82Chapter 5.3.2 --- Choices of Filtering Probabilities --- p.84Chapter 5.3.3 --- Experimental Results --- p.85Chapter 5.4 --- Deployment Issues --- p.91Chapter 5.4.1 --- Backward Compatibility --- p.91Chapter 5.4.2 --- Processing Overheads to the Routers and Network --- p.93Chapter 5.5 --- Evaluations --- p.95Chapter 6 --- Conclusion --- p.96Chapter 6.1 --- Contributions --- p.96Chapter 6.2 --- Discussions and future work --- p.99Bibliography --- p.11

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

Wide spectrum attribution: Using deception for attribution intelligence in cyber attacks

Modern cyber attacks have evolved considerably. The skill level required to conduct a cyber attack is low. Computing power is cheap, targets are diverse and plentiful. Point-and-click crimeware kits are widely circulated in the underground economy, while source code for sophisticated malware such as Stuxnet is available for all to download and repurpose. Despite decades of research into defensive techniques, such as firewalls, intrusion detection systems, anti-virus, code auditing, etc, the quantity of successful cyber attacks continues to increase, as does the number of vulnerabilities identified. Measures to identify perpetrators, known as attribution, have existed for as long as there have been cyber attacks. The most actively researched technical attribution techniques involve the marking and logging of network packets. These techniques are performed by network devices along the packet journey, which most often requires modification of existing router hardware and/or software, or the inclusion of additional devices. These modifications require wide-scale infrastructure changes that are not only complex and costly, but invoke legal, ethical and governance issues. The usefulness of these techniques is also often questioned, as attack actors use multiple stepping stones, often innocent systems that have been compromised, to mask the true source. As such, this thesis identifies that no publicly known previous work has been deployed on a wide-scale basis in the Internet infrastructure. This research investigates the use of an often overlooked tool for attribution: cyber de- ception. The main contribution of this work is a significant advancement in the field of deception and honeypots as technical attribution techniques. Specifically, the design and implementation of two novel honeypot approaches; i) Deception Inside Credential Engine (DICE), that uses policy and honeytokens to identify adversaries returning from different origins and ii) Adaptive Honeynet Framework (AHFW), an introspection and adaptive honeynet framework that uses actor-dependent triggers to modify the honeynet envi- ronment, to engage the adversary, increasing the quantity and diversity of interactions. The two approaches are based on a systematic review of the technical attribution litera- ture that was used to derive a set of requirements for honeypots as technical attribution techniques. Both approaches lead the way for further research in this field

IP traceback marking scheme based DDoS defense.

Ping Yan.Thesis submitted in: December 2004.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 93-100).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- INTRODUCTION --- p.1Chapter 1.1 --- The Problem --- p.1Chapter 1.2 --- Research Motivations and Objectives --- p.3Chapter 1.3 --- The Rationale --- p.8Chapter 1.4 --- Thesis Organization --- p.9Chapter 2 --- BACKGROUND STUDY --- p.10Chapter 2.1 --- Distributed Denial of Service Attacks --- p.10Chapter 2.1.1 --- Taxonomy of DoS and DDoS Attacks --- p.13Chapter 2.2 --- IP Traceback --- p.17Chapter 2.2.1 --- Assumptions --- p.18Chapter 2.2.2 --- Problem Model and Performance Metrics --- p.20Chapter 2.3 --- IP Traceback Proposals --- p.24Chapter 2.3.1 --- Probabilistic Packet Marking (PPM) --- p.24Chapter 2.3.2 --- ICMP Traceback Messaging --- p.26Chapter 2.3.3 --- Logging --- p.27Chapter 2.3.4 --- Tracing Hop-by-hop --- p.29Chapter 2.3.5 --- Controlled Flooding --- p.30Chapter 2.4 --- DDoS Attack Countermeasures --- p.30Chapter 2.4.1 --- Ingress/Egress Filtering --- p.33Chapter 2.4.2 --- Route-based Distributed Packet Filtering (DPF) --- p.34Chapter 2.4.3 --- IP Traceback Based Intelligent Packet Filtering --- p.35Chapter 2.4.4 --- Source-end DDoS Attack Recognition and Defense --- p.36Chapter 2.4.5 --- Classification of DDoS Defense Methods --- p.38Chapter 3 --- ADAPTIVE PACKET MARKING SCHEME --- p.41Chapter 3.1 --- Scheme Overview --- p.41Chapter 3.2 --- Adaptive Packet Marking Scheme --- p.44Chapter 3.2.1 --- Design Motivation --- p.44Chapter 3.2.2 --- Marking Algorithm Basics --- p.46Chapter 3.2.3 --- Domain id Marking --- p.49Chapter 3.2.4 --- Router id Marking --- p.51Chapter 3.2.5 --- Attack Graph Reconstruction --- p.53Chapter 3.2.6 --- IP Header Overloading --- p.56Chapter 3.3 --- Experiments on the Packet Marking Scheme --- p.59Chapter 3.3.1 --- Simulation Set-up --- p.59Chapter 3.3.2 --- Experimental Results and Analysis --- p.61Chapter 4 --- DDoS DEFENSE SCHEMES --- p.67Chapter 4.1 --- Scheme I: Packet Filtering at Victim-end --- p.68Chapter 4.1.1 --- Packet Marking Scheme Modification --- p.68Chapter 4.1.2 --- Packet Filtering Algorithm --- p.69Chapter 4.1.3 --- Determining the Filtering Probabilities --- p.70Chapter 4.1.4 --- Suppressing Packets Filtering with did Markings from Nearby Routers --- p.73Chapter 4.2 --- Scheme II: Rate Limiting at the Sources --- p.73Chapter 4.2.1 --- Algorithm of the Rate-limiting Scheme --- p.74Chapter 4.3 --- Performance Measurements for Scheme I & Scheme II . --- p.77Chapter 5 --- CONCLUSION --- p.87Chapter 5.1 --- Contributions --- p.87Chapter 5.2 --- Discussion and Future Work --- p.91Bibliography --- p.10

Affecting IP traceback with recent Internet topology maps

Computer network attacks are on the increase and are more sophisticated in today\u27s network environment than ever before. One step in tackling the increasing spate of attacks is the availability of a system that can trace attack packets back to their original sources irrespective of invalid or manipulated source addresses. IP Traceback is one of such methods, and several schemes have already been proposed in this area. Notably though, no traceback scheme is in wide use today due to reasons including a lack of compatibility with existing network protocols and infrastructure, as well as the high costs of deployment. Recently, remarkable progress has been made in the area of Internet topology mappings and more detailed and useful maps and metrics of the Internet are being made available to the corporate and academic research communities. This thesis introduces a novel use of these maps to influence IP Traceback in general, and packet marking schemes in particular. We note that while other schemes have previously taken advantage of such maps, most of these have viewed the maps from the available router node level. We take a novel router-aggregation node view of the Internet and explore ways to use this to make improvements to packet marking schemes and solving the problem of the limited space available in the current IP header for marking purposes. We evaluate our proposed schemes using real network paths traversed by several traceroute packets from diverse sources and to various destinations, and compare our results to other packet marking schemes. Finally, we explore the possibility of partial deployment of one of our schemes and estimate the probability of success at different stages of deployment

Traffic Monitoring and analysis for source identification

Ph.DDOCTOR OF PHILOSOPH

A Defense Framework Against Denial-of-Service in Computer Networks

Denial-of-Service (DoS) is a computer security problem that poses a serious challenge totrustworthiness of services deployed over computer networks. The aim of DoS attacks isto make services unavailable to legitimate users, and current network architectures alloweasy-to-launch, hard-to-stop DoS attacks. Particularly challenging are the service-level DoSattacks, whereby the victim service is flooded with legitimate-like requests, and the jammingattack, in which wireless communication is blocked by malicious radio interference. Theseattacks are overwhelming even for massively-resourced services, and effective and efficientdefenses are highly needed. This work contributes a novel defense framework, which I call dodging, against service-level DoS and wireless jamming. Dodging has two components: (1) the careful assignment ofservers to clients to achieve accurate and quick identification of service-level DoS attackersand (2) the continuous and unpredictable-to-attackers reconfiguration of the client-serverassignment and the radio-channel mapping to withstand service-level and jamming DoSattacks. Dodging creates hard-to-evade baits, or traps, and dilutes the attack "fire power".The traps identify the attackers when they violate the mapping function and even when theyattack while correctly following the mapping function. Moreover, dodging keeps attackers"in the dark", trying to follow the unpredictably changing mapping. They may hit a fewtimes but lose "precious" time before they are identified and stopped. Three dodging-based DoS defense algorithms are developed in this work. They are moreresource-efficient than state-of-the-art DoS detection and mitigation techniques. Honeybees combines channel hopping and error-correcting codes to achieve bandwidth-efficientand energy-efficient mitigation of jamming in multi-radio networks. In roaming honeypots, dodging enables the camouflaging of honeypots, or trap machines, as real servers,making it hard for attackers to locate and avoid the traps. Furthermore, shuffling requestsover servers opens up windows of opportunity, during which legitimate requests are serviced.Live baiting, efficiently identifies service-level DoS attackers by employing results fromthe group-testing theory, discovering defective members in a population using the minimumnumber of tests. The cost and benefit of the dodging algorithms are analyzed theoretically,in simulation, and using prototype experiments

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Message traceback systems dancing with the devil

The research community has produced a great deal of work in recent years in the areas of IP, layer 2 and connection-chain traceback. We collectively designate these as message traceback systems which, invariably aim to locate the origin of network data, in spite of any alterations effected to that data (whether legitimately or fraudulently). This thesis provides a unifying definition of spoofing and a classification based on this which aims to encompass all streams of message traceback research. The feasibility of this classification is established through its application to our literature review of the numerous known message traceback systems. We propose two layer 2 (L2) traceback systems, switch-SPIE and COTraSE, which adopt different approaches to logging based L2 traceback for switched ethernet. Whilst message traceback in spite of spoofing is interesting and perhaps more challenging than at first seems, one might say that it is rather academic. Logging of network data is a controversial and unpopular notion and network administrators don't want the added installation and maintenance costs. However, European Parliament Directive 2006/24/EC requires that providers of publicly available electronic communications networks retain data in a form similar to mobile telephony call records, from April 2009 and for periods of up to 2 years. This thesis identifies the relevance of work in all areas of message traceback to the European data retention legislation. In the final part of this thesis we apply our experiences with L2 traceback, together with our definitions and classification of spoofing to discuss the issues that EU data retention implementations should consider. It is possible to 'do logging right' and even safeguard user privacy. However this can only occur if we fully understand the technical challenges, requiring much further work in all areas of logging based, message traceback systems. We have no choice but to dance with the devil.EThOS - Electronic Theses Online ServiceGBUnited Kingdo