Scalable schemes against Distributed Denial of Service attacks

Defense against Distributed Denial of Service (DDoS) attacks is one of the primary concerns on the Internet today. DDoS attacks are difficult to prevent because of the open, interconnected nature of the Internet and its underlying protocols, which can be used in several ways to deny service. Attackers hide their identity by using third parties such as private chat channels on IRC (Internet Relay Chat). They also insert false return IP address, spoofing, in a packet which makes it difficult for the victim to determine the packet\u27s origin. We propose three novel and realistic traceback mechanisms which offer many advantages over the existing schemes. All the three schemes take advantage of the Autonomous System topology and consider the fact that the attacker\u27s packets may traverse through a number of domains under different administrative control. Most of the traceback mechanisms make wrong assumptions that the network details of a company under an administrative control are disclosed to the public. For security reasons, this is not the case most of the times. The proposed schemes overcome this drawback by considering reconstruction at the inter and intra AS levels. Hierarchical Internet Traceback (HIT) and Simple Traceback Mechanism (STM) trace back to an attacker in two phases. In the first phase the attack originating Autonomous System is identified while in the second phase the attacker within an AS is identified. Both the schemes, HIT and STM, allow the victim to trace back to the attackers in a few seconds. Their computational overhead is very low and they scale to large distributed attacks with thousands of attackers. Fast Autonomous System Traceback allows complete attack path reconstruction with few packets. We use traceroute maps of real Internet topologies CAIDA\u27s skitter to simulate DDoS attacks and validate our design

Traffic Monitoring and analysis for source identification

Ph.DDOCTOR OF PHILOSOPH

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

The User Attribution Problem and the Challenge of Persistent Surveillance of User Activity in Complex Networks

In the context of telecommunication networks, the user attribution problem refers to the challenge faced in recognizing communication traffic as belonging to a given user when information needed to identify the user is missing. This is analogous to trying to recognize a nameless face in a crowd. This problem worsens as users move across many mobile networks (complex networks) owned and operated by different providers. The traditional approach of using the source IP address, which indicates where a packet comes from, does not work when used to identify mobile users. Recent efforts to address this problem by exclusively relying on web browsing behavior to identify users were limited to a small number of users (28 and 100 users). This was due to the inability of solutions to link up multiple user sessions together when they rely exclusively on the web sites visited by the user. This study has tackled this problem by utilizing behavior based identification while accounting for time and the sequential order of web visits by a user. Hierarchical Temporal Memories (HTM) were used to classify historical navigational patterns for different users. Each layer of an HTM contains variable order Markov chains of connected nodes which represent clusters of web sites visited in time order by the user (user sessions). HTM layers enable inference generalization by linking Markov chains within and across layers and thus allow matching longer sequences of visited web sites (multiple user sessions). This approach enables linking multiple user sessions together without the need for a tracking identifier such as the source IP address. Results are promising. HTMs can provide high levels of accuracy using synthetic data with 99% recall accuracy for up to 500 users and good levels of recall accuracy of 95 % and 87% for 5 and 10 users respectively when using cellular network data. This research confirmed that the presence of long tail web sites (rarely visited) among many repeated destinations can create unique differentiation. What was not anticipated prior to this research was the very high degree of repetitiveness of some web destinations found in real network data

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