A Holistic Approach to Service Survivability

We present SABER (Survivability Architecture: Block, Evade, React), a proposed survivability architecture that blocks, evades and reacts to a variety of attacks by using several security and survivability mechanisms in an automated and coordinated fashion. Contrary to the ad hoc manner in which contemporary survivable systems are built--using isolated, independent security mechanisms such as firewalls, intrusion detection systems and software sandboxes--SABER integrates several different technologies in an attempt to provide a unified framework for responding to the wide range of attacks malicious insiders and outsiders can launch. This coordinated multi-layer approach will be capable of defending against attacks targeted at various levels of the network stack, such as congestion-based DoS attacks, software-based DoS or code-injection attacks, and others. Our fundamental insight is that while multiple lines of defense are useful, most conventional, uncoordinated approaches fail to exploit the full range of available responses to incidents. By coordinating the response, the ability to survive even in the face of successful security breaches increases substantially. We discuss the key components of SABER, how they will be integrated together, and how we can leverage on the promising results of the individual components to improve survivability in a variety of coordinated attack scenarios. SABER is currently in the prototyping stages, with several interesting open research topics

MFIRE-2: A Multi Agent System for Flow-based Intrusion Detection Using Stochastic Search

Detecting attacks targeted against military and commercial computer networks is a crucial element in the domain of cyberwarfare. The traditional method of signature-based intrusion detection is a primary mechanism to alert administrators to malicious activity. However, signature-based methods are not capable of detecting new or novel attacks. This research continues the development of a novel simulated, multiagent, flow-based intrusion detection system called MFIRE. Agents in the network are trained to recognize common attacks, and they share data with other agents to improve the overall effectiveness of the system. A Support Vector Machine (SVM) is the primary classifier with which agents determine an attack is occurring. Agents are prompted to move to different locations within the network to find better vantage points, and two methods for achieving this are developed. One uses a centralized reputation-based model, and the other uses a decentralized model optimized with stochastic search. The latter is tested for basic functionality. The reputation model is extensively tested in two configurations and results show that it is significantly superior to a system with non-moving agents. The resulting system, MFIRE-2, demonstrates exciting new network defense capabilities, and should be considered for implementation in future cyberwarfare applications

A Multi Agent System for Flow-Based Intrusion Detection

The detection and elimination of threats to cyber security is essential for system functionality, protection of valuable information, and preventing costly destruction of assets. This thesis presents a Mobile Multi-Agent Flow-Based IDS called MFIREv3 that provides network anomaly detection of intrusions and automated defense. This version of the MFIRE system includes the development and testing of a Multi-Objective Evolutionary Algorithm (MOEA) for feature selection that provides agents with the optimal set of features for classifying the state of the network. Feature selection provides separable data points for the selected attacks: Worm, Distributed Denial of Service, Man-in-the-Middle, Scan, and Trojan. This investigation develops three techniques of self-organization for multiple distributed agents in an intrusion detection system: Reputation, Stochastic, and Maximum Cover. These three movement models are tested for effectiveness in locating good agent vantage points within the network to classify the state of the network. MFIREv3 also introduces the design of defensive measures to limit the effects of network attacks. Defensive measures included in this research are rate-limiting and elimination of infected nodes. The results of this research provide an optimistic outlook for flow-based multi-agent systems for cyber security. The impact of this research illustrates how feature selection in cooperation with movement models for multi agent systems provides excellent attack detection and classification

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Survival in the e-conomy: 2nd Australian information warfare & security conference 2001

This is an international conference for academics and industry specialists in information warfare, security, and other related fields. The conference has drawn participants from national and international organisations

Finding and Exploiting Vulnerabilities in Embedded TCP/IP Stacks

In the context of the rapid development of IoT technology, cyber-attacks are becoming more frequent, and the damage caused by cyber-attacks is remaining obstinately high. How to take the initiative in the rivalry with attackers is a major problem in today's era of the Internet. Vulnerability research is of great importance in this contest, especially the study of vulnerability detection and exploitation methodologies. The objective of the thesis is to examine vulnerabilities in DNS client implementations of embedded TCP/IP stacks, specifically in terms of vulnerability detection and vulnerability exploitation research. In the thesis, a detection method is developed for some anti-patterns in DNS client implementations using a static analysis platform. We tested it against 10 embedded TCP/IP stacks, the result shows that the developed detection method has high precision for detecting the vulnerabilities found by the Forescout research labs with a total of 88% accuracy. For different anti-patterns, the method has different detection precision and it is closely related to the implementation of the detection queries. The thesis also conducted vulnerability exploitation research for a heap overflow vulnerability that exists in a DNS client implementation of a popular TCP/IP stack. A proof-of-concept of this exploitation is developed. Though there are many constraints for successful exploitations, the ability to conduct remote code execution attacks still makes exploitation of heap overflow vulnerability dangerous. In addition, attacks against TCP/IP stacks can take advantage of the stacks and make it possible for an attacker to exploit vulnerabilities in other devices. Often it takes a huge amount of time for researchers to have deep knowledge of a codebase and to find vulnerabilities in it. But with the developed detection method, we can automate the process of locating vulnerable code patterns to add support for detecting relevant vulnerabilities. Research on the exploitation of vulnerabilities can allow us to discover the potential impact of a vulnerability from the perspective of an attacker and implement targeted defences

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