Government agencies and corporations are growing increasingly reliant on networks for day-to-day operations including communication, data processing, and data storage. As a result, these networks are in a constant state of growth. These burgeoning networks cause the number of network security events requiring investigation to grow exceptionally, creating new problems for network security analysts. The increasing number of attacks propagated against high-value networks only increases the gravity. Therefore, security analysts need assistance to be able to continue to monitor network events at an acceptable rate. Network analysts rely on many different systems and tools to properly secure a network. One line of defense is an intrusion detection system or IDS. Intrusion detection systems monitor networks for suspicious activity and then print alerts to a log file. An important part of effective intrusion detection is finding relationships between network events, which allows for detection of network anomalies. However, network analysts typically monitor these logs in a sparsely formatted view, which simply isn’t effective for large networks. Therefore, a Visual Intrusion Detection System or VIDS is an interesting solution to aid network security analysts in properly securing the networks. The visualization tool takes a log file and represents the alerts on a three-dimensional graph. Previous research shows that humans have an innate ability to match patterns based on visual cues, which we hope will allow network analysts to match patterns between alerts and identify anomalies. In addition, the tool will leverage the user’s intuition and experience to aid intrusion detection by allowing them to manipulate the view of the data. The objective of this thesis is to quantify and measure the effectiveness of this Visual Intrusion Detection System built as an extension to the SNORT open source IDS. The purpose of the visualization is to give network security analysts an alternative view from what traditional network security software provides. This thesis will also explore other features that can be built into a Visual Intrusion Detection System to improve its functionality. / Department of Computer Science

Thesis (M.S.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains viii, 64 p. : ill., maps. Includes abstract. Includes bibliographical references (p. 58-64).

Cross-domain intrusion detection, a critical component of cybersecurity, involves evaluating the performance of neural networks across diverse datasets or databases. The ability of intrusion detection systems to effectively adapt to new threats and data sources is paramount for safeguarding networks and sensitive information. This research delves into the intricate world of cross-domain intrusion detection, where neural networks must demonstrate their versatility and adaptability. The results of our experiments expose a significant challenge: the phenomenon known as catastrophic forgetting. This is the tendency of neural networks to forget previously acquired knowledge when exposed to new information. In the context of intrusion detection, it means that as models are sequentially trained on different intrusion detection datasets, their performance on earlier datasets degrades drastically. This degradation poses a substantial threat to the reliability of intrusion detection systems. In response to this challenge, this research investigates potential solutions to mitigate the effects of catastrophic forgetting. We propose the application of continual learning techniques as a means to address this problem. Specifically, we explore the Elastic Weight Consolidation (EWC) algorithm as an example of preserving previously learned knowledge while allowing the model to adapt to new intrusion detection tasks. By examining the performance of neural networks on various intrusion detection datasets, we aim to shed light on the practical implications of catastrophic forgetting and the potential benefits of adopting EWC as a memory-preserving technique. This research underscores the importance of addressing catastrophic forgetting in cross-domain intrusion detection systems. It provides a stepping stone for future endeavours in enhancing multi-task learning and adaptability within the critical domain of intrusion detection, ultimately contributing to the ongoing efforts to fortify cybersecurity defences.

Enterprise networks present very high value targets in the eyes of malicious actors who seek to exfiltrate sensitive proprietary data, disrupt the operations of a particular organization, or leverage considerable computational and network resources to further their own illicit goals. For this reason, enterprise networks typically attract the most determined of attackers. These attackers are prone to using the most novel and difficult-to-detect approaches so that they may have a high probability of success and continue operating undetected. Many existing network security approaches that fall under the category of intrusion detection systems (IDS) and intrusion prevention systems (IPS) are able to detect classes of attacks that are well-known. While these approaches are effective for filtering out routine attacks in automated fashion, they are ill-suited for detecting the types of novel tactics and zero-day exploits that are increasingly used against the enterprise. In this thesis, a solution is presented that augments existing security measures to provide enhanced coverage of novel attacks in conjunction with what is already provided by traditional IDS and IPS. The approach enables honeypots, a class of tech- nique that observes novel attacks by luring an attacker to perform malicious activity on a system having no production value, to be deployed in a turn-key fashion and at large scale on enterprise networks. In spite of the honeypot’s efficacy against tar- geted attacks, organizations can seldom afford to devote capital and IT manpower to integrating them into their security posture. Furthermore, misconfigured honeypots can actually weaken an organization’s security posture by giving the attacker a stag- ing ground on which to perform further attacks. A turn-key approach is needed for organizations to use honeypots to trap, observe, and mitigate novel targeted attacks.

M. Tech. Information Networks / The growth of the internet has made it easy for people to exchange information resulting in the abundance of information commonly referred to as information overload. It causes retailers to fail to make adequate sales since the customers are swamped with a lot of options and choices. To lessen this problem retailers have begun to find it useful to make use of algorithmic approaches to determine which content to show consumers. These algorithmic approaches are known as recommender systems. Collaborative Filtering recommender systems suggest items to users based on other users reported prior experience with those items. These systems are, however, vulnerable to shilling attacks since they are highly dependent on outside sources of information. Shilling is a process in which syndicating users can connive to promote or demote a certain item, where malicious users benefit from introducing biased ratings. It is, however, critical that shilling detection systems are implemented to detect, warn and shut down shilling attacks within minutes. Modern patented shilling detection systems employ: (a) classification methods, (b) statistical methods, and (c) rules and threshold values defined by shilling detection analysts, using their knowledge of valid shilling cases and the false alarm rate as guidance. The goal of this dissertation is to determine a context for, and assess the performance of Meta-Learning techniques that can be integrated in the shilling detection process.

Firms lose millions of dollars every year to cyber-attacks and the risk to these companies is growing exponentially. The threat to monetary and intellectual property has made Information Technology (IT) security management a critical challenge to firms. Security devices, including Intrusion Detections Systems (IDS), are commonly used to help protect these firms from malicious users by identifying the presence of malicious network traffic. However, the actual value of these devices remains uncertain among the IT security community because of the costs associated with the implementation of different monitoring strategies that determine when to inspect potentially malicious traffic and the costs associated with false positive and negative errors. Game theoretic models have proven effective for determining the value of these devices under several conditions where firms and users are modeled as players. However, these models assume that both the firm and attacker have complete information about their opponent and lack the ability to account for more realistic situations where players have incomplete information regarding their opponent's payoffs. The proposed research develops an enhanced model that can be used for strategic decision making in IT security management where the firm is uncertain about the user's utility of intrusion. By using Harsanyi Transformation Analysis, the model provides the IT security research community with valuable insight into the value of IDS when the firm is uncertain of the incentives and payoffs available to users choosing to hack. Specifically, this dissertation considers two possible types of users with different utility for intrusion to gain further insights about the players' strategies. The firm's optimal strategy is to start the game with the expected value of the user's utility as an estimate. Under this strategy, the firm can determine the user's utility with certainty within one iteration of the game. After the first iteration, the game may be analyzed as a game of perfect information.

O objetivo principal do presente trabalho é apresentar uma proposta que permita a combinação entre uma solução de captura de pacotes já existente e não muito flexível (sniffer) e o conceito de agentes móveis para aplicação em redes segmentadas. Essa pesquisa possui como foco principal a aplicação da técnica captura de pacotes em SDIs network based, utilizando para isso o modelo desenvolvido no ICMC (Cansian, 1997) e posteriormente adequado ao ambiente de agentes móveis (Bernardes, 1999). Assim sendo, foi especificada a camada base do ambiente desenvolvido em (Bernardes, 1999) visando as interações entre seus agentes e o agente de captura de pacotes. / The main objective of the current work is to present a proposal that allows the combination between an existent and not so flexible packet capture solution (sniffer) and the concept of mobile agents for application in switched networks. This research focuses the application of the packet capture technique in IDSs network-based, using for this purpose the model developed at ICMC (Cansian, 1997) and later adjusted to the mobile agents environment (Bernardes, 1999). Therefore, the base layer of the developed environment (Bernardes, 1999} was specified focusing the interactions between its agents and the packet capture agent.

Measurement is a vital tool for organizations attempting to increase, evaluate, or simply maintain their overall security posture over time. Organizations rely on defense in depth, which is a layering of multiple defenses, in order to strengthen overall security. Measuring organizations' total security requires evaluating individual security controls such as firewalls, antivirus, or intrusion detection systems alone as well as their joint effectiveness when deployed together in defense in depth. Currently, organizations must rely on best practices rooted in ad hoc expert opinion, reports on individual product performance, and marketing hype to make their choices. When attempting to measure the total security provided by a defense in depth architecture, dependencies between security controls compound the already difficult task of measuring a single security control accurately. We take two complementary approaches to address this challenge of measuring the total security provided by defense in depth deployments. In our first approach, we use direct measurement where for some set of attacks, we compute a total detection rate for a set of security controls deployed in defense in depth. In order to compare security controls operating on different types of data, we link together all data generated from each particular attack and track the specific attacks detected by each security control. We implement our approach for both the drive-by download and web application attack vectors across four separate layers each. We created an extensible automated framework for web application data generation using public sources of English text. For our second approach, we measure the total adversary cost that is the total effort, resources, and time required to evade security controls deployed in defense in depth. Dependencies between security controls prevent us from simply summing the adversary cost to evade individual security controls in order to compute a total adversary cost. We create a methodology that accounts for these dependencies especially focusing on multiplicative relationships where the adversary cost of evading two security controls together is more than the sum of the adversary costs to evade each individually. Using the insight gained into the multiplicative dependency, we design a method for creating sets of multiplicative security controls. Additionally, we create a prototype to demonstrate our methodology for empirically measuring total adversary cost using attack tree visualizations and a database design capable of representing dependent relationships between security controls.

Em decorrência do aumento do número de ataques de origem interna, a utilização de mecanismos de proteção, como o firewall, deve ser ampliada. Visto que este tipo de ataque, ocasionado pelos usuários internos ao sistema, não permite a localização imediata, torna-se necessário o uso integrado de diversas tecnologias para aumentar a capacidade de defesa de um sistema. Desta forma, a introdução de agentes móveis em apoio a segurança computacional apresenta-se como uma solução natural, uma vez que permitirá a distribuição de tarefas de monitoramento do sistema e automatização do processo de tomada de decisão, no caso de ausência do administrador humano. Este trabalho apresenta uma avaliação do uso do mecanismo de agentes móveis para acrescentar características de mobilidade ao processo de monitoração de intrusão em sistemas computacionais. Uma abordagem modular é proposta, onde agentes pequenos e independentes monitoram o sistema. Esta abordagem apresenta significantes vantagens em termos de overhead, escalabilidade e flexibilidade. / The use of protection mechanisms must be improved due the increase of attacks from internal sources. As this kind of attack, made by internal users do not allow its immediate localization, it is necessary the integrated use of several technologies to enhance the defense capabilities of a system. Therefore, the introduction of mobile agents to provide security appears to be a natural solution. It will allow the distribution of the system monitoring tasks and automate the decision making process, in the absence of a human administrator. This project presents an evaluation of the use of mobile agents to add mobile capabilities to the process of intrusion detection in computer systems. A finer-grained approach is proposed, where small and independent agents monitor the system. This approach has significant advantages in terms of overhead, scalability and flexibility.

The integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors of persistent and emerging threats. When collecting modern day network traffic for intrusion detection, substantial amounts of traffic can be collected, much of which consists of relatively few attack instances as compared to normal traffic. This skewed distribution between normal and attack data can lead to high levels of class imbalance. Machine learning techniques can be used to aid in attack detection, but large levels of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection