With the rise of information technology and the dependence on these systems, it becomes increasingly more important to keep the systems secure. The possibility to detect an intrusion with intrusion detection systems (IDS) is one of multiple fundamental technologies that may increase the security of a system. One of the bigger challenges of an IDS, is to detect types of intrusions that have previously not been encountered, so called unknown intrusions. These types of intrusions are generally detected by using methods collectively called anomaly detection methods. In this thesis I evaluate the performance of the algorithm Tree Augmented Naive Bayes Classifier (TAN) as an intrusion detection classifier. More specifically, I created a TAN program from scratch in Python and tested the program on two data sets containing data traffic. The thesis aims to create a better understanding of how TAN works and evaluate if it is a reasonable algorithm for intrusion detection. The results show that TAN is able to perform at an acceptable level with a reasonably high accuracy. The results also highlights the importance of using the smoothing operator included in the standard version of TAN. / Med informationsteknikens utveckling och det ökade beroendet av dessa system, blir det alltmer viktigt att hålla systemen säkra. Intrångsdetektionssystem (IDS) är en av många fundamentala teknologier som kan öka säkerheten i ett system. En av de större utmaningarna inom IDS, är att upptäcka typer av intrång som tidigare inte stötts på, så kallade okända intrång. Dessa intrång upptäcks oftast med hjälp av metoder som kollektivt kallas för avvikelsedetektionsmetoder. I denna uppsats utvärderar jag algoritmen Tree Augmented Naive Bayes Classifiers (TAN) prestation som en intrångsdetektionsklassificerare. Jag programmerade ett TAN-program, i Python, och testade detta program på två dataset som innehöll datatrafik. Denna uppsats ämnar att skapa en bättre förståelse för hur TAN fungerar, samt utvärdera om det är en lämplig algoritm för detektion av intrång. Resultaten visar att TAN kan prestera på en acceptabel nivå, med rimligt hög noggrannhet. Resultaten markerar även betydelsen av "smoothing operator", som inkluderas i standardversionen av TAN.

This research work presents the design and implementation of a host-based Intrusion Detection and Prevention System (IDPS) called HIDROID (Host-based Intrusion Detection and protection system for andROID) for Android smartphones. It runs completely on the mobile device, with a minimal computation burden. It collects data in real-time, periodically sampling features that reflect the overall utilisation of scarce resources of a mobile device (e.g. CPU, memory, battery, bandwidth, etc.). The Detection Engine of HIDROID adopts an anomaly-based approach by exploiting statistical and machine learning algorithms. That is, it builds a data-driven model for benign behaviour and looks for the outliers considered as suspicious activities. Any observation failing to match this model triggers an alert and the preventive agent takes proper countermeasure(s) to minimise the risk. The key novel characteristic of the Detection Engine of HIDROID is the fact that it requires no malicious data for training or tuning. In fact, the Detection Engine implements the following two anomaly detection algorithms: a variation of K-Means algorithm with only one cluster and the univariate Gaussian algorithm. Experimental test results on a real device show that HIDROID is well able to learn and discriminate normal from anomalous behaviour, demonstrating a very promising detection accuracy of up to 0.91, while maintaining false positive rate below 0.03. Finally, it is noteworthy to mention that to the best of our knowledge, publicly available datasets representing benign and abnormal behaviour of Android smartphones do not exist. Thus, in the context of this research work, two new datasets were generated in order to evaluate HIDROID. / Fundação para a Ciência e Tecnologia (FCT-Portugal) with reference SFRH/BD/112755/2015, European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020), Regional Operational Program of the Algarve (2020), Fundação para a Ciência e Tecnologia; i-Five .: Extensão do acesso de espectro dinâmico para rádio 5G, POCI-01-0145-FEDER-030500, Instituto de telecomunicações, (IT-Portugal) as the host institution.

In this dissertation research, we design and analyze resilient cyber-physical systems (CPSs) under high network dynamics, adversarial attacks, and various uncertainties. We focus on three key system attributes to build resilient CPSs by developing a suite of the autonomous cyber defense mechanisms. First, we consider network adaptability to achieve the resilience of a CPS. Network adaptability represents the network ability to maintain its security and connectivity level when faced with incoming attacks. We address this by network topology adaptation. Network topology adaptation can contribute to quickly identifying and updating the network topology to confuse attacks by changing attack paths. We leverage deep reinforcement learning (DRL) to develop CPSs using network topology adaptation. Second, we consider the fault-tolerance of a CPS as another attribute to ensure system resilience. We aim to build a resilient CPS under severe resource constraints, adversarial attacks, and various uncertainties. We chose a solar sensor-based smart farm as one example of the CPS applications and develop a resource-aware monitoring system for the smart farms. We leverage DRL and uncertainty quantification using a belief theory, called Subjective Logic, to optimize critical tradeoffs between system performance and security under the contested CPS environments. Lastly, we study system resilience in terms of system recoverability. The system recoverability refers to the system's ability to recover from performance degradation or failure. In this task, we mainly focus on developing an automated intrusion response system (IRS) for CPSs. We aim to design the IRS with effective and efficient responses by reducing a false alarm rate and defense cost, respectively. Specifically, We build a lightweight IRS for an in-vehicle controller area network (CAN) bus system operating with DRL-based autonomous driving. / Doctor of Philosophy / In this dissertation research, we design and analyze resilient cyber-physical systems (CPSs) under high network dynamics, adversarial attacks, and various uncertainties. We focus on three key system attributes to build resilient CPSs by developing a suite of the autonomous cyber defense mechanisms. First, we consider network adaptability to achieve the resilience of a CPS. Network adaptability represents the network ability to maintain its security and connectivity level when faced with incoming attacks. We address this by network topology adaptation. Network topology adaptation can contribute to quickly identifying and updating the network topology to confuse attacks by changing attack paths. We leverage deep reinforcement learning (DRL) to develop CPSs using network topology adaptation. Second, we consider the fault-tolerance of a CPS as another attribute to ensure system resilience. We aim to build a resilient CPS under severe resource constraints, adversarial attacks, and various uncertainties. We chose a solar sensor-based smart farm as one example of the CPS applications and develop a resource-aware monitoring system for the smart farms. We leverage DRL and uncertainty quantification using a belief theory, called Subjective Logic, to optimize critical tradeoffs between system performance and security under the contested CPS environments. Lastly, we study system resilience in terms of system recoverability. The system recoverability refers to the system's ability to recover from performance degradation or failure. In this task, we mainly focus on developing an automated intrusion response system (IRS) for CPSs. We aim to design the IRS with effective and efficient responses by reducing a false alarm rate and defense cost, respectively. Specifically, We build a lightweight IRS for an in-vehicle controller area network (CAN) bus system operating with DRL-based autonomous driving.

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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.

Efficient self-organizing virtual clusterheads that supervise data collection based on their wireless connectivity, risk, and overhead costs, are an important element of Wireless Sensor Networks (WSNs). This function is especially critical during deployment when system resources are allocated to a subsequent application. In the presented research, a model used to deploy intrusion detection capability on a Local Area Network (LAN), in the literature, was extended to develop a role-based hierarchical agent deployment algorithm for a WSN. The resulting model took into consideration the monitoring capability, risk, deployment distribution cost, and monitoring cost associated with each node. Changing the original LAN methodology approach to model a cluster-based sensor network depended on the ability to duplicate a specific parameter that represented the monitoring capability. Furthermore, other parameters derived from a LAN can elevate costs and risk of deployment, as well as jeopardize the success of an application on a WSN. A key component of the approach presented in this research was to reduce the costs when established clusterheads in the network were found to be capable of hosting additional detection agents. In addition, another cost savings component of the study addressed the reduction of vulnerabilities associated with deployment of agents to high volume nodes. The effectiveness of the presented method was validated by comparing it against a type of a power-based scheme that used each node's remaining energy as the deployment value. While available energy is directly related to the model used in the presented method, the study deliberately sought out nodes that were identified with having superior monitoring capability, cost less to create and sustain, and are at low-risk of an attack. This work investigated improving the efficiency of an intrusion detection system (IDS) by using the proposed model to deploy monitoring agents after a temperature sensing application had established the network traffic flow to the sink. The same scenario was repeated using a power-based IDS to compare it against the proposed model. To identify a clusterhead's ability to host monitoring agents after the temperature sensing application terminated, the deployed IDS utilized the communication history and other network factors in order to rank the nodes. Similarly, using the node's communication history, the deployed power-based IDS ranked nodes based on their remaining power. For each individual scenario, and after the IDS application was deployed, the temperature sensing application was run for a second time. This time, to monitor the temperature sensing agents as the data flowed towards the sink, the network traffic was rerouted through the new intrusion detection clusterheads. Consequently, if the clusterheads were shared, the re-routing step was not preformed. Experimental results in this research demonstrated the effectiveness of applying a robust deployment metric to improve upon the energy efficiency of a deployed application in a multi-application WSN. It was found that in the scenarios with the intrusion detection application that utilized the proposed model resulted in more remaining energy than in the scenarios that implemented the power-based IDS. The algorithm especially had a positive impact on the small, dense, and more homogeneous networks. This finding was reinforced by the smaller percentage of new clusterheads that was selected. Essentially, the energy cost of the route to the sink was reduced because the network traffic was rerouted through fewer new clusterheads. Additionally, it was found that the intrusion detection topology that used the proposed approach formed smaller and more connected sets of clusterheads than the power-based IDS. As a consequence, this proposed approach essentially achieved the research objective for enhancing energy use in a multi-application WSN.

Wireless networks have become ubiquitous, where a wide range of mobile devices are connected to a larger network like the Internet via wireless communications. One widely used wireless communication standard is the IEEE 802.11 protocol, popularly called Wi-Fi. Over the years, the 802.11 has been upgraded to different versions. But most of these upgrades have been focused on the improvement of the throughput of the protocol and not enhancing the security of the protocol, thus leaving the protocol vulnerable to attacks. The goal of this research is to develop and implement an intrusion detection system based on anomaly behavior analysis that can detect accurately attacks on the Wi-Fi networks and track the location of the attacker. As a part of this thesis we present two architectures to develop an anomaly based intrusion detection system for single access point and distributed Wi-Fi networks. These architectures can detect attacks on Wi-Fi networks, classify the attacks and track the location of the attacker once the attack has been detected. The system uses statistical and probability techniques associated with temporal wireless protocol transitions, that we refer to as Wireless Flows (Wflows). The Wflows are modeled and stored as a sequence of n-grams within a given period of analysis. We studied two approaches to track the location of the attacker. In the first approach, we use a clustering approach to generate power maps that can be used to track the location of the user accessing the Wi-Fi network. In the second approach, we use classification algorithms to track the location of the user from a Central Controller Unit. Experimental results show that the attack detection and classification algorithms generate no false positives and no false negatives even when the Wi-Fi network has high frame drop rates. The Clustering approach for location tracking was found to perform highly accurate in static environments (81% accuracy) but the performance rapidly deteriorates with the changes in the environment. While the classification algorithm to track the location of the user at the Central Controller/RADIUS server was seen to perform with lesser accuracy then the clustering approach (76% accuracy) but the system's ability to track the location of the user deteriorated less rapidly with changes in the operating environment.

Intrusion Prevention Systems (IPSs) have long been proposed as a defense against attacks that propagate too fast for any manual response to be useful. While purely-network-based IPSs have the advantage of being easy to install and manage, research have shown that this class of systems are vulnerable to evasion [70, 65], and can be tricked into filtering normal traffic and create more harm than good [12, 13]. Based on these researches, we believe information about how the attacked hosts process the malicious input is essential to an effective and reliable IPS. In existing IPSs, honeypots are usually used to collect such information. The collected information will then be analyzed to generate countermeasures against the observed attack. Unfortunately, techniques that allow the honeypots in a network to be identified ([5, 71]) can render these IPSs useless. In particular, attacks can be designed to avoid targeting the identified honeypots. As a result, the IPSs will have no information about the attacks, and thus no countermeasure will ever be generated. The use of honeypots is also creating other practical issues which limit the usefulness/feasibility of many host-based IPSs. We propose to solve these problems by duplicating the detection and analysis capability on every protected system; i.e., turning every host into a honeypot. / text