Theses and Dissertations - Department of Computer Science

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Mining and Ranking Incidents for High Priority Intrusion Analysis
(University of Alabama Libraries, 2020) Haque, Md Shariful; Atkison, Travis; University of Alabama Tuscaloosa
Threats and intrusions are increasing at an alarming rate, even though related technologies have observed rapid advancement. Hence, advanced threat analysis has become imperative to improve current technologies. These technologies are primarily designed to detect or predict threats and minimize the likelihood of damage. The goal of an efficient intrusion analysis is also to develop models unwavering to any external influences and produce optimized results. Several data mining techniques have been applied in these scenarios to detect both anomaly and misuse, predict possible attack paths, or generate attack models. Some consider determining the priority, an important criterion of alerts, using different characteristics of the attack scenarios. In this dissertation, novel priority-based alert mining techniques and a ranking model are proposed to prioritize sequences of alerts and to realize their actual effect which is often misunderstood due to the generic taxonomies used by detection systems. This dissertation has the following contributions: First, a novel data mining-based alert sequence mining technique is proposed to discover potential attacks from intrusion alerts. Intrusion detection systems maintain signatures of intrusions with a severity scale. This information has been leveraged predominantly in the proposed data mining-based alert association approach. This approach reduces the effort of post-processing alert sequences and calculating their severity when the relationship is established. Second, a non-redundant high priority association rules mining technique is proposed based on theories and background of non-redundant association rule mining. Such techniques are highly adopted to determine the correlation between items in sequences and to develop efficient prediction models with a reduced volume of derived data. Third, the above mining approaches facilitate the process of extracting severe incidents based on priority. However, severity levels determined by the detection system are generic; thus, their real consequences are hard to perceive. Multi-criteria decision making is a prominent research area to assess different alternatives. The proposed approach is equipped with a combination of MCDM techniques to further rank the prioritized threats based on several benchmarks. The novelty of our technique is to consider the priority level of alerts at prior stages of attack analysis and later determine the overall attack scenario.
A novel intersection-based clustering scheme for VANET
(University of Alabama Libraries, 2021) Lee, Michael Sutton; Atkison, Travis; University of Alabama Tuscaloosa
Currently, much attention is being placed on the development and deployment of vehicle communication technologies. Such technologies could revolutionize both navigation and entertainment systems available to drivers. However, there are still many challenges posed by this field that are in need of further investigation. One of these is the limitations on the throughput of networks created by vehicular devices. As such, it is necessary to resolve some of these network throughput issues so that vehicle communication technologies can increase the amount of information they exchange. One scheme to improve network throughput involves dividing the vehicles into subgroups called clusters. Many such clustering algorithms have been proposed, but none have yet been determined to be optimal. This dissertation puts forth a new passive clustering approach that has the key advantage of a significantly reduced overhead. The reduced overhead of passive algorithms increases the amount of the network available in which normal data transmissions can occur. The drawback to passive algorithms is their unreliable knowledge of the network which can cause them to struggle to successfully perform cluster maintenance activities. Clusters created by passive algorithms, therefore, tend to be shorter-lived and smaller than what an active clustering algorithm can maintain. In order to maintain a cluster with a low overhead and better knowledge of the network, this dissertation introduces a new clustering algorithm intended to function at intersections. This new algorithm attempts to take advantage of the decreased overhead of passive clustering algorithms while introducing a lightweight machine learning algorithm that will assist with cluster selection.
Redefining privacy: case study of smart health applications
(University of Alabama Libraries, 2019) Al-Zyoud, Mahran; Carver, Jeffrey; University of Alabama Tuscaloosa
Smart health utilizes the unique capabilities of smart devices to improve healthcare. The smart devices continuously collect and transfer large amounts of useful data about the users' health. As data collection and sharing are two inevitable norms in this connected world, concerns have also been growing about the privacy of health information. Any mismatch between what the user really wants to share and what the devices share could either cause a privacy breach or limit a beneficial service. Understanding what influences information sharing can help resolve mismatches and brings protection and benefits to all stakeholders. The primary goal of this dissertation is to better understand the variability of privacy perceptions among different individuals and reflect this understanding into smart health applications. Towards this goal, this dissertation presents three studies. The first study is a systematic literature review conducted to identify the reported privacy concerns and the suggested solutions and to examine whether the context is part of any effort to describe a concern or form a solution. The study reveals 7 categories of privacy concerns and 5 categories of privacy solutions. I present a mapping between these major concerns and solutions to highlight areas in need of additional research. The results also revealed that there is a lack of both user-centric and context-aware solutions. The second study further empirically investigates the role of context and culture on the sharing decision. It describes a multicultural survey and another cross-cultural survey. The results support the intuitive view of how variable privacy perception is among different users and how understanding a user's culture could play a role in offering a smarter, dynamic set of privacy settings that reflects his privacy needs. Finally, the third study aims at providing a solution that helps users configure their privacy settings. The solution utilizes machine learning to predict the most suitable configuration for the user. As a proof of concept, I implemented and evaluated a prototype of a recommender system. Usage of such recommender systems helps make changing privacy settings less burden in addition to better reflecting the true privacy preferences of users.

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Browsing Theses and Dissertations - Department of Computer Science by Author "Atkison, Travis"

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