Deep Learning-Based Intrusion Detection Methods for Computer Networks and Privacy-Preserving Authentication Method for Vehicular Ad Hoc Networks

The incidence of computer network intrusions has significantly increased over the last decade, partially attributed to a thriving underground cyber-crime economy and the widespread availability of advanced tools for launching such attacks. To counter these attacks, researchers in both academia and industry have turned to machine learning (ML) techniques to develop Intrusion Detection Systems (IDSes) for computer networks. However, many of the datasets use to train ML classifiers for detecting intrusions are not balanced, with some classes having fewer samples than others. This can result in ML classifiers producing suboptimal results. In this dissertation, we address this issue and present better ML based solutions for intrusion detection. Our contributions in this direction can be summarized as follows: Balancing Data Using Synthetic Data to detect intrusions in Computer Networks: In the past, researchers addressed the issue of imbalanced data in datasets by using over-sampling and under-sampling techniques. In this study, we go beyond such traditional methods and utilize a synthetic data generation method called Con- ditional Generative Adversarial Network (CTGAN) to balance the datasets and in- vestigate its impact on the performance of widely used ML classifiers. To the best of our knowledge, no one else has used CTGAN to generate synthetic samples for balancing intrusion detection datasets. We use two widely used publicly available datasets and conduct extensive experiments and show that ML classifiers trained on these datasets balanced with synthetic samples generated by CTGAN have higher prediction accuracy and Matthew Correlation Coefficient (MCC) scores than those trained on imbalanced datasets by 8% and 13%, respectively. Deep Learning approach for intrusion detection using focal loss function: To overcome the data imbalance problem for intrusion detection, we leverage the specialized loss function, called focal loss, that automatically down-weighs easy ex- amples and focuses on the hard negatives by facilitating dynamically scaled-gradient updates for training ML models effectively. We implement our approach using two well-known Deep Learning (DL) neural network architectures. Compared to training DL models using cross-entropy loss function, our approach (training DL models using focal loss function) improved accuracy, precision, F1 score, and MCC score by 24%, 39%, 39%, and 60% respectively. Efficient Deep Learning approach to detect Intrusions using Few-shot Learning: To address the issue of imbalance the datasets and develop a highly effective IDS, we utilize the concept of few-shot learning. We present a Few-Shot and Self-Supervised learning framework, called FS3, for detecting intrusions in IoT networks. FS3 works in three phases. Our approach involves first pretraining an encoder on a large-scale external dataset in a selfsupervised manner. We then employ few-shot learning (FSL), which seeks to replicate the encoder’s ability to learn new patterns from only a few training examples. During the encoder training us- ing a small number of samples, we train them contrastively, utilizing the triplet loss function. The third phase introduces a novel K-Nearest neighbor algorithm that sub- samples the majority class instances to further reduce imbalance and improve overall performance. Our proposed framework FS3, utilizing only 20% of labeled data, out- performs fully supervised state-of-the-art models by up to 42.39% and 43.95% with respect to the metrics precision and F1 score, respectively. The rapid evolution of the automotive industry and advancements in wireless com- munication technologies will result in the widespread deployment of Vehicular ad hoc networks (VANETs). However, despite the network’s potential to enable intelligent and autonomous driving, it also introduces various attack vectors that can jeopardize its security. In this dissertation, we present efficient privacy-preserving authenticated message dissemination scheme in VANETs. Conditional Privacy-preserving Authentication and Message Dissemination Scheme using Timestamp based Pseudonyms: To authenticate a message sent by a vehicle using its pseudonym, a certificate of the pseudonym signed by the central authority is generally utilized. If a vehicle is found to be malicious, certificates associated with all the pseudonyms assigned to it must be revoked. Certificate revocation lists (CRLs) should be shared with all entities that will be corresponding with the vehicle. As each vehicle has a large pool of pseudonyms allocated to it, the CRL can quickly grow in size as the number of revoked vehicles increases. This results in high storage overheads for storing the CRL, and significant authentication overheads as the receivers must check their CRL for each message received to verify its pseudonym. To address this issue, we present a timestamp-based pseudonym allocation scheme that reduces the storage overhead and authentication overhead by streamlining the CRL management process

Security Strategies Information Technology Security Mangers Use in Deploying Blockchain Applications

Blockchain is seen as a potential game-changer in many industries and a transformational technology in the 21st century. However, security concerns have made blockchain technology adoption relatively slow. Massive security breaches in cryptocurrency, an example of blockchain technology, have caused organizations to lose $11.3 billion in illegal transactions, exacerbating these security concerns for information technology (IT) security managers who are worried about the safety of blockchain. Grounded in the routine activity theory, the purpose of this multiple case study was to explore strategies used by IT security managers to deploy blockchain applications securely. The participants were 4 IT security managers from companies in Ghana, the United States, and Europe with experience in implementing blockchain applications securely. Data collection was done using semistructured interviews and a review of organizational documents for triangulation. A thematic analysis produced three themes: (a) cryptographic key management, (b) comprehensive software auditing, and (c) traditional IT security controls. A critical recommendation is for security managers to implement the National Institute of Technology (NIST) key management and cybersecurity frameworks. The implications for positive social change include the potential to alter people’s negative perceptions of blockchain security and giving security assurance to individuals and organizations on their digital assets stored in a blockchain system. In addition, a secured blockchain system could improve people’s confidence in blockchain applications for an increased adoption rate of this useful technology development

Strategies for Integrating the Internet of Things in Educational Institutions

The introduction of the Internet of Things (IoT) into educational institutions has necessitated the integration of IoT devices in the information technology (IT) infrastructural environment of educational institutions. Many IT leaders at educational institutions, however, lack strategies for integrating and deploying IoT devices in their institutions, which has resulted in numerous security breaches. The purpose of this study was to explore security strategies adopted by IT administrators to prevent data breaches resulting from the integration of IoT devices in their educational institutions. The diffusion of innovations theory served as the conceptual framework for this qualitative multiple case study. Eleven IT leaders in 11 public K–12 educational institutions, who had successfully integrated IoT in their educational institutions in the United States Midwest region, were interviewed. Thematic analysis was the data analysis strategy. The 3 major themes that emerged were (a) organizational breach prevention, (b) infrastructure management—external to IT, and (c) policy management—internal to IT. A key recommendation is for IT leaders to develop strategies to harness the efficiencies and stabilities that exist during the integration of IoT devices in their educational institutions. The implications for social change include the potential for securely transforming the delivery of education to students and ensuring the safety of academic personnel by identifying strategies that IT leaders can use to securely integrate IoT devices in educational settings