Cybersecurity has become ubiquitous with the exponential growth of consumer applications and Internet of Things (IoT) devices. It is a discipline which intersects other important industries such as energy, manufacturing, and healthcare. Unfortunately, the educational pace of how Cybersecurity is taught in the classroom has not kept up to date with the research pace i.e., the latest methods revolving around Artificial Intelligence (AI) driven Cybersecurity have not permeated on the education ecosystem. The focus of the paper is to provide a framework which addresses such shortcomings by continuously investigating the impact of developments in AI technology towards redesigning and enhancing the Cybersecurity curriculum in order to produce graduates that are fit for federal, state, local, and tribal governments Cybersecurity missions.

Cybersecurity has become ubiquitous with the exponential growth of consumer applications and Internet of Things (IoT) devices. It is a discipline which intersects other important industries such as energy, manufacturing, and healthcare. Cybersecurity curriculum has traditionally been taught through a mixture of technical-track and non-technical-track (policy, legal and ethical) related courses. In this paper, we chronicle our Division’s efforts to gamify the technical-track curriculum through the Escape the Classroom (ETC) paradigm. The aim of the curriculum gamification is to provide students a fun, interesting, and rewarding experience while learning Cybersecurity through the appropriate Bloom taxonomy levels. The paper discusses our approach and highlights challenges that our faculty encountered while applying ETC towards the Cybersecurity curriculum.

It is an understatement to say that teaching efficient Computer Science and Cybersecurity courses amidst the COVID19 pandemic has placed extreme pressure on faculty. In general, this is true for any academic area which has a strong hands-on component tied to the theoretical counterpart. In this paper, we chronicle and provide our approach, from the lens of St. John's University Computer Science Chairperson and the Cybersecurity Program Director, on the suitability of utilizing WebEx HandsOn-Labs tool to close the instructional gap that exists between face-to-face and fully online teaching.

In this paper we propose a novel privacy-preserving scheme based on the fusion of Dynamic Spectrum Access (DSA) and crowd-sensing paradigm. Our scheme provides location privacy for the crowd-sensing users through homomorphic cryptographic constructions. Furthermore, our scheme mitigates fraudulent sensing report attacks by providing robust fraud prevention based on homomorphic cryptographic constructions. We provide substantial experiments on real-life datasets which shows that our proposed protocol provides realistic efficiency and security.

Security and anonymity are vital components in today's networked world, and play critical roles in several reallife situations, such as whistleblowing, intelligence operations, oppressive governments, etc. In this paper, we study anonymous communications in the context of Delay Tolerant Networks (DTNs). Existing work in this area relies on the standard onion routing paradigm to provide anonymity and is, therefore, vulnerable to malicious nodes. To this end, we introduce a novel message forwarding algorithm that utilizes random walks to deliver messages to their destinations. By removing the requirement to list all the intermediate nodes on the end-to-end path, our method enhances considerably the anonymity of the underlying communications. Our simulation results show that the proposed forwarding algorithm achieves high message delivery rates, at the expense of a moderate computational overhead at the mobile devices.

Dynamic spectrum access (DSA) is envisioned as a promising framework for addressing the spectrum shortage caused by the rapid growth of connected wireless devices. In contrast to the legacy fixed spectrum allocation policies, DSA allows license-exempt users to access the licensed spectrum bands when not in use by their respective owners. More specifically, in the database-driven DSA model, mobile users issue location-based queries to a white-space database, in order to identify idle channels in their area. To preserve location privacy, existing solutions suggest the use of private information retrieval (PIR) protocols when querying the database. Nevertheless, these methods are not communication efficient and fail to take into account user mobility. In this paper, we address these shortcomings and propose an efficient privacy-preserving protocol based on the Hilbert space filling curve. We provide optimizations for mobile users that require privacy on-the-fly and users that have full a priori knowledge of their trajectory. Through experimentation with two real life datasets, we show that, compared to the current state-of-the-art protocol, our methods reduce the query response time at the mobile clients by a large factor.

In the database-driven DSA model, clients learn their geographic location through a GPS device and use this location to retrieve a list of available channels from a centralized white-space database. To mitigate the potential privacy threats associated with location-based queries, existing work has proposed the use of private information retrieval (PIR) protocols when querying the database. Nevertheless, PIR protocols are very expensive and may lead to significant costs for highly mobile clients. In this paper, we propose a novel method that allows wireless users to collaborate in a peer-to-peer (P2P) manner, in order to share their cached channel availability information that is obtained from previous queries. Our experimental results with a real-life dataset show that our methods reduce the number of PIR queries by 50% to 60%, while incurring low computational and communication costs.

The complexity of the Dynamic Spectrum Access (DSA) framework and risk of disruption due to primary-secondary spectrum etiquette pose serious link layer challenges in multi-hop cognitive radio networks (MH-CRNs). In order to properly harness the DSA potential, cognitive radio (CR) channel scheduling and channel selection should be treated in a cross-layer approach with the network layer. In this paper, we present and evaluate a novel route aware dynamic channel scheduling and selection approach (RADCSS) for MH-CRNs. Our proposed approach provides a conflict-free time slot transmission scheduling between neighbor to neighbor CR pairs while estimating the probability of primary user (PU) disruption for every mutual channel. Furthermore, in a cross-layer fashion, our proposed method maximizes neighbor to neighbor transmission time slot reuse in accordance with network layer route request. Through simulations, we demonstrate that our proposed method provides significant improvement on the average number of channel switching, effective channel utilization and average throughput.