Rapid advancement in generative AI and large language models (LLMs) has enabled the creation of highly realistic and context-aware digital content. Multimodal systems such as ChatGPT with DALL-E integration and diffusion models like Stable Diffusion can now generate images that are often indistinguishable from human-created media, posing significant challenges for content authenticity and provenance. Ensuring the integrity and origin of digital data is critical for preserving trust, accountability, and legal compliance in digital ecosystems. This paper presents an embedding-based framework for detecting AI-generated images using vector similarity analysis. The proposed approach is based on the hypothesis that AI-generated images exhibit closer embedding proximity to other synthetic content, whereas human-created images cluster within distinct semantic regions. We validate this hypothesis by developing a prototype system that processes a diverse dataset of AI- and human-generated images using five benchmark embedding models. Furthermore we investigated whether perturbation with image, such as blurring or putting patches, would allow AI-generated images to confuse the system. Experimental results confirm the robustness of the method, showing that image perturbations have minimal impact on the embedding space, with altered images retaining high similarity to their originals.

Regulatory changes have enabled American student-athletes to profit from their name, image, and likeness (NIL). However, only a fraction of the student-athlete population is actually profiting from their NIL, which raises questions concerning fairness and inclusiveness. Motivated by that scenario, we look at technological solutions capable of sharing a limited amount of financial resources fairly and inclusively. Following a design science methodology, we define design requirements for such technological solutions after interviewing student-athletes, which leads us to establish the inclusive-meritocratic fairness criterion. Subsequently, we determine design principles that artifacts aiming at helping student-athletes should satisfy. We find that a solution that satisfies the proposed design principles is to associate student-athletes with digital collectibles represented as non-fungible tokens (NFTs). The core idea behind our artifact is that student-athletes receive royalties in primary markets after NFTs are randomly minted, plus deterministic royalties in secondary markets whenever a transaction involving their collectibles happens. Interviews with student-athletes validate our design. We conclude the paper by discussing how our ideas give rise to a new NIL design theory.

Peer review is an essential component of the evaluation and dissemination of new scientific knowledge. The peer review process can be viewed as a decision support framework relying on scholarly review systems, where decision-makers (editors) solicit input from experts (reviewers) to make editorial decisions on submitted manuscripts. Unfortunately, the challenges editors face in securing sufficient reviewers are well-documented, leading to prolonged review times and potentially diminished review quality. We explore and validate this trend through a literature review and interviews with scholars. We then employ a design science research methodology to design, develop, and evaluate potential incentive mechanisms to reverse that trend. In addition to proposing formal design principles that such mechanisms should follow, we suggest a concrete blockchain-based token system that enables editors to offer review incentives while enabling reviewers to flexibly utilize these incentives to meet their needs. We also explain how different types of tokens can be connected to practical submission and reward policies that journals may adopt. Our cost analysis, along with a survey-based field study and qualitative interviews with academics, highlight the effectiveness of our solution. Finally, we propose a formal design theory framework that designers of peer review systems can follow to create meaningful incentives to attract reviewers.

In July 2021, the IT management software company Kaseya was the victim of a ransomware cyberattack. The perpetrator of this attack was ransomware evil (REvil), an allegedly Russian‐based ransomware threat group. This paper addresses the general events of the incident and the actions executed by the constituents involved. The attack was conducted through specially crafted hypertext transfer protocol (HTTP) requests to circumvent authentication and allow hackers to upload malicious payloads through Kaseya’s virtual system administrator (VSA). The attack led to the emergency shutdown of many VSA servers and a federal investigation. REvil has had a tremendous impact performing ransomware operations, including worsening international relations between Russia and world leaders and costing considerable infrastructure damage and millions of dollars in ransom payments. We present an overview of Kaseya’s defense strategy involving customer interaction, a PowerShell script to detect compromised clients, and a cure‐all decryption key that unlocks all locked files.

Peer review is an integral part of academic publication and is necessary to maintain high standards and novelty of published research. Despite its importance, peer reviewers are rarely provided incentives, leading to journals having difficulty finding reviewers inclined to accept invitations and submit reviews on time. This paper proposes a Blockchain-based Anonymous Reviewer Incentive Token (BARIT) to incentivize peer reviewers. BARIT introduces flexible incentive schemes to provide both recognition and tangible benefits for the reviewers' contribution while preserving the anonymity of reviewers. Using blockchain technology to record reward tokens ensures their permanence and acceptance across different publishers. Incentive models are designed to encourage the involvement of researchers as reviewers, reduce reviewer refusal rates, and prompt the timely submission of review reports.

Mobile Edge Computing (MEC) has been gaining a major interest for use in Cyber-Physical Systems (CPS) for Disaster Response and Tactical applications. These CPS generate a very large amount of mission-critical and personal data that require resilient and secure storage and sharing. In this article, we present the design, implementation, and evaluation of a framework for resilient data storage and sharing for MEC in CPS targeting the aforementioned applications. Our framework is built on the resiliency of three main components: EdgeKeeper, which ensures resilient coordination of the framework’s components; RSock, which provides resilient communication among CPS’s nodes; and R-Drive/R-Share which, leveraging EdgeKeeper and RSock, provides resilient data storage and sharing. EdgeKeeper employs a set of replicas and a consensus protocol for storing critical meta-data and ensuring fast reorganization of the CPS; RSock decides an optimal degree for replicating data that is communicated over lossy links. R-Drive employs an adaptive erasure-coded and encrypted resilient data storage; R-Share, leveraging RSock provides resilient peer-to-peer data sharing. We implemented our proposed framework on rapidly deployable systems (e.g., manpacks, testMobile Edge Clouds) and on Android devices, and integrated it with existing MEC applications. Performance evaluation results from three real-world deployments show that our framework provides resilient data storage and sharing in MEC for CPS.

Recent regulatory changes have enabled NCAA student-athletes to profit from their name, image, and likeness (NIL), departing from previous policies requiring those athletes to maintain their amateur status. However, despite the changes, it is unlikely that all the approximately 500,000 NCAA student-athletes will profit from NIL contracts. Within this context, we study how to design a fair and inclusive solution that may help all student-athletes secure NIL financial resources. Following a design science approach, we define design requirements after interviewing student-athletes. Subsequently, we derive three design principles: inclusiveness, fairness, and transparency. Thereafter, we suggest a blockchain-based artifact that satisfies all design principles. Our idea lies in designing collectibles as non-fungible tokens (NFTs) that pay different royalties whenever a transaction (purchase or exchange) happens in different markets (primary or secondary). Finally, we evaluate our solution by discussing its features with current student-athletes.

With the emergence of Artificial Intelligent chatbot tools such as ChatGPT and code writing AI tools such as GitHub Copilot, educators need to question what and how we should teach our courses and curricula in the future. In reality, automated tools may result in certain academic fields being deeply reduced in the number of employable people. In this work, we make a case study of cybersecurity undergrad education by using the lens of “Understanding by Design” (UbD). First, we provide a broad understanding of learning objectives (LOs) in cybersecurity from a computer science perspective. Next, we dig a little deeper into a curriculum with an undergraduate emphasis on cybersecurity and examine the major courses and their LOs for our cybersecurity program at Miami University. With these details, we perform a thought experiment on how attainable the LOs are with the above-described tools, asking the key question “what needs to be enduring concepts?” learned in this process. If an LO becomes something that the existence of automation tools might be able to do, we then ask “what level is attainable for the LO that is not a simple query to the tools?”. With this exercise, we hope to establish an example of how to prompt ChatGPT to accelerate students in their achievements of LOs given the existence of these new AI tools, and our goal is to push all of us to leverage and teach these tools as powerful allies in our quest to improve human existence and knowledge.

In this paper, we show that Hybrid Routing and Prophet protocols in Opportunistic Mobile Networks (OMNs) are vulnerable to theCollusiveHijackattack, in which a malicious attacker, Eve, compromises a set of nodes and lies about their Inter-Contact-Times (ICTs). Eve claims that her nodes meet more frequently than in reality to hijack the routes of legitimate nodes in OMNs. The CollusiveHijack attack enables Eve to launch more severe attacks like packet modification, traffic analysis, and incentive seeking attacks. To identify the CollusiveHijack attack, we propose the Kolmogorov-Smirnov two-sample test to determine whether the statistical distribution of the packets’ delays follows the derived distribution from the ICTs among the nodes. We propose three techniques to detect the CollusiveHijack attack, the Path Detection Technique (PDT), the Hop Detection Technique (HDT), and the Early Hop Detection Technique (EHDT), which trade off compatibility with the Bundle Security Protocol, the detection rate, and the detection latency. We evaluated our techniques through extensive trace-driven simulations and a proof-of-concept system implementation and show that they can detect CollusiveHijack attacks with 80.0% to 99.4% detection rates (when Eve hijacks more than 60 packets) while maintaining a low false positive rate ($\sim$3.6%) and a short detection latency (7-14 hours) for EHDT (75%-85% enhancement compared to PDT and HDT).

In this paper, we present a novel neighbor discovery method for a wireless ad hoc network where each node is equipped with a Free-Space-Optical (FSO) transceivers capable of electronic beam switching. Directional neighbor discovery can be very challenging due to the requirement of strict line-of-sight (LOS) alignment and can result in long and impractical discovery time if the nodes in the network do not have prior information about each other’s location. Our method guarantees that a node can discover all of its neighbors within one 360◦FSO beam sweep. We present a preliminary prototype of a FSO transceiver module capable of electronic beam steering. Through experiments using the developed prototype, we demonstrate that the proposed method helps in discovering neighbor nodes with minimal delay.

Mobile Edge Computing (MEC) is gaining significant interest from first responders and tactical teams. Typical cloud-based coordination (e.g., service discovery and coordination, device naming, authentication) does not work in MEC due to high user mobility. We design and implement EdgeKeeper to provide cloud-like service coordination to distributed edge computing applications for MEC systems. It maintains an edge cluster among devices and intelligently stores data on a group of replicas to guard against node failures/disconnections. We provide a full-system implementation of Edge-Keeper for Android and Linux platforms and evaluate it with MEC applications in a real-world wide-area search and rescue operation conducted by first responders.

In this paper, we present a novel neighbor discovery method for a Free-Space-Optical (FSO) mobile ad hoc network where the nodes do not have any prior information about each other’s location. Each node is equipped with multiple FSO transceivers and a low bit rate long-range (LoRa) omnidirectional communication channel aids in coordinating the neighbor discovery process to synchronize and establish directional FSO links among the nodes. Our proposed method guarantees that a node can discover all of its neighbors within one 360◦FSO beam scan of the surrounding environment. We also present a preliminary prototype of an FSO transceiver module capable of electronic beam steering. Through extensive simulations and real test-bed experiments using the developed prototype, we demonstrate that the proposed method can help achieve significant reductions in discovery time compared to a state-of-the-art protocol.

Directional Radio Frequency (RF) / Free-Space-Optical (FSO) transceivers have the potential to play a significant role in future generation wireless networks. They are advantageous in terms of improved spectrum utilization, higher data transfer rate, and lower probability of interception from unwanted sources. Despite these advantages, communications using directional transceivers require establishment and maintenance of line-of-sight (LOS). Thus, establishment of the communication link or neighbor discovery plays an important role in mobile ad hoc networks with RF/FSO directional transceivers. We consider two nodes (Unmanned Aerial Vehicles (UAVs) or quadcopters) hovering in 3D space, each with one directional transceiver mounted on a mechanically steerable spherical structure/head, with which they can scan 360 degrees in the horizontal plane and 360 degrees in the vertical plane. We propose a novel scheme that deals with the problem of automatic discovery and establishment of LOS alignment between these nodes. We performed extensive simulations to show the effectiveness of the proposed neighbor discovery method. We also developed a proof-of-concept prototype and conducted experiments with it. The results obtained from both simulations and experiments show that, using such mechanically steerable directional transceivers, it is possible to establish communication links to similar neighboring nodes within several seconds without using GPS support.

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.