Insider threats come from people who have authorized access, hence they are harder to detect using traditional rule-based systems. This makes them one of the toughest cybersecurity challenges. This study investigates artificial intelligence (AI) and machine learning (ML) to create a more flexible framework. The study also discusses Explainable AI (XAI) methods, which helps analysts understand anomalies better by giving clear human-readable explanations. The suggested hybrid model combines anomaly detection and behavioral analytics to instantly find suspicious user activity and to learn typical behavioral patterns, it uses supervised and unsupervised techniques like auto-encoders and Isolation Forests classifiers. Hybrid learning models improve detection accuracy, lower false positives, and adjust to changing conditions. The proposed model is evaluated on the CERT Insider Threat Dataset (CMU-SEI), which simulates realistic insider behaviours. This research contributes to creating proactive, transparent, and intelligent defense mechanisms, shifting cybersecurity from a reactive approach to a preventive and adaptive strategy for protecting critical digital infrastructures.
A random or pseudorandom sequence of bits is important and used in cryptography, simulations, and mathematics. These values are created using a random or pseudorandom binary generator. It is essential that these random strings have a high period and high linear complexity, and that they pass statistical randomness tests to ensure their effectiveness. A generator must be developed taking all these factors into account. Each step of the process must be carefully controlled to guarantee a good final result. To ensure that the pseudorandom binary generator is sufficiently robust, cryptographic components must be combined correctly. This project demonstrates how to create a reliable pseudorandom binary generator. The most important parts of the generator described in this presentation are the New Self-Shrinking Generators (NSSGs). These are combined using Threshold Generators in one case and Geffe Generators in another.
Non-Fungible Tokens (NFTs) are widely adopted across digital art, gaming, and decentralized applications. However, existing NFT marketplaces largely treat NFTs as static assets with limited interactivity and narrative depth. This paper presents Demon Forge Hub, a decentralized NFT marketplace designed to address these limitations through on-chain gamification, structured storytelling, and improved accessibility. Built using ERC-721 smart contracts deployed on the Polygon Mumbai network, the platform enables NFTs to function as evolving digital characters with persistent attributes such as levels, rarity, and battle outcomes. Metadata is stored using NFT.Storage and IPFS to ensure verifiable persistence. Additionally, an AI-assisted lore generation module produces structured narrative identities for each NFT. The proposed system demonstrates how NFT marketplaces can transition toward interactive, narrative-driven digital ecosystems.
As the demand for edge computing grows, there is a critical need for learning mechanisms that offer both energy efficiency and temporal predictability. In this study, we present a single-board implementation of a Coupled Learning Loop Network (CLLN), a nonlinear analogue architecture based on self-adjusting resistive elements. Unlike traditional von Neumann systems, this architecture learns via physical relaxation processes rather than gradient descent algorithms. We constructed a 16-edge network integrated with an FPGA to manage reconfigurable inputs and verified its operation through a sine waveform regression task, demonstrating rapid convergence. To validate scalability and performance on high-dimensional tasks, we developed a physics-informed digital twin of the system and benchmarked it against the MNIST dataset using CPU, GPU, and TPU architectures. While specialised accelerators (TPUs) running TensorFlow Lite demonstrated highly reduced latency, our analysis reveals that digital frameworks remain subject to latency jitter (up to 110 s standard deviation on standard processors) due to discrete memory access and operating system overheads. In contrast, the analogue network exhibits high temporal determinism, with settling times governed strictly by RC time constants. Combined with a measured power envelope of approximately 0.45 W, this intrinsic stability and efficiency suggest that FPGA-driven nonlinear analogue networks offer a robust solution for latency-critical and energy-constrained edge applications.
Residue Class (RC)-based peer-to-peer (P2P) networks employ structured group-head overlays to achieve scalable, locality-aware routing; however, the reliance on intermediate group heads exposes the forwarding process to insider attacks. A malicious group head can modify, replace, or forge forwarded data while remaining undetected, undermining end-to-end integrity. This paper proposes a Non-Interactive Zero-Knowledge Proof (NIZKP)–based forwarding mechanism that enforces cryptographically verifiable hop-by-hop integrity in RC-based P2P networks. Each group head attaches a NIZKP proving that the outgoing ciphertext is a correct re-encryption of the same plaintext received, while preserving a Pedersen commitment to the file hash. We formally define the forwarding relation, construct a Sigma protocol and its Fiat–Shamir-based non-interactive instantiation, and analyze the resulting completeness, soundness, and zero-knowledge guarantees. We evaluate the proposed mechanism using lightweight simulations over a ten–group-head backbone with forwarding paths of varying lengths. Results show that proof generation incurs an average overhead of approximately 0.12 ms per hop, while proof verification averages 0.10 ms per hop, leading to near-linear end-to-end forwarding latency of 0.19 ms (2 hops), 0.29 ms (3 hops), and 0.37 ms (4 hops). In adversarial scenarios, all tampering attempts are detected at the next honest hop, yielding a 0
