Evidence is a tangible demonstrative artifact that proves a fact and shapes the investigation of various misconduct cases involving for instance corruption, misbehavior, or violation. It is imperative to maintain proper evidence management to guarantee the admissibility of an evidence in a court of law. Chain of custody forms the forensic link of evidence sequence of control, transfer, and analysis to preserve evidence's integrity and to prevent its contamination. Blockchain, a distributed tamper-resistant ledger can be leveraged to offer a decentralized secure digital evidence system. In this paper, we propose a secure chain of custody framework by utilizing the blockchain technology to store evidence metadata while the evidence is stored in a reliable storage medium. The framework is built on top of a private Ethereum blockchain to document every transmission from the moment the evidence is seized, thus ensuring that evidence can only be accessed or possessed by authorized parties. The framework is integrated with the digital evidence system where evidence is physically stored and locked using smart locks. To secure the sequence of evidence submission and retrieval, only an authorized party can possess the key to unlock the evidence. Our proposed framework offers a secure solution that maintains evidence integrity and admissibility among multiple stakeholders such as law enforcement agencies, lawyers, and forensic professionals. The research findings shed light on hidden opportunities for the efficient usage of blockchain in other realms beyond finance and cryptocurrencies.

While the usage of Artificial Intelligence and Machine Learning Software (AI/MLS) in defensive cybersecurity has received considerable attention, there remains a noticeable research gap on their offensive use. This paper reviews the defensive usage of AI/MLS in cybersecurity and then presents a survey of its offensive use. Inspired by the System-Fault-Risk (SFR) framework, we categorize AI/MLS-powered cyberattacks by their actions into seven categories. We cover a wide spectrum of attack vectors, discuss their practical implications and provide some recommendations for future research.

Summary The rapid proliferation of Internet of things (IoT) devices, such as smart meters and water valves, into industrial critical infrastructures and control systems has put stringent performance and scalability requirements on modern Supervisory Control and Data Acquisition (SCADA) systems. While cloud computing has enabled modern SCADA systems to cope with the increasing amount of data generated by sensors, actuators, and control devices, there has been a growing interest recently to deploy edge data centers in fog architectures to secure low‐latency and enhanced security for mission‐critical data. However, fog security and privacy for SCADA‐based IoT critical infrastructures remains an under‐researched area. To address this challenge, this contribution proposes a novel security “toolbox” to reinforce the integrity, security, and privacy of SCADA‐based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature: a cryptographic‐based access approach to the cloud services using identity‐based cryptography and signature schemes at the fog layer. We present the implementation details of a prototype for our proposed secure fog‐based platform and provide performance evaluation results to demonstrate the appropriateness of the proposed platform in a real‐world scenario. These results can pave the way toward the development of a more secure and trusted SCADA‐based IoT critical infrastructure, which is essential to counter cyber threats against next‐generation critical infrastructure and industrial control systems. The results from the experiments demonstrate a superior performance of the secure fog‐based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform.

Radio Frequency Identification (RFID) is an enabling technology that can provide organizations with unprecedented improved visibility and traceability of items throughout their journey in the value chain. As RFID deployments are scaling-up from pilot projects and proof-of-concept trials towards fully-fledged enterprise applications, RFID system management challenges will escalate. This paper takes an in-depth look at the management aspects of RFID systems. The current state-of-the art of RFID systems management is exposed, whereby various approaches are discussed under five functional areas, namely configuration, fault, performance, accounting and security management. The paper also highlights some future trends and open research areas that can potentially trigger further interests and investigations in this important topic.

The application of neural networks to the optimum routing problem in packet-switched computer networks, where the goal is to minimize the network-wide average time delay, is addressed. Under appropriate assumptions, the optimum routing algorithm relies heavily on shortest path computations that have to be carried out in real time. For this purpose an efficient neural network shortest path algorithm that is an improved version of previously suggested Hopfield models is proposed. The general principles involved in the design of the proposed neural network are discussed in detail. Its computational power is demonstrated through computer simulations. One of the main features of the proposed model is that it will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.