Industry 5.0 represents a paradigm shift toward human–AI collaboration in manufacturing, incorporating unprecedented volumes of robots, Internet of Things (IoT) devices, Augmented/Virtual Reality (AR/VR) systems, and smart devices. This extensive interconnectivity introduces significant cybersecurity vulnerabilities. While AI has proven effective for cybersecurity applications, including intrusion detection, malware identification, and phishing prevention, cybersecurity professionals have shown reluctance toward adopting black-box machine learning solutions due to their opacity. This hesitation has accelerated the development of explainable artificial intelligence (XAI) techniques that provide transparency into AI decision-making processes. This systematic review examines XAI-based intrusion detection systems (IDSs) for Industry 5.0 environments. We analyze how explainability impacts cybersecurity through the critical lens of adversarial XAI (Adv-XIDS) approaches. Our comprehensive analysis of 135 studies investigates XAI’s influence on both advanced deep learning and traditional shallow architectures for intrusion detection. We identify key challenges, opportunities, and research directions for implementing trustworthy XAI-based cybersecurity solutions in high-stakes Industry 5.0 applications. This rigorous analysis establishes a foundational framework to guide future research in this rapidly evolving domain.

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks are not operational due to natural disasters. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates for the users. Prior studies on UAV-based wireless coverage typically consider an air-to-ground path loss model, which assumes that the users are outdoor and located on a 2-D plane. In this paper, we propose using UAVs to provide wireless coverage for indoor users inside a high-rise building. First, we present realistic outdoor–indoor path loss models and describe the tradeoff introduced by these models. Then, we study the problem of efficient placement of a single UAV, where the objective is to minimize the total transmit power required to cover the entire high-rise building. The formulated problem is generally difficult to solve. To that end, we consider three cases of practical interest and provide efficient solutions to the formulated problem under these cases. Then, we study the problem of minimizing the number of UAVs required to provide wireless coverage to the high-rise building and prove that this problem is NP-complete. Due to the intractability of the problem, we use clustering to minimize the number of UAVs required to cover the indoor users. In our proposed algorithm, we check if the maximum transmit power of a UAV is sufficient to cover each cluster. If not, the number of clusters is incremented by one, and the problem is solved again. In the uniform split method, we split the building into $k$ regular structures and utilize $k$ UAVs to provide wireless coverage for indoor users regardless of user distribution. We demonstrate through simulations that the method that splits the building into regular structures requires 80% more number of UAVs relative to our proposed algorithm.

The capability of generating high-quality source code using large language models (LLMs) reduces software development time and costs. However, recent literature and our empirical investigation in this work show that while LLMs can generate functioning code, they inherently tend to introduce security vulnerabilities, limiting their potential. This problem is mainly due to their training on massive open-source corpora exhibiting insecure and inefficient programming practices. Therefore, automatic optimization of LLM prompts for generating secure and functioning code is a demanding need. This paper introduces PromSec, an algorithm for prompt optimization for secure and functioning code generation using LLMs. In PromSec, we combine 1) code vulnerability clearing using a generative adversarial graph neural network, dubbed as gGAN, to fix and reduce security vulnerabilities in generated codes and 2) code generation using an LLM into an interactive loop, such that the outcome of the gGAN drives the LLM with enhanced prompts to generate secure codes while preserving their functionality. Introducing a new contrastive learning approach in gGAN, we formulate the code-clearing and generation loop as a dual-objective optimization problem, enabling PromSec to notably reduce the number of LLM inferences. As a result, PromSec becomes a cost-effective and practical solution for generating secure and functioning codes. Extensive experiments conducted on Python and Java code datasets confirm that PromSec effectively enhances code security while upholding its intended functionality. Our experiments show that despite the comprehensive application of a state-of-the-art approach, it falls short in addressing all vulnerabilities within the code, whereas PromSec effectively resolves each of them. Moreover, PromSec achieves more than an order-of-magnitude reduction in operational time, number of LLM queries, and security analysis costs. Furthermore, prompts optimized with PromSec for a certain LLM are transferable to other LLMs across programming languages and generalizable to unseen vulnerabilities in training. This study presents an essential step towards improving the trustworthiness of LLMs for secure and functioning code generation, significantly enhancing their large-scale integration in real-world software code development practices.

Visible light communication (VLC)-based indoor localization approaches enjoy many advantages, such as utilizing ubiquitous lighting infrastructure, high location accuracy, and no interruption to RF-based devices. However, existing VLC-based localization methods lack a real-time backward channel from the device to landmarks and necessitate computation at the device, which make them unsuitable for real-time tracking of small IoT devices. In this paper, we propose and prototype RETRO, that establishes an almost zero-delay backward channel by retroreflection. RETRO localizes passive IoT devices without requiring computation and heavy sensing (e.g., camera) at the devices. Multiple photodiodes (i.e., landmarks) are mounted on any single unmodified light source to sense the retroreflected optical signal (i.e., location signature). We derive a closed-form expression, which is validated by experiments and ray tracing simulations, for the reflected optical power relative to the location and the orientation of the retroreflector. The expression is applied to a received signal strength indicator and trilateration based localization algorithm. Extensive experiments demonstrate centimeter-level location accuracy and single-digit angular error. For practicality concern, to mitigate the thickness problem of a single retroreflector, the capabilities of different retroreflector arrays are studied. The range of the localization system is theoretically evaluated for different light emission patterns.

Hadoop is an open source software framework for storage and processing of large-scale datasets. The proliferation of cloud services and its corresponding increasing number of users lead to a larger attack surface, especially for internal threats. Therefore, in corporate data centers, it is essential to ensure the security, authenticity, and integrity of all the entities of Hadoop. The current secure implementations of Hadoop mainly utilize Kerberos, which is known to suffer from many security and performance issues, including the concentration of authentication credentials, single point of failure, and online availability. Most importantly, these Kerberos-based implementations do not guard against insider threats. In this paper, we propose an authentication framework for Hadoop that utilizes trusted platform module technology. The proposed approach provides significant security guarantees against insider threats, which manipulate the execution environment without the consent of legitimate clients. We have conducted extensive experiments to validate the performance and the security properties of our approach. The results demonstrate that the proposed approach alleviates many of the shortcomings of Kerberos-based state-of-the-art protocols and provides unique security guarantees with acceptable overhead. Moreover, we have formally proved the correctness and the security guarantees of our protocol via Burrows–Abadi–Needham logic.

Many web computing systems are running real time database services where their information change continuously and expand incrementally. In this context, web data services have a major role and draw significant improvements in monitoring and controlling the information truthfulness and data propagation. Currently, web telemedicine database services are of central importance to distributed systems. However, the increasing complexity and the rapid growth of the real world healthcare challenging applications make it hard to induce the database administrative staff. In this paper, we build an integrated web data services that satisfy fast response time for large scale Tele-health database management systems. Our focus will be on database management with application scenarios in dynamic telemedicine systems to increase care admissions and decrease care difficulties such as distance, travel, and time limitations. We propose three-fold approach based on data fragmentation, database websites clustering and intelligent data distribution. This approach reduces the amount of data migrated between websites during applications' execution; achieves cost-effective communications during applications' processing and improves applications' response time and throughput. The proposed approach is validated internally by measuring the impact of using our computing services' techniques on various performance features like communications cost, response time, and throughput. The external validation is achieved by comparing the performance of our approach to that of other techniques in the literature. The results show that our integrated approach significantly improves the performance of web database systems and outperforms its counterparts.