The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the-art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.

Blockchain technology promises to overcome trust and privacy concerns inherent to centralized information sharing. However, current decentralized supply chain management systems do either not meet privacy and scalability requirements or require a trustworthy consortium, which is challenging for increasingly dynamic supply chains with constantly changing participants. In this paper, we propose CCChain, a scalable and privacy-aware supply chain management system that stores all information locally to give companies complete sovereignty over who accesses their data. Still, tamper protection of all data through a permissionless blockchain enables on-demand tracking and tracing of products as well as reliable information sharing while affording the detection of data inconsistencies. Our evaluation confirms that CCChain offers superior scalability in comparison to alternatives while also enabling near real-time tracking and tracing for many, less complex products.

Blockchain has emerged as a leading technological innovation because of its indisputable safety and services in a distributed setup. Applications of blockchain are rising covering varied fields such as financial transactions, supply chains, maintenance of land records, etc. Supply chain management is a potential area that can immensely benefit from blockchain technology (BCT) along with smart contracts, making supply chain operations more reliable, safer, and trustworthy for all its stakeholders. However, there are numerous challenges such as scalability, coordination, and safety-related issues which are yet to be resolved. Multi-agent systems (MAS) offer a completely new dimension for scalability, cooperation, and coordination in distributed culture. MAS consists of a collection of automated agents who can perform a specific task intelligently in a distributed environment. In this work, an attempt has been made to develop a framework for implementing a multi-agent system for a large-scale product manufacturing supply chain with blockchain technology wherein the agents communicate with each other to monitor and organize supply chain operations. This framework eliminates many of the weaknesses of supply chain management systems. The overall goal is to enhance the performance of SCM in terms of transparency, traceability, trustworthiness, and resilience by using MAS and BCT.

In the context of cybersecurity systems, trust is the firm belief that a system will behave as expected. Trustworthiness is the proven property of a system that is worthy of trust. Therefore, trust is ephemeral, i.e. trust can be broken; trustworthiness is perpetual, i.e. trustworthiness is verified and cannot be broken. The gap between these two concepts is one which is, alarmingly, often overlooked. In fact, the pressure to meet with the pace of operations for mission critical cross domain solution (CDS) development has resulted in a status quo of high-risk, ad hoc solutions. Trustworthiness, proven through formal verification, should be an essential property in any hardware and/or software security system. We have shown, in "vCDS: A Virtualized Cross Domain Solution Architecture", that developing a formally verified CDS is possible. virtual CDS (vCDS) additionally comes with security guarantees, i.e. confidentiality, integrity, and availability, through the use of a formally verified trusted computing base (TCB). In order for a system, defined by an architecture description language (ADL), to be considered trustworthy, the implemented security configuration, i.e. access control and data protection models, must be verified correct. In this paper we present the first and only security auditing tool which seeks to verify the security configuration of a CDS architecture defined through ADL description. This tool is useful in mitigating the risk of existing solutions by ensuring proper security enforcement. Furthermore, when coupled with the agile nature of vCDS, this tool significantly increases the pace of system delivery.

This paper proposes a novel approach for privacy preserving face recognition aimed to formally define a trade-off optimization criterion between data privacy and algorithm accuracy. In our methodology, real world face images are anonymized with Gaussian blurring for privacy preservation. The anonymized images are processed for face detection, face alignment, face representation, and face verification. The proposed methodology has been validated with a set of experiments on a well known dataset and three face recognition classifiers. The results demonstrate the effectiveness of our approach to correctly verify face images with different levels of privacy and results accuracy, and to maximize privacy with the least negative impact on face detection and face verification accuracy.

Python continues to be one of the most popular programming languages and has been used in many safety-critical fields such as medical treatment, autonomous driving systems, and data science. These fields put forward higher security requirements to Python ecosystems. However, existing studies on machine learning systems in Python concentrate on data security, model security and model privacy, and just assume the underlying Python virtual machines (PVMs) are secure and trustworthy. Unfortunately, whether such an assumption really holds is still unknown.This paper presents, to the best of our knowledge, the first and most comprehensive empirical study on the security of CPython, the official and most deployed Python virtual machine. To this end, we first designed and implemented a software prototype dubbed PVMSCAN, then use it to scan the source code of the latest CPython (version 3.10) and other 10 versions (3.0 to 3.9), which consists of 3,838,606 lines of source code. Empirical results give relevant findings and insights towards the security of Python virtual machines, such as: 1) CPython virtual machines are still vulnerable, for example, PVMSCAN detected 239 vulnerabilities in version 3.10, including 55 null dereferences, 86 uninitialized variables and 98 dead stores; Python/C API-related vulnerabilities are very common and have become one of the most severe threats to the security of PVMs: for example, 70 Python/C API-related vulnerabilities are identified in CPython 3.10; 3) the overall quality of the code remained stable during the evolution of Python VMs with vulnerabilities per thousand line (VPTL) to be 0.50; and 4) automatic vulnerability rectification is effective: 166 out of 239 (69.46%) vulnerabilities can be rectified by a simple yet effective syntax-directed heuristics.We have reported our empirical results to the developers of CPython, and they have acknowledged us and already confirmed and fixed 2 bugs (as of this writing) while others are still being analyzed. This study not only demonstrates the effectiveness of our approach, but also highlights the need to improve the reliability of infrastructures like Python virtual machines by leveraging state-of-the-art security techniques and tools.

Active consumers have now been empowered thanks to the smart grid concept. To avoid fossil fuels, the demand side must provide flexibility through Demand Response events. However, selecting the proper participants for an event can be complex due to response uncertainty. The authors design a Contextual Consumer Rate to identify the trustworthy participants according to previous performances. In the present case study, the authors address the problem of new players with no information. In this way, two different methods were compared to predict their rate. Besides, the authors also refer to the consumer privacy testing of the dataset with and without information that could lead to the participant identification. The results found to prove that, for the proposed methodology, private information does not have a high impact to attribute a rate.

Security is a key concern across the world, and it has been a common thread for all critical sectors. Nowadays, it may be stated that security is a backbone that is absolutely necessary for personal safety. The most important requirements of security systems for individuals are protection against theft and trespassing. CCTV cameras are often employed for security purposes. The biggest disadvantage of CCTV cameras is their high cost and the need for a trustworthy individual to monitor them. As a result, a solution that is both easy and cost-effective, as well as secure has been devised. The smart door lock is built on Raspberry Pi technology, and it works by capturing a picture through the Pi Camera module, detecting a visitor's face, and then allowing them to enter. Local binary pattern approach is used for Face recognition. Remote picture viewing, notification, on mobile device are all possible with an IOT based application. The proposed system may be installed at front doors, lockers, offices, and other locations where security is required. The proposed system has an accuracy of 89%, with an average processing time is 20 seconds for the overall process.

Cloud computing is a model of service provisioning in heterogeneous distributed systems that encourages many researchers to explore its benefits and drawbacks in executing workflow applications. Recently, high-quality security protection has been a new challenge in workflow allocation. Different tasks may and may not have varied security demands, security overhead may vary for different virtual machines (VMs) at which the task is assigned. This paper proposes a Security Oriented Deadline-Aware workflow allocation (SODA) strategy in an IaaS cloud environment to minimize the risk probability of the workflow tasks while considering the deadline met in a deterministic environment. SODA picks out the task based on the highest security upward rank and assigns the selected task to the trustworthy VMs. SODA tries to simultaneously satisfy each task’s security demand and deadline at the maximum possible level. The simulation studies show that SODA outperforms the HEFT strategy on account of the risk probability of the cloud system on scientific workflow, namely CyberShake.

This paper presents a physically-secure wireless communication system utilizing orbital angular momentum (OAM) waves at 0.31THz. A trustworthy key distribution mechanism for symmetric key cryptography is proposed by exploiting random hopping among the orthogonal OAM-wave modes and phases. Keccak-f[400] based pseudorandom number generator provides randomness to phase distribution of OAM-wave modes for additional security. We assess the security vulnerabilities of using OAM modulation in a THz communication system under various physical-layer threat models as well as analyze the effectiveness of these threat models for varying attacker complexity levels under different conditions.

Ubiquitous environment embedded with artificial intelligent consist of heterogenous smart devices communicating each other in several context for the computation of requirements. In such environment the trust among the smart users have taken as the challenge to provide the secure environment during the communication in the ubiquitous region. To provide the secure trusted environment for the users of ubiquitous system proposed approach aims to extract behavior of smart invisible entities by retrieving their behavior of communication in the network and applying the recommendation-based filters using Deep learning (RBF-DL). The proposed model adopts deep learning-based classifier to classify the unfair recommendation with fair ones to have a trustworthy ubiquitous system. The capability of proposed model is analyzed and validated by considering different attacks and additional feature of instances in comparison with generic recommendation systems.

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Federated learning and decentralized learning systems address this by letting the data stay on user's machines to preserve privacy: each user performs the training on local data and only the model parameters are shared. However, sharing the model parameters across the network may still yield privacy breaches. In this paper, we present Rex, the first enclave-based decentralized CF recommender. Rex exploits Trusted execution environments (TEE), such as Intel software guard extensions (SGX), that provide shielded environments within the processor to improve convergence while preserving privacy. Firstly, Rex enables raw data sharing, which ultimately speeds up convergence and reduces the network load. Secondly, Rex fully preserves privacy. We analyze the impact of raw data sharing in both deep neural network (DNN) and matrix factorization (MF) recommenders and showcase the benefits of trusted environments in a full-fledged implementation of Rex. Our experimental results demonstrate that through raw data sharing, Rex significantly decreases the training time by 18.3 x and the network load by 2 orders of magnitude over standard decentralized approaches that share only parameters, while fully protecting privacy by leveraging trustworthy hardware enclaves with very little overhead.

The healthcare industry is confronted with a slew of significant challenges, including stringent regulations, privacy concerns, and rapidly rising costs. Many leaders and healthcare professionals are looking to new technology and informatics to expand more intelligent forms of healthcare delivery. Numerous technologies have advanced during the last few decades. Over the past few decades, pharmacy has changed and grown, concentrating less on drugs and more on patients. Pharmaceutical services improve healthcare's affordability and security. The primary invention was a cyber-infrastructure made up of smart gadgets that are connected to and communicate with one another. These cyber infrastructures have a number of problems, including privacy, trust, and security. These gadgets create cyber-physical systems for pharmaceutical care services in p-health. In the present period, cyber-physical systems for pharmaceutical care services are dealing with a variety of important concerns and demanding conditions, i.e., problems and obstacles that need be overcome to create a trustworthy and effective medical system. This essay offers a thorough examination of CPS's architectural difficulties and emerging tendencies.

Cyber threats can cause severe damage to computing infrastructure and systems as well as data breaches that make sensitive data vulnerable to attackers and adversaries. It is therefore imperative to discover those threats and stop them before bad actors penetrating into the information systems.Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. The accuracy of the AI/ML algorithms could be affected by many factors, from algorithm, data, to prejudicial, or even intentional. As a result, AI/ML applications need to be non-biased and trustworthy.In this research, we developed a machine learning-based cyber threat detection and assessment tool. It uses two-stage (both unsupervised and supervised learning) analyzing method on 822,226 log data recorded from a web server on AWS cloud. The results show the algorithm has the ability to identify the threats with high confidence.