The security of AI systems becomes increasingly paramount, as artificial intelligence (AI) pervades various aspects of our lives. For instance, from autonomous driving to banking and medical diagnostics, AI is playing a pivotal role in powering such security-sensitive systems. However, attacks on these high-risk AI systems could have severe consequences, jeopardizing the lives, finances, and well-being of people. Thus, adherence to fundamental security properties, namely, Confidentiality, Integrity, and Availability (CIA), is essential for AI systems to comply with security standards. In this paper, we first define a simplified abstraction of AI systems to discuss four independent viewpoints: data, models, inputs, and deployment. Subsequently, we conduct a detailed analysis of attack vectors targeting each of these viewpoints, with a rigorous assessment of CIA properties. Proactive identification of attack vectors throughout the entire AI lifecycle is a requisite step toward establishing a secure-by-design framework for AI systems.

Demand for autonomous protection in computing devices can not go unnoticed with a cataclysmic rise in cyber-attacks. Consequently, cybersecurity measures with an improved generalization that can proactively determine the indicators of compromises to predict zero-day threats or previously unseen malware together with known malware are highly desirable. In this article, we present a novel concept of autonomous device protection based on behavioral profiling by continuously monitoring internal resource usage and exploiting a large language model to distinguish between benign and malicious behavior. We design and develop a proof-of-concept for Windows-based computing devices relying on a built-in event tracing mechanism for log collection that is converted into structured data using a graph data structure. We extract graph-level features, \textit{i.e., graph depth, nodes count, number of leaf nodes, node degree statistics, and events count}, and node-level features, i.e., process start, file create and registry events details for each graph. Further, we exploit a pre-trained large language network - a simple contrastive sentence embedding framework to extract strong features, i.e., dense vectors, from event graphs. Finally, we train a random forest classifier using both the graph- and node-level features to obtain classification models that are evaluated on a collected dataset containing one thousand benign and malicious samples achieving accuracy up to 99.25%.

Connected and autonomous vehicles (CAVs) can fulfill the emerging demand for smart transportation on a global scale. Such innovations for transportation can bring manyfold benefits from fully autonomous driving services to proactive vehicle monitoring and traffic management. However, given the complexity involved in the deployment of CAVs, zero-tolerance safety, and security measures must be incorporated to avert vehicle immobilization, road accidents, disclosure of sensitive data, or any potential threats. In this article, we present a reference architecture of CAVs to investigate existing and emerging cyber threats and thus, derive a common attack taxonomy for a CAVs ecosystem based on our studies of academic literature and industry white papers. After that, we discuss security mechanisms for the CAVs ecosystem that can be useful for the safe and secure transportation of passengers from one destination to another. Our work can provide insights to security engineers and system architects for investigating security problems using a top-to-bottom approach and subsequently, identifying optimal security solutions for CAVs.

Wireless internet are among the fastest-growing segment of the communication industry. It is absolutely necessitated for future wireless internet to reach all terrestrial and aerial spaces covering densely populated areas to entire geographical regions across the globe to bring the cyber and the physical world further closer. The International Telecommunication Union (ITU) recommended that wireless internet research not only considers the technical aspects but also be able to contemplate economic development and societal needs. In this article, we first cover the pivotal technological aspects of 6G and satellite internet for rendering seamless wireless connectivity that can be reliable, affordable, and scalable for emerging use cases. Afterward, we discuss the impact of future wireless communications on economic growth and social benefits aspects.

The proliferation of the Internet of Things (IoT) devices and advances in their computing capabilities give an impetus to the Edge Computing (EC) paradigm that can facilitate localize computing and data storage. As a result, limitations like network connectivity issues, data mobility constraints, and real-time processing delays, in Cloud computing can be addressed more efficiently. EC can create a lot of opportunities across the breadth of the IT domains and cyber-physical systems. Several studies have been conducted describing EC general requirements, challenges, and issues. However, considering the complexity involved in the EC paradigm, non-functional requirements (NFRs) are equally important as functional requirements, to be thoroughly investigated. This paper discusses NFRs, namely, performance, reliability, scalability, and security that can assist in maturing the EC paradigm. To accomplish the objective, available case studies and the state-of-the-art related to non-functional requirements, real-world issues, and challenges concerning EC are reviewed. Ultimately, the paper anatomizes the aforementioned NFRs leveraging the six-part scenario form of source-stimulus-artifact-environment-response-response measure to assert Quality of Service (QoS) in EC.

The proliferation of Internet of Things (IoT) systems is having a profound impact across all aspects of life.  Recognising and identifying particular users is central to delivering the personalised experience that citizens want to experience, and that organisations wish to deliver.  This article presents a survey of human-computer interaction-based (HCI-based) and natural habits-based behavioral biometrics that can be acquired unobtrusively through smart devices or IoT sensors for user recognition purposes. Robust and usable user recognition is also a security requirement for emerging IoT ecosystems to protect them from adversaries. Typically, it can be specified as a fundamental building block for most types of human-to-things accountability principles and access-control methods. However, end-users are facing numerous security and usability challenges in using currently available knowledge- and token-based recognition (i.e., authentication and identification) schemes. To address the limitations of conventional recognition schemes, biometrics, naturally come as a first choice to supporting sophisticated user recognition solutions. We perform a comprehensive review of touch-stroke, swipe, touch signature, hand-movements, voice, gait and footstep behavioral biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioral biometrics  with a goal to identify their attributes and features for generating unique identification signatures. Finally, we present security, privacy, and usability evaluations that can strengthen the designing of robust and usable user recognition schemes for IoT applications.

A number of research in Information and Communication Technology (ICT) have shown that usability is an important goal for software-based interactive systems and products for a wider acceptance by their end-users. To evaluate the usability of a system under design, usability evaluation methods predominantly rely on subject matter experts or testers’ assessments. However, integrating the Artificial Intelligence (AI) technology and existing usability evaluation methods’ processes can aid to evolve more effective user-centered designs. In this paper, we conceptualize an AI-augmented usability evaluation framework (AIUEF) that aims at replacing ‘end-users’ with ‘personas’ to evaluate requirements involving human-computer interaction (HCI) for a given Software Requirements Specification (SRS). We present a blueprint of AIUEF that is the first step to furnishing a theoretical basis for automated usability evaluation of HCI requirements for a given SRS with an aim to improve the usability of a system under design.