Machine learning (ML) represents one of the main pillars of the current digital era, specifically in modern real-world applications. The Internet of Things (IoT) technology is foundational in developing advanced intelligent systems. The convergence of ML and IoT drives significant advancements across various domains, such as making IoT-based security systems smarter and more efficient. However, ML-based IoT systems are vulnerable to lurking attacks during the training and testing phases. An adversarial attack aims to corrupt the ML model\'s functionality by introducing perturbed inputs. Consequently, it can pose significant risks leading to devices\' malfunction, services\' interruption, and personal data misuse. This article examines the severity of adversarial attacks and accentuates the importance of designing secure and robust ML models in the IoT context. A comprehensive classification of adversarial machine learning (AML) is provided. Moreover, a systematic literature review of the latest research trends (from 2020 to 2024) of the intersection of AML and IoT-based security systems is presented. The results revealed the availability of various AML attack techniques, where the Fast Gradient Signed Method (FGSM) is the most employed. Several studies recommend the adversarial training technique to defend against such attacks. Finally, potential open issues and main research directions are highlighted for future consideration and enhancement.

While the second wave of the Covid-19 pandemic is keeping the world on tenterhooks, the last few months have also led to a new wave of cybercrime. The following article analyzes the background and manifestations of pandemic-related cybercrimes and shows how our criminal law systems are able to deal with current challenges in the age of the coronavirus.

The high-level integration and interaction between the information flow at the cyber layer and the physical subjects at the vehicular layer enables the connected automated vehicles (CAVs) to achieve rapid, cooperative and shared travel. However, the cyber layer is challenged by malicious attacks and the shortage of communication resources, which makes the vehicular layer suffer from system nonlinearity, disturbance randomness and behavior uncertainty, thus interfering with the stable operation of the platoon. So far, scholars usually adopt the method of assuming or improving the car-following model to explore the platoon behavior and the defense mechanism in cyberattacks, but they have not considered whether the model itself has disturbance and impact on cyberattack defenses. In other words, it is still being determined whether the car-following model designed can be fully applicable to such cyberattacks. To provide a theoretical basis for vehicular layer modeling, it is necessary to comprehend the self-resistance of different car-following models faced on various cyberattacks. First, we review the car-following models adopted on the vehicular layer in cyberattacks, involving traffic engineering, physical statistics, and platoon dynamics. Based on the review, we divide the malicious attacks faced by the cyber layer into explicit attacks and implicit attacks. Second, we develop a cooperative generalized force model (CGFM), which combines and unifies the r-predecessors following communication topology. The proposed models, labeled the vulnerable cooperative intelligent driver model (VCIDM), the vulnerable cooperative optimal velocity model (VCOVM), and the vulnerable cooperative platoon dynamics model (VCPDM), incorporate the CGFM model and assorted cyberattack injection modes to explain the cyberattack effects on the platoon self-resistance capability. Upon the described models, we provide six indicators in three dimensions from the basic traffic element, including drivers, vehicles, and environment. These indicators illustrate driver tolerance, vehicle adaptability, and environmental resistance when a platoon faces attacks such as bogus information, replay/delay, and communication interruption. We arrange and reorganize the car-following models and the cyberattack injection modes to complete the research on the self-resistance capability of the platoon, which has positive research value and practical significance for enhancing the endogenous security at the vehicular layer and improving the intrusion tolerability at the cyber layer.

An act of cyberterrorism involves using the internet and other forms of information and communication technology to threaten or cause bodily harm to gain political or ideological power through threat or intimidation. Data theft, data manipulation, and disruption of essential services are all forms of cyberattacks. As digital infrastructure becomes more critical and entry barriers for malicious actors decrease, cyberterrorism has become a growing concern. Detecting, responding, and preventing this crime presents unique challenges for law enforcement and governments, which require a multifaceted approach. Cyberterrorism can have devastating effects on a wide range of people and organizations. A country\'s reputation and stability can be damaged, financial losses can occur, and in some cases, even lives can be lost. As a result of cyberattacks, critical infrastructure, such as power grids, hospitals, and transportation systems, can also be disrupted, leading to widespread disruptions and distress. The past ten years have seen several cyber-attacks around the globe including WannaCry attack (2017), Yahoo data breaches (2013-2014), OPM data breach (2015), SolarWinds supply chain attack (2020) etc. This study covers some of the cyberterrorism events that have happened in the past ten years, their target countries, their devastating effects, their impacts on nation\'s economy, political instability, and measures adopted to counter them over the passage of time. Our survey-based research on cyberterrorism will complement existing literature by providing valuable empirical data, understanding of perceptions and awareness, and insights into targeted populations. It can contribute to the development of better measurement tools, strategies, and policies for countering cyberterrorism.

The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website\'s content as a technical resource and reference.

iBeacon systems have been increasingly established in public areas to assist users in terms of indoor location navigation and positioning. People receive the services through the Bluetooth Low Energy (BLE) installed on their mobile phones. However, the positioning and navigation functions of an iBeacon system may be compromised when faced with cyberattacks issued by hackers. In other words, its security needs to be further considered and enhanced. This study took the iBeacon system of Taipei Main Station, the major transportation hub with daily traffic of at least three hundred thousand passengers, as an example for exploring its potential attacks and further studying the defense technologies, with the assistance of AI techniques and human participation. Our experiments demonstrate that in the early stage of iBeacon system information security planning, information security technology and a rolling coding encryption should be included, representing the best defense methods at present. In addition, we believe that the adoption of rolling coding is the most cost-effective defense. However, if the security of critical infrastructure is involved, the most secure defense method should be adopted, namely a predictable and encrypted rolling coding method.

This research study mainly focused on the dynamic malware detection. Malware progressively changes, leading to the use of dynamic malware detection techniques in this research study. Each day brings a new influx of malicious software programmes that pose a threat to online safety by exploiting vulnerabilities in the Internet. The proliferation of harmful software has rendered manual heuristic examination of malware analysis ineffective. Automatic behaviour-based malware detection using machine learning algorithms is thus considered a game-changing innovation. Threats are automatically evaluated based on their behaviours in a simulated environment, and reports are created. These records are converted into sparse vector models for use in further machine learning efforts. Classifiers used to synthesise the results of this study included kNN, DT, RF, AdaBoost, SGD, extra trees and the Gaussian NB classifier. After reviewing the test and experimental data for all five classifiers, we found that the RF, SGD, extra trees and Gaussian NB Classifier all achieved a 100% accuracy in the test, as well as a perfect precision (1.00), a good recall (1.00), and a good f1-score (1.00). Therefore, it is reasonable to assume that the proof-of-concept employing autonomous behaviour-based malware analysis and machine learning methodologies might identify malware effectively and rapidly.

Cyber-physical systems (CPSs) are monitored and controlled by a computing and communicating core. This cyber layer enables better management of the controlled subsystem, but it also introduces threats to the security and protection of CPSs, as demonstrated by recent cyberattacks. The resulting governance and policy emphasis on cybersecurity is reflected in the academia by a vast body of literature. In this article, we systematize existing knowledge on CPS analysis. Specifically, we focus on the quantitative assessment of CPSs before and after the occurrence of a disruption. Through the systematic analysis of the models and methods adopted in the literature, we develop a CPS resilience assessment framework consisting of three steps, namely, (1) CPS description, (2) disruption scenario identification, and (3) resilience strategy selection. For each step of the framework, we suggest established methods for CPS analysis and suggest four criteria for method selection. The framework proposes a standardized workflow to assess the resilience of CPSs before and after the occurrence of a disruption. The application of the proposed framework is exemplified with reference to a power substation and associated communication network.The case study shows that the proposed framework supports resilience decision making by quantifying the effects of the implementation of resilience strategies.

Connected and automated vehicles (CAVs) present significant potential for improving road safety and mitigating traffic congestion for the future mobility system. However, cooperative driving vehicles are more vulnerable to cyberattacks when communicating with each other, which will introduce a new threat to the transportation system. In order to guarantee safety aspects, it is also necessary to ensure a high level of information quality for CAV. To the best of our knowledge, this is the first investigation on the impacts of cyberattacks on CAV in mixed traffic (large vehicles, medium vehicles, and small vehicles) from the perspective of vehicle dynamics. The paper aims to explore the influence of cyberattacks on the evolution of CAV mixed traffic flow and propose a resilient and robust control strategy (RRCS) to alleviate the threat of cyberattacks. First, we propose a CAV mixed traffic car-following model considering cyberattacks based on the Intelligent Driver Model (IDM). Furthermore, a RRCS for cyberattacks is developed by setting the acceleration control switch and its impacts on the mixed traffic flow are explored in different cyberattack types. Finally, sensitivity analyses are conducted in different platoon compositions, vehicle distributions, and cyberattack intensities. The results show that the proposed RRCS of cyberattacks is robust and can resist the negative threats of cyberattacks on the CAV platoon, thereby providing a theoretical basis for restoring the stability and improving the safety of the CAV.

Although cyber technologies benefit our society, there are also some related cybersecurity risks. For example, cybercriminals may exploit vulnerabilities in people, processes, and technologies during trying times, such as the ongoing COVID-19 pandemic, to identify opportunities that target vulnerable individuals, organizations (e.g., medical facilities), and systems. In this paper, we examine the various cyberthreats associated with the COVID-19 pandemic. We also determine the attack vectors and surfaces of cyberthreats. Finally, we will discuss and analyze the insights and suggestions generated by different cyberattacks against individuals, organizations, and systems.