This article presents a comprehensive analysis of AI’s role in human-machine cooperation (HMC), offering an integrated perspective on how machine agents assess and interact with humans. While previous research examined individual aspects like human assessment, trust development, or function allocation separately, we integrate these components into a holistic framework for cooperative systems. We examine two assessment approaches: external methods (observing human cognitive states, intentions, and communications) and internal approaches (using cognitive models to emulate human thinking). Applications in manufacturing and autonomous vehicles demonstrate these concepts systematically. Building on these assessments, we investigate how AI enables machines to develop and calibrate trust in human partners and how it optimizes human-machine interaction through intelligent function allocation and interference management. The article addresses challenges and future research directions in human assessment, machine trust development, transparency, and interaction optimization. This review provides structured insights for utilizing AI in designing effective HMC systems where machines can assess humans, build appropriate trust, maintain transparency, and interact optimally to enable successful cooperation.

The enormous advancement of digital technology and the Internet usage have significantly improved our lives, but have threatened our security and privacy as well. Cyberattacks may have harmful long-term implications to individuals and organizations. High school students are accessible targets for various cybercrimes due to the lack of cybersecurity knowledge and cyber-safe practices. It is important that education about cybersecurity awareness and cyber hygiene practices must begin at a young age. Offering cybersecurity knowledge through interactive tutorials and game-based techniques may increase students' interest in this domain. To develop a security mindset and improve the perception and attitude towards cybersecurity, we created an interactive cybersecurity framework for high school students. Through this framework, we attempt to ef-fectively educate students in cybersecurity through interactive animated visualization modules developed in Unity 3D engine, enabling learning of physical, software, and mathematical aspects of cybersecurity. Each topic in the visualization tool is explained in four stages including information, interaction, explanation, and assessment. Several surveys have been conducted to determine whether this framework enhances users' cognitive abilities.

The increasing use of computer technologies to perform everyday activities simplifies living, but brings the underlying cybersecurity concern to the fore. Due to the accessibility of smartphones, many teenagers are “online” for significant hours in a day. Many middle and high school students have been victims of a cybercrime through online activities. Additionally, various incidents of Internet fraud have been reported where teenagers are persuaded to buy games, music, and videos without realizing they are falling for a scam or disclosing their credit card information. Studies have shown that implementing a successful security awareness camp is crucial in boosting cybersecurity and attracting talent to this domain. This paper discusses our efforts on creating smartphone apps in the context of cyber-security to encourage safe use of apps and raise awareness among teenagers. The strategy used is to develop apps with the intention of closing security gaps. By doing this, teenagers gain a wealth of information about cybersecurity. This work aims to develop students' problem-solving skills and create a cybersecurity mindset for dealing with real-world cybersecurity-related problems such as malware or phishing assaults and to promote interest in cybersecurity careers among high school students utilizing smartphone-based interactive learning modules. We also examine gender-specific patterns and evaluate whether students' cybersecurity problem-solving skills have improved due to this novel intervention.

Several host intrusion detection systems (HIDSs) based on system call analysis have been proposed in the past to detect intrusions and malware using relevant datasets. Machine learning (ML) techniques have been applied on those datasets to improve the performances of HIDSs. However, the emphasis given on their real-world deployment is limited. To address this issue, we propose a framework for system call processing for benign and malware Android apps with an ability of early detection of malware. We extracted and analyzed system call traces for benign and malware apps, and processed their system call traces with N-gram and TF-IDF models. Six ML algorithms – Decision Trees, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machines, and Multi-layer Perceptron – were trained for the malware detection system. The experimental results demonstrate that our Android malware detection system (AMDS), using traces of 3000 system calls, is capable of early detection with an average accuracy of 99.34%. We also implemented an Android app based on a client-server architecture for the proposed AMDS to demonstrate its deployment for malware detection in real-time.

In this paper, a machine learning (ML) framework is devised for physical layer (PHY) authentication in mobile orthogonal frequency-division multiplexing (OFDM) transceivers. The ML framework utilizes various classification models that exploit features extracted to capture the unique hardware behavior of different transmitters, namely: coarse carrier frequency offset (CFO), fine CFO, and residual CFO. These features are leveraged to train various classification models to distinguish between legitimate and non-legitimate transmitters. Since these features are mainly dependent of the hardware behavior more than the channel behavior, they are more resilient in mobility scenarios where channels are more dynamic. For validation, we adopt a software-defined radio (SDR) testbed to record the pertinent measurements in indoor and outdoor mobility environments. It is shown that the proposed approach can distinguish between legitimate and malicious transmitters effectively. Among various classifiers adopted, support vector machine (SVM) gives a true positive rate (TPR) classification of 0.97 with a false positive rate (FPR) of 0.02 in indoor environment; and a TPR of 0.93 with an FPR of 0.05 in outdoor environment.

Computing and intelligence are substantial requirements for the accurate performance of autonomous ground vehicles (AGVs). In this context, the use of cloud services in addition to onboard computers enhances computing and intelligence capabilities of AGVs. In addition, the vast amount of data processed in a cloud system contributes to overall performance and capabilities of the onboard system. This research study entails a qualitative analysis to gather insights on the applicability of the leading cloud service providers in AGV operations. These services include Google Cloud, Microsoft Azure, Amazon AWS, and IBM Cloud. The study begins with a brief review of AGV technical requirements that are necessary to determine the rationale for identifying the most suitable cloud service. The qualitative analysis studies and addresses the applicability of the cloud service over the proposed generalized AGV's architecture integration, performance, and manageability. Our findings conclude that a generalized AGV architecture can be supported by state-of-the-art cloud service, but there should be a clear line of separation between the primary and secondary computing needs. Moreover, our results show significant lags while using cloud services and preventing their use in real-time AGV operation.