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1. Battery Management System: Threat Modeling, Vulnerability Analysis, and Cybersecurity Strategy

   https://doi.org/10.1109/ACCESS.2025.3543249  

   Murlidharan, Shravan; Ravulakole, Varsha; Karnati, Jyothi; Malik, Hafiz (January 2025, IEEE Access)

   The Battery Management System (BMS) plays a crucial role in modern energy storage technologies, ensuring battery safety, performance, and longevity. However, as the BMS becomes more sophisticated and interconnected, it faces increasing cybersecurity challenges that can lead to catastrophic failures and safety hazards. This paper provides a comprehensive overview of cyberattacks targeting both traditional and wireless BMS. It explores various attack vectors, including malware injection, electromagnetic interference (EMI), temperature sensing manipulation, sensor malfunctioning and fault injection, and jamming attacks on modern BMS. Through threat modeling and vulnerability analysis, this paper examines the potential impacts on BMS functionality, safety, and performance. We highlight vulnerabilities associated with different BMS architectures and components, emphasizing the need for robust cybersecurity measures to protect against emerging threats. Cybersecurity measures are essential to protect the system from potential threats that could trigger false alarms, cause malfunctions, or lead to dangerous failures. Unauthorized access or tampering with the BMS can disrupt its fault response mechanisms, jeopardizing system performance and associated resources. Key cybersecurity strategies include intrusion detection systems (IDS), crypto-based authentication, secure firmware updates, and hardware-based security mechanisms such as trusted platform modules (TPMs). These measures strengthen BMS resilience by preventing unauthorized access and ensuring data integrity. Our findings are essential for mitigating risks in various sectors, including electric vehicles (EVs), renewable energy, and grid storage. They underscore the importance of ongoing research and development of adaptive security strategies to safeguard BMS against evolving cyber threats. Additionally, we propose a trust mechanism that secures the connection between input sensors and the BMS, ensuring the reliability and safety of battery-powered systems across various industries. 

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2. Changes in High-School Student Attitude and Perception Towards Cybersecurity Through the Use of an Interactive Animated Visualization Framework

   https://doi.org/10.1109/EDUCON54358.2023.10125195  

   Sudha, Sai Suma; Aguayo, Gabriel Castro; Angulo, Abel R; Kothakapu, Jyothirmai; Niyaz, Quamar; Yang, Xiaoli; Javaid, Ahmad Y (May 2023, IEEE)

   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 effectively 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. 

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3. Real-world Cyber Security Demonstration for Networked Electric Drives

   https://doi.org/10.1109/JESTPE.2025.3550830  

   Yang, He; Yang, Bowen; Coshatt, Stephen; Li, Qi; Hu, Kun; Hammond, Bryan Cooper; Ye, Jin; Parasuraman, Ramviyas; Song, Wenzhan (January 2025, IEEE Journal of Emerging and Selected Topics in Power Electronics)

   In this paper, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed’s capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed’s security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. 

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4. The Impact of COVID-19 on Cybersecurity

   Armstrong, Ayanna; Boonthum-Denecke, Chutima (January 2023, The 2023 ADMI Symposium)

   This report will discuss the impact of COVID-19 on cybersecurity. This report also discusses cyber threats in the home office, the healthcare and public health (HPH) sector in attackers’ sight, and the rise of COVID-themed phishing attacks targeted towards remote workers and internet users. Tips on mitigating cyber risk will also be touched upon. This study utilizes surveys conducted to identify the effects this ongoing pandemic has had on businesses and people. 

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5. Using Knowledge Graphs and Reinforcement Learning for Malware Analysis

   https://doi.org/10.1109/BigData50022.2020.9378491  

   Piplai, Aritran; Ranade, Priyanka; Kotal, Anantaa; Mittal, Sudip; Narayanan, Sandeep Nair; Joshi, Anupam (December 2020, 2020 IEEE International Conference on Big Data (Big Data))

   null (Ed.)

   Machine learning algorithms used to detect attacks are limited by the fact that they cannot incorporate the back-ground knowledge that an analyst has. This limits their suitability in detecting new attacks. Reinforcement learning is different from traditional machine learning algorithms used in the cybersecurity domain. Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points. This is important for the cybersecurity domain, especially for malware detection and mitigation, as not all problems have a single, known, correct answer. Often, security researchers have to resort to guided trial and error to understand the presence of a malware and mitigate it.In this paper, we incorporate prior knowledge, represented as Cybersecurity Knowledge Graphs (CKGs), to guide the exploration of an RL algorithm to detect malware. CKGs capture semantic relationships between cyber-entities, including that mined from open source. Instead of trying out random guesses and observing the change in the environment, we aim to take the help of verified knowledge about cyber-attack to guide our reinforcement learning algorithm to effectively identify ways to detect the presence of malicious filenames so that they can be deleted to mitigate a cyber-attack. We show that such a guided system outperforms a base RL system in detecting malware. 

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