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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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This content will become publicly available on March 29, 2026
AI-Driven Cybersecurity: Opportunities, Challenges, and the Future of Human-AI Collaboration
As cyber threats grow in both frequency and sophistication, traditional cybersecurity measures struggle to keep pace with evolving attack methods. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing threat detection, prevention, and response. AI-driven security systems offer the ability to analyze vast amounts of data in real-time, recognize subtle patterns indicative of cyber threats, and adapt to new attack strategies more efficiently than conventional approaches. However, despite AI’s potential, challenges remain regarding its effectiveness, ethical implications, and risks of adversarial manipulation. This research investigates the strengths and limitations of AI-driven cybersecurity by comparing AI-based security tools with traditional methods, identifying key advantages and vulnerabilities, and exploring ethical considerations. Additionally, a survey of cybersecurity professionals was conducted to assess expert opinions on AI’s role, effectiveness, and potential risks. By combining these insights with experimental testing and a comprehensive review of existing literature, this study provides a nuanced understanding of AI’s impact on cybersecurity and offers recommendations for optimizing its integration into modern security infrastructures.
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- Award ID(s):
- 1754054
- PAR ID:
- 10623602
- Publisher / Repository:
- The 2025 ADMI Symposium.
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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