[论文解读] Cybersecurity: Past, Present and Future

Shahid Alam|arXiv (Cornell University)|Jul 4, 2022

Digital and Cyber Forensics被引用 5

一句话总结

本书篇幅较长，对网络安全的发展历程进行了全面分析，涵盖软件与硬件安全、恶意软件演变、生物识别、网络情报和数字取证等关键专业领域的过去发展、当前挑战与未来方向。文章主张通过整合混合增强型与可解释人工智能来提升系统性能与透明度，强调人机协作作为保障物联网和云计算等新兴网络环境安全的关键进展。

ABSTRACT

The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.

研究动机与目标

• 提供对网络安全历史发展、当前状态与未来趋势的全面理解。
• 识别物联网、云计算和智能设备等新兴网络领域中的关键挑战。
• 突出在软件、硬件、生物识别和数字取证等网络安全专业领域开展高级研究的必要性。
• 探讨混合增强型与可解释人工智能在提升网络安全系统性能与透明度方面的作用。
• 通过识别可解释机器学习模型在网络威胁检测中的潜在研究方向，为未来研究提供指导。

提出的方法

• 系统性地综述和整合核心网络安全领域现有文献与技术进展。
• 分析恶意软件的演变及其对现代防御机制的影响。
• 考察生物识别系统在实际部署中的漏洞。
• 探索融合人工智能与人类专业知识的网络情报框架，以提升威胁检测能力。
• 研究可解释人工智能（XAI）技术，以解释和验证网络安全应用中机器学习模型的运行机制。
• 整合人机协作模型，以提升动态网络环境中决策制定的效率。

实验结果

研究问题

• RQ1 物联网和云计算等网络技术的演进如何重塑网络安全的威胁格局？
• RQ2 当前基于人工智能的网络安全系统存在哪些关键局限性？可解释人工智能如何缓解这些局限？
• RQ3 人机协作在哪些方面可提升网络威胁检测与响应的有效性？
• RQ4 网络情报领域中哪些新兴研究方向最有可能推动未来网络安全的创新？
• RQ5 在实际应用中，如何加强生物识别系统以抵御不断演变的仿冒与对抗性攻击？

主要发现

• 可解释人工智能（XAI）的整合显著提升了基于机器学习的网络安全系统中的信任度与可解释性。
• 在网络安全情报中引入人机协作，相比完全自动化系统，能实现更准确、更具上下文感知的威胁检测。
• 新兴网络威胁正日益针对物联网和云环境，亟需采用专门的加固技术。
• 恶意软件的演变速度持续超过传统基于特征的检测方法，因此必须采用自适应与行为分析型防御机制。
• 生物识别系统仍易受仿冒与展示攻击影响，需通过多因素认证与活体检测技术加以增强。
• 混合增强智能模型在大规模安全运营中展现出降低误报率与提升响应速度的潜力。