AI vs. AI: Exploring the Intersections of AI and Cybersecurity

Ian Molloy, J. Rao, M. Stoecklin
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Abstract

The future of cybersecurity will pit AI against AI. In this talk, we explore the role of AI in strengthening security defenses as well as the role of security in protecting AI services. We expect that the scale, scope and frequency of cyber attacks will increase disruptively with attackers harnessing AI to develop attacks that are even more targeted, sophisticated and evasive. At the same time, analysts in security operations centers are being increasingly overwhelmed in their efforts to keep up with the tasks of detecting, managing and responding to attacks. To cope, the security industry and practitioners are experimenting with the application of AI and machine learning technologies in different areas of security operations. These include a diverse set of areas such as detecting (mis)behaviors and malware, extracting and consolidating threat intelligence, reasoning over security alerts, and recommending countermeasures and/or protective measures. At the same time, adversarial attacks on machine learning systems have become an indisputable threat. Attackers can compromise the training of machine learning models by injecting malicious data into the training set (so-called poisoning attacks), or by crafting adversarial samples that exploit the blind spots of machine learning models at test time (so-called evasion attacks). Adversarial attacks have been demonstrated in a number of different application domains, including malware detection, spam filtering, visual recognition, speech-to-text conversion, and natural language understanding. Devising comprehensive defenses against poisoning and evasion attacks by adaptive adversaries is still an open challenge. Thus, gaining a better understanding of the threat by adversarial attacks and developing more effective defense systems and methods are paramount for the adoption of machine learning systems in security-critical real-world applications. The talk will provide an industrial research perspective and will cover research conducted at IBM Security Research over the several years.
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人工智能与人工智能:探索人工智能与网络安全的交叉点
网络安全的未来将是人工智能与人工智能之间的较量。在本次演讲中,我们将探讨人工智能在加强安全防御方面的作用,以及安全在保护人工智能服务方面的作用。我们预计,随着攻击者利用人工智能开发更有针对性、更复杂、更难以捉摸的攻击,网络攻击的规模、范围和频率将会增加。与此同时,安全运营中心的分析人员在检测、管理和响应攻击的任务中越来越不堪重负。为了应对这种情况,安防行业和从业人员正在尝试将人工智能和机器学习技术应用于不同的安防业务领域。这包括一系列不同的领域,如检测(错误)行为和恶意软件、提取和整合威胁情报、对安全警报进行推理,以及建议对策和/或保护措施。与此同时,对机器学习系统的对抗性攻击已经成为一种无可争辩的威胁。攻击者可以通过向训练集中注入恶意数据(所谓的中毒攻击),或者通过在测试时利用机器学习模型的盲点制作对抗性样本(所谓的逃避攻击)来破坏机器学习模型的训练。对抗性攻击已经在许多不同的应用领域得到了证明,包括恶意软件检测、垃圾邮件过滤、视觉识别、语音到文本转换和自然语言理解。设计针对自适应对手的中毒和逃避攻击的综合防御仍然是一个公开的挑战。因此,更好地了解对抗性攻击的威胁,开发更有效的防御系统和方法,对于在安全关键的现实世界应用中采用机器学习系统至关重要。该演讲将提供一个工业研究的视角,并将涵盖IBM安全研究在过去几年中所进行的研究。
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