The Internet of Things (IoT) has revolutionized connectivity by linking billions of devices worldwide. However, this rapid expansion has also introduced severe security vulnerabilities, making IoT devices attractive targets for malware such as the Mirai botnet. Power side-channel analysis has recently emerged as a promising technique for detecting malware activity based on device power consumption patterns. However, the resilience of such detection systems under adversarial manipulation remains underexplored. This work presents a novel adversarial strategy against power side-channel-based malware detection. By injecting structured dummy code into the scanning phase of the Mirai botnet, we dynamically perturb power signatures to evade AI/ML-based anomaly detection without disrupting core functionality. Our approach systematically analyzes the trade-offs between stealthiness, execution overhead, and evasion effectiveness across multiple state-of-the-art models for side-channel analysis, using a custom dataset collected from smartphones of diverse manufacturers. Experimental results show that our adversarial modifications achieve an average attack success rate of 75.2\%, revealing practical vulnerabilities in power-based intrusion detection frameworks.

翻译：物联网通过连接全球数十亿设备，彻底改变了互联性。然而，这种快速扩张也带来了严重的安全漏洞，使物联网设备成为Mirai僵尸网络等恶意软件的攻击目标。功率侧信道分析最近成为一种基于设备功耗模式检测恶意软件活动的有前景的技术。然而，此类检测系统在对抗性操纵下的鲁棒性仍未得到充分研究。本文提出了一种针对基于功率侧信道的恶意软件检测的新型对抗策略。通过在Mirai僵尸网络的扫描阶段注入结构化虚拟代码，我们动态扰动功率特征以规避基于AI/ML的异常检测，同时不影响核心功能。我们的方法利用从不同制造商智能手机收集的自定义数据集，系统分析了在多种最先进的侧信道分析模型中，隐蔽性、执行开销与规避有效性之间的权衡。实验结果表明，我们的对抗性修改实现了平均75.2\%的攻击成功率，揭示了基于功率的入侵检测框架中存在的实际漏洞。