As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has become imperative to understand the vulnerabilities of the IoT systems when supporting diverse applications. Because machine learning has been applied in many IoT systems, the security implications of machine learning need to be studied following an adversarial machine learning approach. In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices. Our numerical results demonstrate the feasibility of this attack to disrupt the decision making in data fusion with limited control of IoT devices, e.g., the attack success rate reaches 83% when the adversary tampers with only 8 out of 20 IoT devices. These results show that the machine learning engine of IoT system is highly vulnerable to attacks even when the adversary manipulates a small portion of IoT devices, and the outcome of these attacks severely disrupts IoT system operations.
CCS CONCEPTS• Security and privacy → Mobile and wireless security; • Networks → Network reliability.
Cyber deception has recently received increasing attentions as a promising mechanism for proactive cyber defense. Cyber deception strategies aim at injecting intentionally falsified information to sabotage the early stage of attack reconnaissance and planning in order to render the final attack action harmless or ineffective.Motivated by recent advances in cyber deception research, we in this paper provide a formal view of cyber deception, and review high-level deception schemes and actions. We also summarize and classify recent research results of cyber defense techniques built upon the concept of cyber deception, including gametheoretic modeling at the strategic level, network-level deception, in-host-system deception and cryptography based deception. Finally, we lay out and discuss in detail the research challenges towards developing full-fledged cyber deception frameworks and mechanisms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.