Hamed Haddadpajouh (University of Guelph), Ali Dehghantanha (University of Guelph)

As the integration of Internet of Things devices continues to increase, the security challenges associated with autonomous, self-executing Internet of Things devices become increasingly critical. This research addresses the vulnerability of deep learning-based malware threat-hunting models, particularly in the context of Industrial Internet of Things environments. The study introduces an innovative adversarial machine learning attack model tailored for generating adversarial payloads at the bytecode level of executable files.

Our investigation focuses on the Malconv malware threat hunting model, employing the Fast Gradient Sign methodology as the attack model to craft adversarial instances. The proposed methodology is systematically evaluated using a comprehensive dataset sourced from instances of cloud-edge Internet of Things malware. The empirical findings reveal a significant reduction in the accuracy of the malware threat-hunting model, plummeting from an initial 99% to 82%. Moreover, our proposed approach sheds light on the effectiveness of adversarial attacks leveraging code repositories, showcasing their ability to evade AI-powered malware threat-hunting mechanisms.

This work not only offers a practical solution for bolstering deep learning-based malware threat-hunting models in Internet of Things environments but also underscores the pivotal role of code repositories as a potential attack vector. The outcomes of this investigation emphasize the imperative need to recognize code repositories as a distinct attack surface within the landscape of malware threat-hunting models deployed in the Internet of Things environments.

View More Papers

Secret-Shared Shuffle with Malicious Security

Xiangfu Song (National University of Singapore), Dong Yin (Ant Group), Jianli Bai (The University of Auckland), Changyu Dong (Guangzhou University), Ee-Chien Chang (National University of Singapore)

Read More

Work-in-Progress: Manifest V3 Unveiled: Navigating the New Era of...

Nikolaos Pantelaios and Alexandros Kapravelos (North Carolina State University)

Read More

LARMix: Latency-Aware Routing in Mix Networks

Mahdi Rahimi (KU Leuven), Piyush Kumar Sharma (KU Leuven), Claudia Diaz (KU Leuven)

Read More

Pencil: Private and Extensible Collaborative Learning without the Non-Colluding...

Xuanqi Liu (Tsinghua University), Zhuotao Liu (Tsinghua University), Qi Li (Tsinghua University), Ke Xu (Tsinghua University), Mingwei Xu (Tsinghua University)

Read More