Subgraph-based adversarial examples against graph-based IoT malware detection systems

Ahmed Abusnaina, Hisham Alasmary, Mohammed Abuhamad, Saeed Salem, Dae Hun Nyang, Aziz Mohaisen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


Internet of Things (IoT) has become widely adopted in many fields, including industry, social networks, health care, and smart homes, connecting billions of IoT devices through the internet. Understanding and studying IoT malware through analysis using various approaches, such as Control Flow Graph (CFG)-based features and then applying deep learning detection, are widely explored. In this study, we investigate the robustness of such models against adversarial attacks. Our approach crafts the adversarial IoT software using the Subgraph Embedding and Augmentation (SGEA) method that reduces the embedded size required to cause misclassification. Intensive experiments are conducted to evaluate the performance of the proposed method. We observed that SGEA approach is able to misclassify all IoT malware samples as benign by embedding an average size of 6.8 nodes. This highlights that the current detection systems are prone to adversarial examples attacks; thus, there is a need to build more robust systems to detect such manipulated features generated by adversarial examples.

Original languageEnglish
Title of host publicationComputational Data and Social Networks - 8th International Conference, CSoNet 2019, Proceedings
EditorsAndrea Tagarelli, Hanghang Tong
Number of pages14
ISBN (Print)9783030349790
StatePublished - 2019
Event8th International Conference on Computational Data and Social Networks, CSoNet 2019 - Ho Chi Minh City, Viet Nam
Duration: 18 Nov 201920 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11917 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Conference on Computational Data and Social Networks, CSoNet 2019
Country/TerritoryViet Nam
CityHo Chi Minh City

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.


  • Adversarial learning
  • Graph embedding
  • IoT malware detection


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