SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine learning

Tanujay Saha, Najwa Aaraj, Neel Ajjarapu, Niraj K. Jha

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are increasingly being deployed across multiple functionalities. These devices are inherently insecure across their comprehensive software, hardware, and network stacks, thus presenting a large attack surface that can be exploited by hackers. In this article, we present an innovative technique for detecting unknown system vulnerabilities, manage associated vulnerabilities, and improve incident response when such vulnerabilities are exploited. The novelty of this approach lies in extracting intelligence from known real-world CPS/IoT attacks, representing them in the form of regular expressions, and employing machine learning (ML) techniques on this ensemble of regular expressions to generate new attack vectors and security vulnerabilities. Our results show that 10 new attack vectors and 122 new vulnerability exploits can be successfully generated that have the potential to exploit a CPS or an IoT ecosystem. The ML methodology achieves an accuracy of 97.4% and enables us to predict these attacks efficiently with an 87.2% reduction in the search space. We demonstrate the application of our method to the hacking of the in-vehicle network of a connected car. To defend against the known attacks and possible novel exploits, we discuss a defense mechanism for various classes of attacks.

Original languageEnglish (US)
JournalIEEE Transactions on Emerging Topics in Computing
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications


  • Artificial Intelligence
  • Attack Graphs
  • Automation
  • Computer security
  • Cyber-Physical Systems
  • Cybersecurity
  • Embedded Systems
  • Hardware
  • Hidden Markov models
  • Internet-of-Things
  • Machine Learning
  • Malware
  • Security
  • Support vector machines


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