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

arXiv: https://arxiv.org/abs/2101.02780 · HTML: https://mlsanity.com/paper/2101.02780/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 84 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [Spectre Attacks: Exploiting Speculative Execution](https://mlsanity.com/paper/1801.01203/) (2018)
- [IoT Security Techniques Based on Machine Learning](https://mlsanity.com/paper/1801.06275/) (2018)
- [Cyber-Physical Systems Security -- A Survey](https://mlsanity.com/paper/1701.04525/) (2017)
- [SCANN: Synthesis of Compact and Accurate Neural Networks](https://mlsanity.com/paper/1904.09090/) (2019)
- [Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks](https://mlsanity.com/paper/1607.00497/) (2016)
- [Meltdown](https://mlsanity.com/paper/1801.01207/) (2018)
- [Linking Received Packet to the Transmitter Through Physical-Fingerprinting of Controller Area Network](https://mlsanity.com/paper/1801.09011/) (2018)
- [Efficient GAN-based method for cyber-intrusion detection](https://mlsanity.com/paper/1904.02426/) (2019)
- [Deep ahead-of-threat virtual patching](https://mlsanity.com/paper/2007.08296/) (2020)

## Built on by (most cited first, 4 of 4)

- [GRAVITAS: Graphical Reticulated Attack Vectors for Internet-of-Things Aggregate Security](https://mlsanity.com/paper/2106.00073/) (2021)
- [Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems](https://mlsanity.com/paper/2108.03514/) (2021)
- [Is this IoT Device Likely to be Secure? Risk Score Prediction for IoT Devices Using Gradient Boosting Machines](https://mlsanity.com/paper/2111.11874/) (2021)
- [Shared Loss between Generators of GANs](https://mlsanity.com/paper/2211.07234/) (2022)

## Abstract (arXiv)

Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are increasingly being deployed across multiple functionalities, ranging from healthcare devices and wearables to critical infrastructures, e.g., nuclear power plants, autonomous vehicles, smart cities, and smart homes. These devices are inherently not secure 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, managing these vulnerabilities, and improving 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-in-depth mechanism for various classes of attacks and the classification of data targeted by such attacks. This defense mechanism optimizes the cost of security measures based on the sensitivity of the protected resource, thus incentivizing its adoption in real-world CPS/IoT by cybersecurity practitioners.