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SALT: transfer learning-based threat model for attack detection in smart home.

Pooja Anand1, Yashwant Singh1, Harvinder Singh2,3

  • 1Department of Computer Science and Information Technology, Central University of Jammu, Rahya Suchani, Jammu and Kashmir, 181143, India.

Scientific Reports
|July 19, 2022
PubMed
Summary
This summary is machine-generated.

Transfer Learning (TL) enhances Machine Learning (ML) for detecting known and unknown Internet of Things (IoT) threats. This approach improves classifier robustness against evolving cyberattacks in smart home environments.

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things

Background:

  • The Internet of Things (IoT) presents significant security challenges due to increased connectivity.
  • Traditional Machine Learning (ML) struggles with detecting novel IoT threats because of domain shifts and limited labeled data.
  • Transfer Learning (TL) offers a promising solution to overcome ML limitations in dynamic threat landscapes.

Purpose of the Study:

  • To leverage Transfer Learning (TL) strategies for enhancing threat detection in IoT systems.
  • To develop a robust threat model for identifying both known and unknown attacks in Smart Home environments.
  • To improve the accuracy and reliability of ML classifiers against evolving cyber threats.

Main Methods:

  • Proposed a learning-based threat model named SALT (Security And Learning Threat model) for attack detection in Smart Homes.
  • Utilized TL to transfer knowledge from a source domain with labeled data (known threats) to a target domain with unlabeled data (unknown threats).
  • Addressed challenges related to feature space distribution differences and imbalanced data ratios between normal and attack instances.

Main Results:

  • The proposed TL-based model demonstrated improved robustness and competence in detecting known and unknown IoT threats compared to traditional schemes.
  • Traditional methods showed significant underperformance against unknown threat variants, with accuracy dropping to 39% and recall to 56%.
  • The SALT model effectively utilizes knowledge from known threats to identify novel attack patterns.

Conclusions:

  • Transfer Learning is a viable and effective strategy for enhancing Machine Learning-based threat detection in IoT environments.
  • The proposed SALT model offers a robust solution for identifying diverse and evolving cyber threats in Smart Homes.
  • TL-based approaches are crucial for improving the resilience of IoT security systems against sophisticated attacks.