Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things

@article{Ferdowsi2018DeepLD,
  title={Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things},
  author={Aidin Ferdowsi and Walid Saad},
  journal={2018 IEEE International Conference on Communications (ICC)},
  year={2018},
  pages={1-6}
}
  • A. FerdowsiW. Saad
  • Published 3 November 2017
  • Computer Science
  • 2018 IEEE International Conference on Communications (ICC)
Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. [] Key Method The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal.

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References

SHOWING 1-10 OF 23 REFERENCES

On the authentication of devices in the Internet of things

  • Yaman Sharaf-DabbaghW. Saad
  • Computer Science
    2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)
  • 2016
The proposed object authentication framework is shown to effectively track the effects of physical environment on objects' fingerprints via a transfer learning tool to differentiate between security attacks and normal change in fingerprints.

Physical-Layer Security in the Internet of Things: Sensing and Communication Confidentiality Under Resource Constraints

An overview of low-complexity physical-layer security schemes that are suitable for the IoT is presented, pinpointing the most energy-efficient and low- complexity security techniques that are best suited for IoT sensing applications.

A lightweight authentication protocol for Internet of Things

This paper proposes an encryption method based on XOR manipulation, instead of complex encryption such as using the hash function, for anti-counterfeiting and privacy protection, and enhances the security and hardware design methodology.

Security in the Internet of Things: A Review

The research status of key technologies including encryption mechanism, communication security, protecting sensor data and cryptographic algorithms, and the challenges of IoT are discussed.

Dynamic Watermarking: Active Defense of Networked Cyber–Physical Systems

A general technique is addressed by which the actuators can detect the actions of malicious sensors in the system and disable closed-loop control based on their information, called watermarking, which employs the technique of actuators injecting private excitation into the system, which will reveal malicious tampering with signals.

Physical Authentication of Control Systems: Designing Watermarked Control Inputs to Detect Counterfeit Sensor Outputs

A wide variety of motivations exists for launching an attack on CPSs, ranging from economic reasons, such as obtaining a financial gain, all the way to terrorism, for instance, threatening an entire population by manipulating life-critical resources.

The challenges facing physical layer security

  • W. Trappe
  • Computer Science
    IEEE Communications Magazine
  • 2015
It is highlighted that the opportunities for applying physical layer security to real systems will be quite rich if the community can overcome challenges and note new directions for the community to investigate.

Designing optimal watermark signal for a stealthy attacker

The main result is to show that in the one step version of the problem, if the watermark is a Gaussian distributed random variable, then the maximal performance degradation for any given level of stealthiness for the attacker is achieved when the attacker replaces the control input with the realization of aGaussian random variable.

Dynamic watermarking for general LTI systems

A dynamic watermarking approach for detecting malicious sensor attacks for general LTI systems is designed and provided, and a new set of asymptotic and statistical tests are provided that can distinguish between sensor attacks and wind disturbance.

The Internet of Things for Health Care: A Comprehensive Survey

An intelligent collaborative security model to minimize security risk is proposed; how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context is discussed; and various IoT and eHealth policies and regulations are addressed to determine how they can facilitate economies and societies in terms of sustainable development.