Dynamic watermarking for general LTI systems

Hespanhol, Pedro; Porter, Matthew; Vasudevan, Ram; Aswani, Anil

doi:10.1109/cdc.2017.8263914

Cited by 30 publications

(37 citation statements)

References 21 publications

(45 reference statements)

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“…Note, we have also modified the formula from [17], by including a constant term, and removing the scale matrix inverse from the trace in (27) due to the normalization carried out in (24). Note that for n < + k , the test statistic value is defined as 0.…”

Section: Dynamic Watermarkingmentioning

confidence: 99%

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Simulation and Real-World Evaluation of Attack Detection Schemes

Porter

Joshi

Hespanho

et al. 2019

2019 American Control Conference (ACC)

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A variety of anomaly detection schemes have been proposed to detect malicious attacks to Cyber-Physical Systems. Among these schemes, Dynamic Watermarking methods have been proven highly effective at detecting a wide range of attacks. Unfortunately, in contrast to other anomaly detectors, no method has been presented to design a Dynamic Watermarking detector to achieve a user-specified false alarm rate, or subsequently evaluate the capabilities of an attacker under such a selection. This paper describes methods to measure the capability of an attacker, to numerically approximate this metric, and to design a Dynamic Watermarking detector that can achieve a user-specified rate of false alarms. The performance of the Dynamic Watermarking detector is compared to three classical anomaly detectors in simulation and on a real-world platform. These experiments illustrate that the attack capability under the Dynamic Watermarking detector is comparable to those of classic anomaly detectors. Importantly, these experiments also make clear that the Dynamic Watermarking detector is consistently able to detect attacks that the other class of detectors are unable to identify.

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Section: Dynamic Watermarkingmentioning

confidence: 99%

“…These methods were proven to detect arbitrary attack models [15] including attacks that replayed a measured signal [16]. These methods were later extended to generalized LTI systems [17], and to networked control systems [18].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Real-World Evaluation of Attack Detection Schemes

Porter

Joshi

Hespanho

et al. 2019

2019 American Control Conference (ACC)

Self Cite

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“…A dynamic watermarking algorithm was proposed in [17] for integrity attack detection in networked CPSs. In [18], the authors introduced a security scheme that ensures detection of data injection attacks using a non-stationary watermarking technique. Finally, the authors in [19] analyzed the optimality of Gaussian watermarked signals in presence of cyber attacks in linear time-variant IoT-like systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Signal Authentication and Security in Massive Internet-of-Things Systems

Ferdowsi

Saad

2019

IEEE Trans. Commun.

137

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Secure signal authentication is arguably one of the most challenging problems in the Internet of Things (IoT), due to the large-scale nature of the system and its susceptibility to man-in-the-middle and data injection attacks. In this paper, a novel watermarking algorithm is proposed for dynamic authentication of IoT signals to detect cyber attacks. The proposed watermarking algorithm, based on a deep learning long shortterm memory (LSTM) structure, enables the IoT devices (IoTDs) to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT gateway, which collects signals from the IoTDs, to effectively authenticate the reliability of the signals. Moreover, in massive IoT scenarios, since the gateway cannot authenticate all of the IoTDs simultaneously due to computational limitations, a game-theoretic framework is proposed to improve the gateway's decision making process by predicting vulnerable IoTDs. The mixed-strategy Nash equilibrium (MSNE) for this game is derived and the uniqueness of the expected utility at the equilibrium is proven. In the massive IoT system, due to the large set of available actions for the gateway, the MSNE is shown to be analytically challenging to derive, and, thus, a learning algorithm that converges to the MSNE is proposed. Moreover, in order to handle incomplete information scenarios in which the gateway cannot access the state of the unauthenticated IoTDs, a deep reinforcement learning algorithm is proposed to dynamically predict the state of unauthenticated IoTDs and allow the gateway to decide on which IoTDs to authenticate. Simulation results show that, with an attack detection delay of under 1 second, the messages can be transmitted from IoTDs with an almost 100% reliability. The results also show that, by optimally predicting the set of vulnerable IoTDs, the proposed deep reinforcement learning algorithm reduces the number of compromised IoTDs by up to 30%, compared to an equal probability baseline.

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“…A dynamic watermarking algorithm was proposed in [12] for integrity attack detection in networked CPSs. In [13], the authors introduced a security scheme that ensures detection of attacks that add a nonzeroaverage-power signal to sensor measurements using a nonstationary watermarking technique. Finally, the authors in [14] analyzed the optimality of Gaussian watermarked signals against cyber attacks in linear time-variant IoT-like systems.…”

Section: Introductionmentioning

confidence: 99%

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

Ferdowsi

Saad

2018

2018 IEEE International Conference on Communications (ICC)

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Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. In particular, the functionality of the IoT devices are extremely dependent on the reliability of their message transmission. Cyber attacks such as data injection, eavesdropping, and man-in-the-middle threats can lead to security challenges. Securing IoT devices against such attacks requires accounting for their stringent computational power and need for low-latency operations. In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks. 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. This method enables the IoT's cloud center, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Furthermore, the proposed method prevents complicated attack scenarios such as eavesdropping in which the cyber attacker collects the data from the IoT devices and aims to break the watermarking algorithm. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability.

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Dynamic watermarking for general LTI systems

Cited by 30 publications

References 21 publications

Simulation and Real-World Evaluation of Attack Detection Schemes

Simulation and Real-World Evaluation of Attack Detection Schemes

Deep Learning for Signal Authentication and Security in Massive Internet-of-Things Systems

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

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