Physical-layer intrusion detection for wireless networks using compressed sensing

2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace &Amp; Elec

Τράγος

Traganitis

2014

Self Cite

Wireless sensor networks are the building blocks of the Internet-of-Things architectures, conveying critical and often sensitive and private information. As these networks often consist of severe resource constrained devices, lightweight encryption mechanisms are of paramount importance for achieving energy efficiency. However, the majority of the proposed algorithms do not fully fulfill the requirements for energy efficiency. Furthermore, key distribution schemes are necessary for their proper operation, making the network vulnerable to adversaries that manage to capture the keys during key exchange. In this work, we propose a scheme where key extraction is performed using channel measurements, thus there is no need for any key distribution mechanism. The derived keys are used for encryption/decryption using the primitives of the compressed sensing theory, which allows encryption and compression simultaneously. The evaluation results show that legitimate nodes experience a very low reconstruction (decryption) error. At the same time, adversaries located at a distance greater than half of the carrier frequency's wavelength, experience a higher error, thus being unable to capture sensitive information.

Section: Compressed Sensing Backgroundmentioning

confidence: 99%

Lightweight and secure encryption using channel measurements

2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace &Amp; Elec

Τράγος

Traganitis

2014

Self Cite

“…One such choice is the Gaussian distribution used in several works (e.g. [22]). However, the generation of a Gaussian distribution may not be easily achieved in practical implementations, such in this [23] show that Toeplitz matrices with entries drawn from the same distributions (e.g.…”

Section: Adversary Modelmentioning

confidence: 99%

Malicious traffic analysis in wireless sensor networks using advanced signal processing techniques

2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

Askoxylakis

2013

Self Cite

Abstract-The recent advances in micro-sensor hardware technologies, along with the invention of energy-efficient protocols, have enabled a world-wide spread in wireless sensor networks deployment. These networks are used for a large number of purposes, while having small maintenance and deployment costs. However, as these are usually unattended networks, several security threats have emerged. In this work, we show how an adversary can overhear the encrypted wireless transmissions, and detect the periodic components of the wireless traffic that can further reveal the application used in the sensor network. Traffic analysis is performed in a very energy-efficient way using the compressed sensing principles. Furthermore, the periodic components are detected using the Lomb-Scargle periodogram technique.

“…It has been proved that the most energy consuming task is the communication task, because the radio interface requires a high amount of energy for both receiving and transmitting packets. Taking this into account, the Compressive Sensing (CS) theory [2] has been widely exploited in WSNs for minimizing the data that have to be transmitted from the devices achieving both data compression and encryption at a single step [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Rate-Adaptive Compressive Sensing for IoT Applications

Charalampidis

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

Τράγος

2015

Internet of Things (IoT) interconnects resourceconstrained devices for providing smart applications to citizens. These devices have to be able to ensure both a minimum Quality of Service (QoS) and a minimum level of security when gathering and transmitting data. Compressive Sensing (CS) is a relatively new theory that performs simultaneous lightweight compression and encryption and can be used to prolong the battery lifetime of devices. In this paper, we stress the fact that on the contrary with most previous approaches, the sparsity of the signals can change significantly due to their time-varying nature. We propose a rate-adaptive scheme for maintaining a maximum level of reconstruction error at the receiver, and ensure the QoS requirements. This scheme uses a change point detection method, detecting the change in the sparsity, and estimating the optimum compression rate for maintaining a minimum reconstruction error. Performance is evaluated using real experimental data.