A Frequency Domain-based Detection Technique for Digital Distortion on GNSS Signals

2016 ◽  
Author(s):  
Chao Sun ◽  
Hongbo Zhao ◽  
Chen Zhuang ◽  
Wenquan Feng
Keyword(s):  
Frequency Domain ◽  
Detection Technique ◽  
Digital Distortion

scholarly journals Malicious Activity Detection in Lightweight Wearable and IoT Devices Using Signal Stitching

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3408
Author(s):  
Fatih Karabacak ◽  
Umit Ogras ◽  
Sule Ozev
Keyword(s):  
Supply Chain ◽  
Frequency Domain ◽  
Integrated Circuit ◽  
Low Frequency ◽  
Detection Technique ◽  
Wearable Electronics ◽  
Security Risk ◽  
Sample Collection ◽  
Active Device ◽  
Malicious Activity

The integrated circuit (IC) manufacturing process involves many players, from chip/board design and fabrication to firmware design and installation. In today’s global supply chain, any of these steps are prone to interference from rogue players, creating a security risk. Therefore, manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities. This paper presents a detection technique for malicious activity that can stem from hardware or firmware Trojans. The proposed technique relies on (i) repetitious side-channel sample collection of the active device, (ii) time-domain stitching, and (iii) frequency domain analysis. Since finding a trusted sample is generally impractical, the proposed technique is based on self-referencing to remove the effects of environmental or device-to-device variation in the frequency domain. We first observe that the power spectrum of the Trojan activity is confined to a low-frequency band. Then, we exploit this fact to achieve self-referencing using signal detection theory. The proposed technique’s effectiveness is demonstrated through experiments on a wearable electronics prototype and system-on-chip (SoC) under a variety of practical scenarios. Experimental results show the proposed detection technique enables a high overall detection coverage for malicious activities of varying types with 0.8 s monitoring time overhead, which is negligible.

scholarly journals SMART WORMS DEFENSE AND DETECTION

2016 ◽  
pp. 264-266
Author(s):  
DASARI NAGARAJU ◽  
C. SHOBA BINDU
Keyword(s):  
Spectral Density ◽  
Frequency Domain ◽  
Power Spectral Density ◽  
Traffic Volume ◽  
Detection Technique ◽  
Worm Detection ◽  
Detection Scheme ◽  
Power Spectral ◽  
Background Traffic ◽  
Worm Attacks

There are several worm attacks in the recent years, this leads to an essentiality of producing new detection technique for worm attacks. In this paper we present a spectrum based smart worm detection scheme, this is based on the idea of detection of worm in the frequency domain. This scheme uses the power spectral density of the scan traffic volume and its corresponding flatness measure to distinguish the smart worm traffic from background traffic. This scheme showed better results against the smart worms and also for the c-worm detection.

Correction to "The analysis of slug tests in the frequency domain" (WRR 25(11), pp. 2388-2396

1990 ◽  
Vol 26 (8) ◽  
pp. 1863-1863
Author(s):  
Paul Marschall ◽  
Baldur Barczewski
Keyword(s):  
Frequency Domain ◽  
Slug Tests
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