scholarly journals Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8288
Author(s):  
Ethan Chen ◽  
John Kan ◽  
Bo-Yuan Yang ◽  
Jimmy Zhu ◽  
Vanessa Chen
Keyword(s):  
Detection System ◽  
Sensitive Information ◽  
Malicious Attacks ◽  
Electromagnetic Sensors ◽  
Self Testing ◽  
Detection Approach ◽  
Good Energy ◽  
Extreme Edge ◽  
On Chip ◽  
Efficient Learning

Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line.

scholarly journals Intelligent Botnet Detection Approach in Modern Applications

2021 ◽  
Vol 15 (16) ◽  
pp. 113
Author(s):  
Khattab M. Ali Alheeti ◽  
Ibrahim Alsukayti ◽  
Mohammed Alreshoodi
Keyword(s):  
Detection System ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Learning Networks ◽  
Detection Systems ◽  
Testing Dataset ◽  
Detection Approach ◽  
Secure Environment ◽  
Iot Devices ◽  
The Internet Of Things

<p class="0abstract">Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection systems of IoT are vulnerable to various types of attacks. Hence, intrusion detection systems (IDS) have become crucial requirements for any modern design. In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks. The protection system can provide a secure environment for IoT. To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets. The accuracy rate in the case of the normal testing dataset was 99.30%, while was 99.42% for the fuzzification testing dataset. The experimental results of the proposed system reflect its robustness, reliability, and efficiency.</p>

On-chip orbital angular momentum detection via catenary grating metasurface

2021 ◽  
Author(s):  
Panpan Chen ◽  
Cong Chen ◽  
Jianxin Xi ◽  
Xiang Du ◽  
Li Liang ◽  
...  
Keyword(s):  
Angular Momentum ◽  
Integrated Circuits ◽  
Orbital Angular Momentum ◽  
Topological Charge ◽  
Detection System ◽  
Great Promise ◽  
Vortex Beam ◽  
Detection Approach ◽  
On Chip ◽  
Plasmon Polaritons

Abstract Vortex lights with optical orbital angular momentum (OAM) have shown great promise in the areas of optical communication, optical manipulation and quantum optics. However, traditional methods for detecting the topological charge of vortex beams, such as interference and diffraction, are still challenging in miniaturization of the detection system and perfect matching of wave vectors. Here, a detection approach is proposed for measuring the topological charge of Laguerre-Gaussian (LG) vortex beam based on a catenary grating metasurface. According to the wave vector matching principle, the LG vortex beam can be coupled into surface plasmon polaritons (SPPs) waves propagating in different directions by using the well-designed catenary grating structure. The positive and negative of the topological charge can be distinguished by different arrangement of the catenary gratings. Besides, the propagation angle of the launched SPPs waves increases with the value of the topological charge. We believe that the proposed device would have a broader application prospect in high compact photonic integrated circuits.

scholarly journals Interband Cascade Photonic Integrated Circuits on Native III-V Chip

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 599
Author(s):  
Jerry R. Meyer ◽  
Chul Soo Kim ◽  
Mijin Kim ◽  
Chadwick L. Canedy ◽  
Charles D. Merritt ◽  
...  
Keyword(s):  
Integrated Circuits ◽  
Integrated Circuit ◽  
Detection System ◽  
Laser Cavity ◽  
Building Blocks ◽  
Drive Power ◽  
Optical Elements ◽  
Photonic Integrated Circuit ◽  
Cleaved Facets ◽  
On Chip

We describe how a midwave infrared photonic integrated circuit (PIC) that combines lasers, detectors, passive waveguides, and other optical elements may be constructed on the native GaSb substrate of an interband cascade laser (ICL) structure. The active and passive building blocks may be used, for example, to fabricate an on-chip chemical detection system with a passive sensing waveguide that evanescently couples to an ambient sample gas. A variety of highly compact architectures are described, some of which incorporate both the sensing waveguide and detector into a laser cavity defined by two high-reflectivity cleaved facets. We also describe an edge-emitting laser configuration that optimizes stability by minimizing parasitic feedback from external optical elements, and which can potentially operate with lower drive power than any mid-IR laser now available. While ICL-based PICs processed on GaSb serve to illustrate the various configurations, many of the proposed concepts apply equally to quantum-cascade-laser (QCL)-based PICs processed on InP, and PICs that integrate III-V lasers and detectors on silicon. With mature processing, it should become possible to mass-produce hundreds of individual PICs on the same chip which, when singulated, will realize chemical sensing by an extremely compact and inexpensive package.

scholarly journals IDS for Industrial Applications: A Federated Learning Approach with Active Personalization

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6743
Author(s):  
Vasiliki Kelli ◽  
Vasileios Argyriou ◽  
Thomas Lagkas ◽  
George Fragulis ◽  
Elisavet Grigoriou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Network Flow ◽  
Detection System ◽  
Human Life ◽  
Industrial Sector ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Learning Approaches ◽  
Monitoring And Control

Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.

scholarly journals Low-Power Pedestrian Detection System on FPGA

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 35 ◽  
Author(s):  
Vinh Ngo ◽  
David Castells-Rufas ◽  
Arnau Casadevall ◽  
Marc Codina ◽  
Jordi Carrabina
Keyword(s):  
Low Power ◽  
Detection System ◽  
Low Cost ◽  
Pedestrian Detection ◽  
Support Vector ◽  
Embedded Processor ◽  
Car Industry ◽  
Field Programmable ◽  
On Chip ◽  
Histogram Of Gradients

Pedestrian detection is one of the key problems in the emerging self-driving car industry. In addition, the Histogram of Gradients (HOG) algorithm proved to provide good accuracy for pedestrian detection. Many research works focused on accelerating HOG algorithm on FPGA (Field-Programmable Gate Array) due to its low-power and high-throughput characteristics. In this paper, we present an energy-efficient HOG-based implementation for pedestrian detection system on a low-cost FPGA system-on-chip platform. The hardware accelerator implements the HOG computation and the Support Vector Machine classifier, the rest of the algorithm is mapped to software in the embedded processor. The hardware runs at 50 Mhz (lower frequency than previous works), thus achieving the best pixels processed per clock and the lower power design.

Design and Optimization of Reaction Chamber and Detection System in Dynamic Labs-on-Chip for Proteins Detection

2013 ◽  
Vol 60 (8) ◽  
pp. 2161-2166 ◽  
Author(s):  
Maya Briani ◽  
Giacomo Germani ◽  
Eugenio Iannone ◽  
Maurizio Moroni ◽  
Roberto Natalini
Keyword(s):  
Detection System ◽  
Reaction Chamber ◽  
Design And Optimization ◽  
On Chip
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