Survey on recent counterfeit IC detection techniques and future research directions

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

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Evolution of Malware in the Digital Transformation Age

Handbook of Research on Advancing Cybersecurity for Digital Transformation - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-6975-7.ch013 ◽

2021 ◽

pp. 244-266

Author(s):

Shahid Alam

Keyword(s):

Leading Edge ◽

Digital Transformation ◽

Future Research ◽

Malware Analysis ◽

New Wave ◽

New Techniques ◽

Research Directions ◽

Detection Techniques ◽

Future Research Directions ◽

Native Code

As corporations are stepping into the new digital transformation age and adopting leading-edge technologies such as cloud, mobile, and big data, it becomes crucial for them to contemplate the risks and rewards of this adoption. At the same time, the new wave of malware attacks is posing a severe impediment in implementing these technologies. This chapter discusses some of the complications, challenges, and issues plaguing current malware analysis and detection techniques. Some of the key challenges discussed are automation, native code, obfuscations, morphing, and anti-reverse engineering. Solutions and recommendations are provided to solve some of these challenges. To stimulate further research in this thriving area, the authors highlight some promising future research directions. The authors believe that this chapter provides an auspicious basis for future researchers who intend to know more about the evolution of malware and will act as a motivation for enhancing the current and developing the new techniques for malware analysis and detection.

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Transmission and detection techniques for Internet of Bio-Nano Things applications with static and mobile molecular communication: A survey

ITU Journal on Future and Evolving Technologies ◽

10.52953/gxez7259 ◽

2021 ◽

Vol 2 (3) ◽

pp. 33-78

Author(s):

Amit K. Shrivastava ◽

Debanjan Das ◽

Neeraj Varshney ◽

Rajarshi Mahapatra

Keyword(s):

Communication Systems ◽

Future Research ◽

Molecular Communication ◽

Research Directions ◽

Detection Techniques ◽

Practical Design ◽

Future Research Directions ◽

Experimental Works ◽

Detection Schemes ◽

Health Applications

Recent studies have shown that designing communication systems at nanoscale and microscale for the Internet of Bio-Nano Things (IoBNT) applications is possible using Molecular Communication (MC), where two or multiple nodes communicate with each other by transmitting chemical molecules. The basic steps involved in MC are the transmission of molecules, propagation of molecules in the medium, and reception of the molecules at the receiver. Various transmission schemes, channel models, and detection techniques have been proposed for MC in recent years. This paper, therefore, presents an exhaustive review of the existing literature on detection techniques along with their transmission schemes under various MC setups. More specifically, for each setup, this survey includes the transmission and detection techniques under four different environments to support various IoBNT applications: (i) static transmitter and receiver in a pure-diffusive channel, (ii) static transmitter and receiver in a flow-induced diffusive channel, (iii) mobile transmitter and receiver in a pure-diffusive channel, (iv) mobile transmitter and receiver in a flow-induced diffusive channel. Also, performances and complexities of various detection schemes have been compared. Further, several challenges in detection and their possible solutions have been discussed under both static and mobile scenarios. Furthermore, some experimental works in MC are presented to show realistic transmission and detection procedures available in practice. Finally, future research directions and challenges in the practical design of the transmitter and receiver are described to realize MC for IoBNT health applications.

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