Adversarial learning techniques for security and privacy preservation: A comprehensive review

【Author】 Hathaliya, Jigna J.; Tanwar, Sudeep; Sharma, Priyanka

【Source】SECURITY AND PRIVACY

【影响因子】0.000

【Abstract】In recent years, the use of smart devices has increased exponentially, resulting in massive amounts of data. To handle this data, effective data storage and management has required. Cloud computing (CC) is a promising solution to deal with this huge amount of data. Electronic devices are collecting real-time data from sensors and applications through a wireless communication channel in the digital era. In some cases, CC cannot protect against various malicious attacks in the wireless communication channel. To address this issue, we have used machine learning (ML) and deep learning (DL) techniques for attack detection in a wireless channel on an early basis. It trains a model to predict malicious activities of attackers, which aids in the security of CC's sensitive data. We employed adversarial learning techniques (AL) to add fake data into the model to ensure that the trained model was correct. The trained model can distinguish between the fake and real data from the training samples and improve the training samples' performance. AL provides different defense mechanisms to preserve the privacy of ML- and DL-based model but does not ensure the system's robustness. To improve the system's robustness, we have used federated learning with blockchain technology to make a system more robust, reliable, accurate, and transparent. This integration aids in providing high-graded security against adversarial attacks. This paper presents a comprehensive review to highlight the recent improvements in AL techniques. Moreover, we explored the various AL applications in security and privacy preservation. Finally, open research issues and future directions are discussed to show future research avenues.

【Keywords】adversarial attacks; adversarial defenses; adversarial learning; block chain; deep learning; federated learning; machine learning; privacy; reinforcement learning; security

【发表时间】

【收录时间】2022-02-06

【文献类型】综述

【主题类别】

综述--