Journal ArticleDOI
Machine Learning Models for Secure Data Analytics: A taxonomy and threat model
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TLDR
This paper explored Machine Learning (ML) and Deep Learning (DL)-based models and techniques which are capable off to identify and mitigate both the known as well as unknown attacks and proposed a DL and ML-based Secure Data Analytics (SDA) architecture to classify normal or attack input data.About:
This article is published in Computer Communications.The article was published on 2020-03-01. It has received 128 citations till now. The article focuses on the topics: Threat model & Big data.read more
Citations
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Journal ArticleDOI
A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions
TL;DR: A LSTM and GRU-based hybrid cryptocurrency prediction scheme is proposed, which focuses on only two cryptocurrencies, namely Litecoin and Monero, and accurately predicts the prices with high accuracy, revealing that the scheme can be applicable in various cryptocurrencies price predictions.
Journal ArticleDOI
A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems
Umesh Bodkhe,Dhyey Mehta,Sudeep Tanwar,Pronaya Bhattacharya,Pradeep Kumar Singh,Wei-Chiang Hong +5 more
TL;DR: The proposed survey will act as a road-map for blockchain developers and researchers to evaluate and design future consensus mechanisms, which helps to build an efficient CPS for industry 4.0 stakeholders.
Journal ArticleDOI
Smart Secure Sensing for IoT-Based Agriculture: Blockchain Perspective
TL;DR: A rigorous literature review to inspect the state-of-the-art development of the schemes that provide information security using blockchain technology and revealed the security goals towards which the research has been directed and helped to identify new avenues for future research using artificial intelligence.
Journal ArticleDOI
An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks
TL;DR: An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks that can achieve detection rate of 99.98%, accuracy of 96.35%, and can reduce false alarm rate upto 5.59% is proposed.
Journal ArticleDOI
A taxonomy of AI techniques for 6G communication networks
Karan Sheth,Keyur Patel,Het Shah,Sudeep Tanwar,Rajesh Gupta,Neeraj Kumar,Neeraj Kumar,Neeraj Kumar +7 more
TL;DR: A comprehensive survey on AI-enabled 6G communication technology, which can be used in wide range of future applications, and how AI can be integrated into different applications such as object localization, UAV communication, surveillance, security and privacy preservation etc.
References
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Journal ArticleDOI
Systematic literature reviews in software engineering - A systematic literature review
Barbara Kitchenham,O. Pearl Brereton,David Budgen,Mark Turner,John W. Bailey,Stephen Linkman +5 more
TL;DR: The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners and European researchers appear to be the leading exponents of systematic literature reviews.
Proceedings ArticleDOI
Boosting Adversarial Attacks with Momentum
TL;DR: A broad class of momentum-based iterative algorithms to boost adversarial attacks by integrating the momentum term into the iterative process for attacks, which can stabilize update directions and escape from poor local maxima during the iterations, resulting in more transferable adversarial examples.
Journal ArticleDOI
Lessons from applying the systematic literature review process within the software engineering domain
TL;DR: In this article, the authors report experiences with applying one such approach, the practice of systematic literature review, to the published studies relevant to topics within the software engineering domain, and some lessons about the applicability of this practice to software engineering are extracted.
Journal ArticleDOI
Adversarial Examples: Attacks and Defenses for Deep Learning
TL;DR: In this paper, the authors review recent findings on adversarial examples for DNNs, summarize the methods for generating adversarial samples, and propose a taxonomy of these methods.
Journal ArticleDOI
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
TL;DR: The experimental results show that RNN-IDS is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of traditional machine learning classification methods in both binary and multiclass classification.