Internet of things (iot) security intelligence: a comprehensive overview, machine learning solutions and research directions

IH Sarker, AI Khan, YB Abushark, F Alsolami - Mobile Networks and …, 2023 - Springer
Abstract The Internet of Things (IoT) is one of the most widely used technologies today, and
it has a significant effect on our lives in a variety of ways, including social, commercial, and …

Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects

IH Sarker - Annals of Data Science, 2023 - Springer
Due to the digitization and Internet of Things revolutions, the present electronic world has a
wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a …

A survey of android malware detection with deep neural models

J Qiu, J Zhang, W Luo, L Pan, S Nepal… - ACM Computing Surveys …, 2020 - dl.acm.org
Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …

HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system

MA Khan - Processes, 2021 - mdpi.com
Nowadays, network attacks are the most crucial problem of modern society. All networks,
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …

Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective

IH Sarker - SN Computer Science, 2021 - Springer
Deep learning, which is originated from an artificial neural network (ANN), is one of the
major technologies of today's smart cybersecurity systems or policies to function in an …

Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

IH Sarker - Security and Privacy, 2023 - Wiley Online Library
Due to the rising dependency on digital technology, cybersecurity has emerged as a more
prominent field of research and application that typically focuses on securing devices …

Evaluating shallow and deep neural networks for network intrusion detection systems in cyber security

RK Vigneswaran, R Vinayakumar… - 2018 9th …, 2018 - ieeexplore.ieee.org
Intrusion detection system (IDS) has become an essential layer in all the latest ICT system
due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty …

Deep learning for android malware defenses: a systematic literature review

Y Liu, C Tantithamthavorn, L Li, Y Liu - ACM Computing Surveys, 2022 - dl.acm.org
Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …

DroidMalwareDetector: A novel Android malware detection framework based on convolutional neural network

AT Kabakus - Expert Systems with Applications, 2022 - Elsevier
Smartphones have become an integral part of our daily lives thanks to numerous reasons.
While benefitting from what they offer, it is critical to be aware of the existence of malware in …

Fog-based attack detection framework for internet of things using deep learning

A Samy, H Yu, H Zhang - Ieee Access, 2020 - ieeexplore.ieee.org
The number of cyber-attacks and data breaches has immensely increased across different
enterprises, companies, and industries as a result of the exploitation of the weaknesses in …