[HTML][HTML] AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems

IH Sarker - SN Computer Science, 2022 - Springer
Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial
Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and …

[HTML][HTML] Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions

IH Sarker - SN Computer Science, 2021 - Springer
Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is
nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or …

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 …

Deep learning for anomaly detection: A survey

R Chalapathy, S Chawla - arXiv preprint arXiv:1901.03407, 2019 - arxiv.org
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

A comprehensive review on malware detection approaches

ÖA Aslan, R Samet - IEEE access, 2020 - ieeexplore.ieee.org
According to the recent studies, malicious software (malware) is increasing at an alarming
rate, and some malware can hide in the system by using different obfuscation techniques. In …

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 …

[HTML][HTML] 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 …

Dynamic android malware category classification using semi-supervised deep learning

S Mahdavifar, AFA Kadir, R Fatemi… - 2020 IEEE Intl Conf …, 2020 - ieeexplore.ieee.org
Due to the significant threat of Android mobile malware, its detection has become
increasingly important. Despite the academic and industrial attempts, devising a robust and …

Anomalous example detection in deep learning: A survey

S Bulusu, B Kailkhura, B Li, PK Varshney… - IEEE Access, 2020 - ieeexplore.ieee.org
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection …

Effective and efficient hybrid android malware classification using pseudo-label stacked auto-encoder

S Mahdavifar, D Alhadidi, AA Ghorbani - Journal of network and systems …, 2022 - Springer
Android has become the target of attackers because of its popularity. The detection of
Android mobile malware has become increasingly important due to its significant threat …