Machine learning models for secure data analytics: A taxonomy and threat model

R Gupta, S Tanwar, S Tyagi, N Kumar - Computer Communications, 2020 - Elsevier
In recent years, rapid technological advancements in smart devices and their usage in a
wide range of applications exponentially increases the data generated from these devices …

Zero-day attack detection: a systematic literature review

R Ahmad, I Alsmadi, W Alhamdani… - Artificial Intelligence …, 2023 - Springer
With the continuous increase in cyberattacks over the past few decades, the quest to
develop a comprehensive, robust, and effective intrusion detection system (IDS) in the …

Autoencoder-based deep metric learning for network intrusion detection

G Andresini, A Appice, D Malerba - Information Sciences, 2021 - Elsevier
Nowadays intrusion detection systems are a mandatory weapon in the war against the ever-
increasing amount of network cyber attacks. In this study we illustrate a new intrusion …

GAN augmentation to deal with imbalance in imaging-based intrusion detection

G Andresini, A Appice, L De Rose, D Malerba - Future Generation …, 2021 - Elsevier
Nowadays attacks on computer networks continue to advance at a rate outpacing cyber
defenders' ability to write new attack signatures. This paper illustrates a deep learning …

A comprehensive survey of databases and deep learning methods for cybersecurity and intrusion detection systems

D Gümüşbaş, T Yıldırım, A Genovese… - IEEE Systems …, 2020 - ieeexplore.ieee.org
This survey presents a comprehensive overview of machine learning methods for
cybersecurity intrusion detection systems, with a specific focus on recent approaches based …

Cyber threat detection based on artificial neural networks using event profiles

J Lee, J Kim, I Kim, K Han - Ieee Access, 2019 - ieeexplore.ieee.org
One of the major challenges in cybersecurity is the provision of an automated and effective
cyber-threats detection technique. In this paper, we present an AI technique for cyber-threats …

Nearest cluster-based intrusion detection through convolutional neural networks

G Andresini, A Appice, D Malerba - Knowledge-Based Systems, 2021 - Elsevier
The recent boom in deep learning has revealed that the application of deep neural networks
is a valuable way to address network intrusion detection problems. This paper presents a …

Multi-channel deep feature learning for intrusion detection

G Andresini, A Appice, N Di Mauro, C Loglisci… - IEEE …, 2020 - ieeexplore.ieee.org
Networks had an increasing impact on modern life since network cybersecurity has become
an important research field. Several machine learning techniques have been developed to …

[HTML][HTML] A survey of malware detection using deep learning

A Bensaoud, J Kalita, M Bensaoud - Machine Learning With Applications, 2024 - Elsevier
The problem of malicious software (malware) detection and classification is a complex task,
and there is no perfect approach. There is still a lot of work to be done. Unlike most other …

FS-IDS: A framework for intrusion detection based on few-shot learning

J Yang, H Li, S Shao, F Zou, Y Wu - Computers & Security, 2022 - Elsevier
Due to the high dependency of traditional intrusion detection method on a fully-labeled large
dataset, existing works can hardly be applied in real-world scenarios, especially facing zero …