Machine learning models for secure data analytics: A taxonomy and threat model
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 …
wide range of applications exponentially increases the data generated from these devices …
Zero-day attack detection: a systematic literature review
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 …
develop a comprehensive, robust, and effective intrusion detection system (IDS) in the …
Autoencoder-based deep metric learning for network intrusion detection
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 …
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
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 …
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
This survey presents a comprehensive overview of machine learning methods for
cybersecurity intrusion detection systems, with a specific focus on recent approaches based …
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 …
cyber-threats detection technique. In this paper, we present an AI technique for cyber-threats …
Nearest cluster-based intrusion detection through convolutional neural networks
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 …
is a valuable way to address network intrusion detection problems. This paper presents a …
Multi-channel deep feature learning for intrusion detection
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 …
an important research field. Several machine learning techniques have been developed to …
[HTML][HTML] A survey of malware detection using deep learning
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 …
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
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 …
dataset, existing works can hardly be applied in real-world scenarios, especially facing zero …