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 …

Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …

The rise of the new generation of cyber threats demands more sophisticated and intelligent
cyber defense solutions equipped with autonomous agents capable of learning to make …

Several new technologies such as the smart cities, the Internet of Things (IoT), and 5G
Internet need services offered by cloud computing for processing and storing more …

Abstract Cloud Internet of Things (CIoT) environments, as the essential basis for computing
services, have been subject to abuses and cyber threats. The adversaries constantly search …

This paper presents a comprehensive literature review on Reinforcement Learning (RL)
techniques used in Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS) …

Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …

Anomaly detection has been used to detect and analyze anomalous elements from data for
years. Various techniques have been developed to detect anomalies. However, the most …

In this paper, we propose a formalization of the process of exploitation of SQL injection
vulnerabilities. We consider a simplification of the dynamics of SQL injection attacks by …

Abstract The Intrusion Detection System plays a significant role in discovering malicious
activities and provides better network security solutions than other conventional defense …