An intrusion detection system (IDS) is an important protection instrument for detecting
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …

With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …

Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of
network traffic data and memory space required is usually large. It is, therefore, almost …

Machine learning is of rising importance in cybersecurity. The primary objective of applying
machine learning in cybersecurity is to make the process of malware detection more …

Abstract Network-based Intrusion Detection Systems (NIDSs) identify malicious activities by
analyzing network traffic. NIDSs are trained with the samples of benign and intrusive …

Intrusion detection (ID) is critical in securing computer networks against various malicious
attacks. Recent advancements in machine learning (ML), deep learning (DL), federated …

The growing evolution of cyber-attacks imposes a risk in network services. The search of
new techniques is essential to detect and classify dangerous attacks. In that regard, deep …

Intrusion detection systems (IDSs) that continuously monitor data flow and take swift action
when attacks are identified safeguard networks. Conventional IDS exhibit limitations, such …

The problem of network attacks is a primary focus in the domain of intrusion detection.
Models face significant challenges in recognizing intrusion behaviors, particularly when …

Information security depends on Network Intrusion Detection (NID), which properly identifies
network threats. This work explores simulating a NID system by stacking ensemble …