As a result of the widespread adoption of the Internet of Things, there are now hundreds of
millions of connected devices, increasing the likelihood that they may be vulnerable to …

Intrusion detection systems (IDS) identify cyber attacks given a sample of network traffic
collected from real-world computer networks. As a powerful classification tool, deep learning …

Traditionally, cyber-attack detection relies on reactive, assistive techniques, where pattern-
matching algorithms help human experts to scan system logs and network traffic for known …

Detecting various types of attack traffic is critical to computer network security. The current
detection methods require massive amounts of data to detect attack traffic. However, in most …

The escalating complexity and impact of cyber threats require organisations to rehearse
responses to cyber-attacks by routinely conducting cyber security exercises. However, the …

In previous work, we proposed an end-to-end intrusion early detection system to identify
network attacks in real-time before they complete and could cause more damage to the …

The objective of this research is to improve the efficiency in identifying malicious traffic by
integrating a deep neural network that utilizes a hierarchical attention mechanism. The …

Internet of Things (IoT) generates huge amount of data, that needs to communicate between
the IoT enabled devices. These communications are vulnerable to security attacks and are …

Edge network devices are typically resource-constrained, but intrusion detection requires
real-time capabilities. Currently deep learning detection models for raw traffic data require …

随着网络的普及和依赖程度的不断增加, 恶意流量的泛滥已经成为网络安全领域的严重挑战.
在这个数字时代, 网络攻击者不断寻找新的方式来侵入系统, 窃取数据和破坏网络服务 …