Network anomaly detection plays a crucial role as it provides an effective mechanism to
block or stop cyberattacks. With the recent advancement of Artificial Intelligence (AI), there …

Anomaly detection is critical given the raft of cyber attacks in the wireless communications
these days. It is thus a challenging task to determine network anomaly more accurately. In …

Currently, expert systems and applied machine learning algorithms are widely used to
automate network intrusion detection. In critical infrastructure applications of communication …

Anomaly detection in a network is one of the prime concerns for network security. In this
work, a novel Channel Boosted and Residual learning based deep Convolutional Neural …

Network traffic anomaly detection is an important technique of ensuring network security.
However, there are usually three problems with existing machine learning based anomaly …

In this paper, we propose an optimization approach by leveraging swarm intelligence and
ensemble methods to solve the non-deterministic feature selection problem. The proposed …

Convolutional neural networks (CNNs) are the specific architecture of feed‐forward artificial
neural networks. It is the de‐facto standard for various operations in machine learning and …

The advancement of networking platforms for assured online services requires robust and
effective network intelligence systems against anomalous events and malicious threats. With …

Abstract The Internet of Things (IoT) is a key enabler in closing the loop in Cyber-Physical
Systems, providing “smartness” and thus additional value to each monitored/controlled …

Due to the monumental growth of Internet applications in the last decade, the need for
security of information network has increased manifolds. As a primary defense of network …