The rapid advances in the internet and communication fields have resulted in a huge
increase in the network size and the corresponding data. As a result, many novel attacks are …

Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data
confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a …

In this paper, we present a survey of deep learning approaches for cyber security intrusion
detection, the datasets used, and a comparative study. Specifically, we provide a review of …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Labeled data sets are necessary to train and evaluate anomaly-based network intrusion
detection systems. This work provides a focused literature survey of data sets for network …

Despite its technological benefits, the Internet of Things (IoT) has cyber weaknesses due to
vulnerabilities in the wireless medium. Machine Larning (ML)-based methods are widely …

As an alternative centralized systems, which may prevent data to be stored in a central
repository due to its privacy and/or abundance, federated learning (FL) is nowadays a game …

To ensure undisrupted business, large Internet companies need to closely monitor various
KPIs (eg, Page Views, number of online users, and number of orders) of its Web …

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

Anomaly detection in time series has attracted considerable attention due to its importance
in many real-world applications including intrusion detection, energy management and …