The increasing demand for communication between networked devices connected either through an intranet or the internet increases the need for a reliable and accurate network defense mechanism. Network intrusion detection systems (NIDSs), which are used to detect malicious or anomalous network traffic, are an integral part of network defense. This research aims to address some of the issues faced by anomaly-based network intrusion detection systems. In this research, we first identify some limitations of the legacy NIDS datasets, including a recent CICIDS2017 dataset, which lead us to develop our novel dataset, CIPMAIDS2023-1. Then, we propose a stacking-based ensemble approach that outperforms the overall state of the art for NIDS. Various attack scenarios were implemented along with benign user traffic on the network topology created using graphical network simulator-3 (GNS-3). Key flow features are extracted usingcicflowmeterfor each attack and are evaluated to analyze their behavior. Several different machine learning approaches are applied to the features extracted from the traffic data, and their performance is compared. The results show that the stacking-based ensemble approach is the most promising and achieves the highest weighted F1-score of 98.24%.

Attacks on cloud infrastructure and services have become a significant concern due to the increasing reliance on cloud computing. Various types of attacks pose threats to the cloud ecosystem. Denial-of-Service (DoS) attacks aim to disrupt services by overwhelming the cloud infrastructure with excessive traffic, rendering them inaccessible. Distributed Denial-of-Service (DDoS) attacks take this a step further by utilizing a network of compromised devices to launch coordinated attacks, causing widespread disruptions. Traditional rule-based detection methods may struggle to keep pace with the sophistication and diversity of modern DoS attacks. Machine learning algorithms, on the other hand, can learn from historical attack data, network behavior, and anomalies to detect and classify malicious traffic accurately. In this paper, we propose a classifier based on deep learning algorithms that can detect modern sophisticated DoS attacks more accurately, enabling early detection and timely response. The proposed classifier demonstrated high detection rates, often exceeding 99%, in effectively identifying DoS attacks on cloud infrastructure.