This study presents a comprehensive exploration of the hyperparameter optimization in one-dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The increasing frequency and complexity cyberattacks have prompted an urgent need effective intrusion-detection systems (IDSs). Herein, we focus on optimizing nine hyperparameters within 1D-CNN model, using two well-esta...

Intrusion Detection System (IDS) plays a crucial role in detecting and identifying the DoS DDoS type of attacks on IoT devices. However, anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection. Also, we found many difficulty levels when applying IDS to devices attempted attacks. Given this background, designed solution detect intrusions using Convolutiona...

This paper proposes an architecture to develop machine learning/deep learning models for anomaly network intrusion detection systems on reconfigurable computing platforms. We build two validate the framework: Anomaly Detection Autoencoder (ADA) and Artificial Neural Classification (ANC) in NetFPGA-sume platform. Three published data sets NSL-KDD, UNSW-NB15, CIC-IDS2017 are used test deployed mo...

<span>IoT networks mostly rely on wireless mediums for communication, and due to that, they are very susceptible intrusions. And the tiny nature, processing complexity, limited storage capacities, IoT require reliable intrusion detection systems (IDS). Although there many IDS types of research available in literature, most these suitable wired network environments, benchmark datasets used...

Abstract—Data sets – both structured & unstructured, which are so large and complex that processing it using database management tools or traditional applications to derive analytical insights becomes difficult. Capturing, storage, internal search, sharing, predictive analysis and visualization are some of the major tasks being performed on such data sets. With the increa sing amount of data ge...

Intrusion Detection System (IDS) has become a vital component in ensuring the safety of today’s network. In this review, we does experimentation on NSL KDD Cup -20% dataset with selected attribute to improve the performance. The time complexity is improved by reducing false alarm rate with Machine Learning (ML) algorithms. This improve the detection rate for DoS, Probe, U2R and R2L attacks. In ...

The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face challenges due to functional physical diversity. These characteristics make exploiting all features attributes for self-protection diffic...

In this paper, we present a two-stage classifier based on RepTree algorithm and protocols subset for network intrusion detection system. To evaluate the performance of our approach, we used the UNSW-NB15 data set and the NSL-KDD data set. In first phase our approach divides the incoming network traffics into three type of protocols TCP, UDP or Other, then classifies into normal or anomaly. In s...