Top Research Papers on Intrusion Detection System

A multitiered hybrid IDS that incorporates a signature- based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks, and experimental results illustrate the feasibility of implementing the proposed system in real-time vehicle systems.

This survey reviews the state of the art in explainable AI (XAI) for IDS, its current challenges, and discusses how these challenges span to the design of an X-IDS, and proposes a generic architecture that considers human-in-the-loop which can be used as a guideline when designing anX-IDS.

Passban is presented, an intelligent intrusion detection system (IDS) able to protect the IoT devices that are directly connected to it that can be deployed directly on very cheap IoT gateways, taking full advantage of the edge computing paradigm to detect cyber threats as close as possible to the corresponding data sources.

A DL-IDS (deep learning-based intrusion detection system), which uses the hybrid network of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract the spatial and temporal features of network traffic data and to provide a better intrusion Detection System.

Intrusion Detection Systems (IDS) plays a part in modern cyber security, as a result of the increasing need for cyber security systems in the “real” world due to the increasing number of cyber attacks, more sophisticated systems are required in order to prevent these attacks - an IDS can provide this protection. Due to the sophistication of these systems, they must be properly understood, developed and analyzed - research papers can be used as a tool to improve IDS systems. This paper is composed of two main sections: a survey and a taxonomy, providing information, reviews ...

A novel Imbalanced Generative Adversarial Network (IGAN) to tackle the class imbalance problem is proposed, and an IGAN-based Intrusion Detection System is established to cope with class-imbalanced intrusion detection.

The proposed Siam-IDS is able to detect R2L and U2R attacks without using traditional class balancing techniques such as oversampling and random undersampling, and was compared with existing IDSs developed using DL techniques namely Deep Neural Network (DNN) and Convolutional Neural network (CNN).

A network intrusion detection system is critical for cyber security against illegitimate attacks. In terms of feature perspectives, network traffic may include a variety of elements such as attack reference, attack type, a sub-category of attack, host information, malicious scripts, etc. In terms of network perspectives, network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic. It is challenging to identify a specific attack due to complex features and data imbalance issues. To address these issues, this paper proposes an Intrusion Detection System us...

This paper proposes an algorithm-level approach called Improved Siam-IDS (I-SiamIDS), which is a two-layer ensemble for handling class imbalance problem and showed significant improvement in terms of Accuracy, Recall, Precision, F1-score and values of Area Under the Curve (AUC) for both NSL-KDD and CIDDS-001 datasets.

This paper proposes an IDS named SwiftIDS, which is capable of both analyzing massive traffic data in high-speed networks timely and keeping satisfactory detection performance, and takes advantage of LightGBM’s effective detection performance to simplify the data preprocessing.

This work presents an in-depth analysis of attacks exploiting IEC–61850 substations and recent research efforts for detecting and preventing them, and presents an original taxonomy comprising design and evaluation aspects for substation-specific Intrusion Detection Systems.

A novel mobile agent based intrusion detection system to secure the network of connected medical devices is designed and developed, which is hierarchical, autonomous, and employs machine learning and regression algorithms to detect network level intrusions as well as anomalies in sensor data.

The mission of an intrusion detection system (IDS) is to monitor network activities and assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover irregular activities by discriminating between normal and anomalous deviations. Nonetheless, existing strategies for detecting anomalies generally rely on single classification models that are still incapable of reducing the false alarm rate and increasing the detection rate. This study introduces a dual ensemble model by combining two existing ensemble techniques, such as bagging and gradient boosting decision tree (GBD...

A hybrid deep learning (DL) model for cyber attack detection in IoV is proposed based on long short-term memory (LSTM) and gated recurrent unit (GRU) and the experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy.

An optimal convolutional neural network and long short-term memory network (CNN-LSTM) model, normalized UTF-8 character encoding for Spatial Feature Learning (SFL) to adequately extract the characteristics of real-time HTTP traffic without encryption, calculating entropy, and compression is proposed.

The high quality feature set obtained by the three-layered feature construction using the GA, FCM, CNN extractor, and a hybrid CNN and BG learning method significantly improves the final detection performance.

This article gives a thorough overview of recent literature regarding neural networks usage in intrusion detection system area, including surveys and new method proposals.

The increasing number of security attacks have inspired researchers to employ various classifiers, such as support vector machines (SVMs), to deal with them in Intrusion detection systems (IDSs). This paper presents a comprehensive study and investigation of the SVM-based intrusion detection and feature selection systems proposed in the literature. It first presents the essential concepts and background knowledge about security attacks, IDS, and SVM classifiers. It then provides a taxonomy of the SVM-based IDS schemes and describes how they have adapted numerous types of SVM classifiers in det...

Experimental result demonstrate that proposed hybrid model is more sensitive to attacks in the IoT network than conventional machine learning and deep learning model.

This work is unique in the intrusion detection field, presenting the first use of the SHAP method to give explanations for IDSs, and the different interpretations between different kinds of classifiers can also help security experts better design the structures of theIDSs.