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 ...

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.

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.

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...

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).

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.

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.

How deep learning or deep neural networks can facilitate flexible IDS with learning capability to detect recognized and new or zero-day network behavioral features, consequently ejecting the systems intruder and reducing the risk of compromise is proposed.

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

This scientific review study presents a road map for researchers and industry employees who focus on IDSs and investigates new attack types, protection mechanisms, and recent scientific studies that have been made in this area.

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 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.

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