Top Research Papers on Intrusion Detection System

The electronic data has now become the bone of communication & network uses electronic form of transfer & to protect it from the unauthorized world the intrusion detection system is needed.

Intrusion detection system (IDS) merupakan penghambat semua serangan yang akan menganggu sebuah jaringan, kemampuan dari IDS memberikan peringatan kepada administrator server saat terjadi sebuah aktifitas tertentu yang tidak diinginkan admiministrator sebagai penanggung jawab sebuah system. Selain memberikan peringatan IDS juga mampu melacak jenis aktivitas yang merugikan sebuah system. Suatu IDS akan melakukan pengamatan (monitoring) terhadap paket-paket yang melewati jaringan dan berusaha menemukan apakah terdapat paket-paket yang berisi aktifitas mencurigakan sekaligus melakukan tindak lanj...

New Angriffsmethoden und unentdeckte Fehler im Betriebssystem des Firewall-Rechners konnen Angreifern auch bei korrekter Konfiguration ein Eindringen ermoglichen, werden insbesondere in komplexen Netzarchitekturen nicht ausgeschlossen werde.

This paper provides an explanation of network intrusion, detection, and prevention to overcome them and recommends an intrusion prevention system that can also attempt to stop possible incidents.

This chapter investigated the most important design approaches for IDSs, by mainly focusing on their collecting, analysis, responding capabilities and types of current IDS products.

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This paper presents a review on Intrusion Detection Systems, a technology that monitors the network to avoid any such attacks and can monitor the system activities for potential breach of security policies.

The double guard system is used to detect attacks using Intrusion detection system, which models the network behavior of user sessions across both the front-end web server and the back-end database.

The stages of the evolution of the idea of IDS are explained and its importance to researchers and research centres, security, military and to examine the importance of intrusion detection systems and categories, classifications, and where can put IDS to reduce the risk to the network.

The concept of net intrusion is discussed, the basic demand to the Intrusion Detecting System (IDS), and a kind of model of the NIDS is summarized, and the role of IDS in network security is discussed.

A high-performance approach that can be applied in real-time systems is proposed: visual IDS (V-IDS), and NSL-KDD dataset, one of the large-scale datasets, is used and data was classified by using the DL model.

The accuracy of four different machine learning models has been used to the NSL-KDD dataset to provide enhanced accuracy and outperform some other conventional classifiers for attack classifications and indicate that this suggested approach can provide a reduced false alarm rate while keeping a high diagnosis rate.

Data mining methods are proposed for cyber analytics in support of intrusion detection that uses various data mining and forensic techniques algorithms for the system to work in real time.

This research shows a significant improvement in the ability to detect and prevent cyber threats when IDS/IPS are properly integrated with OS, and emphasizes the need for organizations to shift to proactive defense strategies and strengthen their OS environments by leveraging IDS/IPS technologies.

The goal of this survey paper is heading toward the future direction into agent based security methods for both wireless as well wired networks, and the agents in intrusion detection systems are presented with their advantages and disadvantages.

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 paper provides a machine-learning-based IDS for IoT network threats (ML-IDS) and compares seven popular categorization techniques on three data sets to find the best intrusion detection results.

Exploring the possibility of using mechanisms that are aimed at detecting anomalies in the system, or to search for abuses can help implement effective solutions for intrusion detection and prevention systems.

Different approaches to network intrusion detection like genetic algorithmic program (GA), associate increased higher cognitive process by rule-list i and varied approaches to network intrusion detection like genetic algorithmic program (GA) are presented.

validation of the smart IDS model is conducted and it is found that the model is efficient and effective in relation to intrusion detection.

This survey is intended to be a comprehensive compilation and categorization of currently available intrusion detection system (IDS) commercial products and is intended wholly as a resource for those interested in the current state and the ongoing evolution of IDS products and what that implies for IDS research and development.

The principle, sort and structure of IDS are introduced, and several methods based on the weak points ofIDS used by hacker to attackIDS are listed, and the method of resolving these problems is given.

The stages of the evolution of the idea of IDS are explained and its importance to researchers and research centres, security, military and the importance of intrusion detection systems and categories, classifications, and where can put IDS to reduce the risk to the network are examined.

A neural network model was proposed with the use of supervised techniques that allows improvement in a Network Intrusion Detection System (NIDS) that allows optimal detection of any type of network layer and achieved a value close to 100% accuracy.

This project is to analysis the network with data free attacks in a simulator that involved selfsimilar traffic that ideally represents the internet traffic modeling as well as the Poisson traffic modeling for the non peak hours periods.

An intelligent lightweight IDS, called host-based forensic intrusion detection system, developed with forensics,biometrics, data mining and watermark techniques, to detect the intrusion real-time, effectively and efficiently.

This paper presents an intelligent lightweight IDS, which used the forensics technique to profile the user behavior in order to automate the maintenance of user profile, data mining technique to find out the cooperative attack, and watermark technique to trace back the hackers or intruders. The goal of the system is to detect the intrusion real-time, effectively and efficiently

This paper proposes a technique called KAS-IDS, i.e. K-Means and Adaptive SVM based Intrusion Detection System that has better performance as compared to the individual algorithm when it comes to detection accuracy and false alarm rate.

A Flow-based Self-Active IDS (FSA-IDS), which is a novel framework adopting active learning (AL) into self-learning to reduce the labeling cost significantly and to realize an effective IDS, is introduced.

The immunological model is explored and Statistical Modeling based Anomaly Detection (SMAD) is proposed as the first layer of Intrusion Detection System (IDS), which works as the Innate Immune System (IIS) interface and captures the initial traffic of a network to find out the first-hand vulnerability.

Experiments conducted on the UNSW-NB15 and CICIDS2017 datasets demonstrate significant improvements in detection performance, with a notable reduction in false negative rates for low-frequency attacks, while maintaining high precision, recall, and F1-scores.

CND-IDS is proposed, a continual novelty detection IDS framework which consists of a learning-based feature extractor that continuously updates new feature representations of the system data, and a novelty detector that identifies new cyber attacks by leveraging principal component analysis (PCA) reconstruction.

A convolutional neural network-based intrusion detection system that was evaluated using the CIC-IDS2017 dataset and aims to deliver a low false alarm rate, high accuracy, and a high detection rate.

Intrusion Detection system and intrusion Prevention System (IDS/IPS) technology in computer networks stability, performance and accuracy wise result are comparing.

Experimental results on KDD99 dataset verified that KC-IDS can achieve better detection performance compared with some existing machine learning methods.

The goal of the work is to design and evaluate wireless IDPS with use of packet injection method, and decrease of attacker's traffic by 95% was observed when compared to attacker’s traffic without deployment of proposed IDPS system.

Convolutional Neural Network (CNN), which a kind of Deep learning is explored along with dimensionality reduction for Intrusion Detection (DRCNN-IDS), this approach is used to identify and classify unpredictable attacks in networks.

IDS for CAN is developed – an Android application that functions as an intrusion detection system (IDS) for the CAN bus system, which will allow consumers to take charge of automotive security.

The project proposed is mainly for the purpose to detect any network vulnerabilities and threats by providing an extra layer of security to SCS Computer System Sdn.

Experimental results show better performance of the proposed hybrid IDS using an Arithmetic Optimizer Algorithm (AOA) and Majority Vote Classifier (MVC) in terms of higher intrusion detection accuracy and fewer features compared to other similar studies.

Introduction; The Internet of Things (IoT) has revolutionized numerous sectors, such as home automation, healthcare, and industrial operations, by enabling interconnected devices to facilitate automation, real-time data analysis, and intelligent decision-making. Despite its transformative potential, the rapid proliferation of IoT has introduced critical cybersecurity challenges due to the heterogeneous and fragmented nature of IoT environments. Objective; IoT networks consist of diverse devices with varying capabilities and protocols, making the implementation of standardized security measures...

The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.

This paper proposes DL based IDS which create model of normal network traffic which classifies attacks based on the various features present in the dataset, and achieves a low False Positive Rate and high Detection Rate compared to existing models.

The main duties of an Intrusion Detection System are anomaly detection and reporting; however, certain Intrusion Detection Systems can act when malicious activity or unusual traffic is discovered.

This paper proposes an on-device sequential learning semi-supervised anomaly detector-based intrusion detection system (ONLAD-IDS) using smart interface network cards (NICs) to address challenges of real-time developments of the Internet of Things.

Using Random Forest, Linear Discriminant Analysis, K-Nearest Neighbor, Decision-Tree, Decision-Tree, Adaboost, and Gradient Boosting algorithms, six machine-based IDS are suggested to improve the reliability of the system based on the types of attacks and to eliminate unreliable access and false alarms.

A new algorithm is proposed for Rule generation phase of proposed IDS to configure the rules by implementing Ant Colony Optimization Technique with Association Rule Mining and it is proved that this proposed algorithm outperforms the other existing algorithms.

The cost sensitivity of true positive and false positive results is done and a method is proposed to select the elements of cost sensitivity metrics for further improving the results to achieve the overall better performance.

Intrusion Detection System (IDS) which detects the thief and unauthorized person in the room with surveillance system or the cameras which may be the bank server room which will track down the unauthorized user in the room and send the alert message to the authorized user/ admin.

Random forest algorithm is used for outlier detection of network patterns in this thesis to reduce the false positive rate and improve the performance of intrusion detection systems which will help to prevent and monitor different types of attack.