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.

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.

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...

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.

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.

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.

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 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.

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.

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.

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.

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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.

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.

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 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.

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

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.

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.

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.

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.

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.

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.

The architecture of IDS is discussed in the manuscript to achieve the reliable and confidential transmission over MANET which follows some techniques such as Watch Dog, Confident, and CORE.

This paper presents a multi level intrusion detection system (ML-IDS) that uses autonomic computing to automate the control and management of ML-IDS, and employs an efficient fusion decision algorithm to improve the overall detection rate and minimize the occurrence of false alarms.

Describes a real-time intrusion-detection expert system (IDES), that observes user behavior on a monitored computer system and adaptively learns what is normal for individual users, groups, remote hosts, and the overall system behavior. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the expert-system rule base.<<ETX>>

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.

This paper surveys different types of IDS and lists preventive methods, software that automates the intrusion detection process and an intrusion prevention system that can also attempt to stop possible incidents.

The components and features of IDS are introduced, the intrusion detection scheme based on Snort is studied, and the design and deployment of Snort IDS with the platform of Linux is put forward.

It is pointed out that to protect the IDS system, the security defects of the components in the system must be detected dynamically and must be updated in time.

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 proposed Supervisory Control and Data Acquisition (SCADA)-IDS with protocol based and behavior based analysis is proposed and exemplified in order to detect known and unknown cyberattacks from inside or outside SCADA systems.

A method of multi-IDS cooperation to improve detection creditability is presented after analyzing false negative rate and false positive rate of IDS and the result fusion is based on boosting Bayesian classification algorithm.

WSNs would not be with success established if security irresponsibleness and privacy problems are not self-addressed adequately, and WSNs are terribly vulnerable and thus appealing to attacks because of their restricted charges and human-unattended preparation.

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

Different intrusions affecting basics of cloud security i.e. availability, confidentiality and integrity of Cloud resources and services are surveyed.

An intrusion detection system in the design and implementation of a computer immune system that would be built on the framework of the human immune system is successfully realized and in addition a prevention mechanism using the windows IP Firewall feature has been incorporated and the system is able to perform intrusion detection and prevention.

A sound analysis of current limitations and a very comprehensive survey of research papers have been conducted and led to important insights into future research efforts and a proposal for a promising future IDS architecture, which is presented in this work.

The architecture of IDS is discussed in the manuscript to achieve the reliable and confidential transmission over MANET which follows some techniques such as Watch Dog, Confident, and CORE.

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.

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.

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.

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 neural network approaches used for intrusion detection in the recent research papers has been surveyed and an extreme learning approach to solve the training time issue is proposed.

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.

An Intrusion Detection System (IDS) that implements pre-defined algorithms for identifying the attacks over a network and is designed to provide the basic detection techniques so as to secure the systems present in the networks that are directly or indirectly connected to the Internet.