Intrusion Detection System Technology Research Articles

Application of deep learning-based Intrusion Detection System (IDS) in network anomaly traffic detection

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, reviewing traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to improve abnormal traffic detections accuracy and efficiency effectively. 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.

Intrusion Detection System using Ensemble Learning Approaches: A Systematic Literature Review

Undoubtedly, the advancements in Machine Learning (ML) and especially ensemble learning models enable researchers to develop numerous fields in ways we had never imagined before. Intrusion Detection System (IDS) is one of these fields that benefits from the aforementioned techniques to identify and classify the security threats with more accuracy. In fact, IDS technology has become an essential component for any defense strategy, since it provides a healthy environment for businesses. Besides, it protects future network infrastructures from intruders and suspicious network activities. In this context, this paper presents a systematic literature review on ensemble learning-based IDS and the datasets used to evaluate the proposed methods. Furthermore, we illustrate the distribution of the reviewed papers by year of publication, datasets utilized and ensemble learning methods. Finally, we discuss the finding of this review and highlight some limitations of the aforementioned models. The overall purpose of this literature review is to provide a guideline on how to choose ensemble learning models to solve IDS issues based on the most efficient and recent trends

SC-IDT: SOFT COMPUTING BASED INTRUSION DETECTION TECHNOLOGY IN SMART HOME SECURITY SYSTEM

In this paper a theoretical model is introduced for the purpose of home security using mamdani system in environment. Paper defines the architecture for such a system, in which the technological combination of computing and soft computing form a home security model. This model is inspired by Intrusion Detection System (IDS) and context awareness (ubicom) system. IDS categorize the normal and intrusive (malwares) data from the network traffic data and allow the normal data to be pass to the system and block the intrusive data. Ubiquitous computing is latest trend where computing can occur using any device, in any location and in any format i.e. everywhere and anywhere. By combining ubiquitous computing, IDS, and fuzzy technologies together a theoretical model of smart home security is introduced. Model consists of input parameters, output parameters and membership functions.

The Study and Simulation on Campus Network Intrusion Detection System

In this paper, the current intrusion detection systems are analyzed in the full study of the development trend of domestic and foreign country. According to the campus network can be divided into functional independence of the structural characteristics of the subnet, while taking full advantage of agent technology in the intrusion detection system technology, we have referenced to the agent technology and a variety of detection methods for the analysis and comparison, and have analyzed the existing distributed intrusion detection system ,we propose a monitoring and management center with a multi-agent intrusion detection model framework. This model uses a distributed architecture that combines network-and host-based intrusion detection method for intrusion detection.

Getting the NAC: Cisco's Bob Gleichauf speaks out

Robert Gleichauf is responsible for the development of secure network infrastructures across Cisco's product line. Most recently, he led the development of Cisco's Network Admission Control (NAC) initiative. He has led other security initiatives at Cisco, including overseeing the R&D for Cisco's Intrusion Detection System (IDS) product line, and the migration of IDS technology into the Cisco Catalyst 6000 platform. Gleichauf joined Cisco with the WheelGroup acquisition in 1998, where he was the head of product engineering. Prior to WheelGroup, he was a senior engineering manager at startup IQ Software, which developed database report writing tools. He spoke at the Gartner IT Security Summit in London in the autumn, and spoke to Brian McKenna for Infosecurity Today about the trials of decrypting data in crisis situations, security officers of a new type, and the challenge of vendor interoperability.

The effect of intrusion detection management methods on the return on investment

This paper examines how implementation methods, management methods, and Intrusion Detection System (IDS) policy affect Return on Investment (ROI). The paper will seek to demonstrate the value associated with a well thought out implementation and effective lifecycle management of IDS technology and will culminate in a case study with a number crunching exercise to calculate the ROI for an IDS deployment by a hypothetical financial company named UTVE, Inc. on risk. The paper also discusses general IDS types and expands on the impact that the logical location of a company's critical networked assets could have on the risk equations. To this end, the Cascading Threat Multiplier (CTM) is introduced to expand on the Single Loss Expectancy (SLE) equation. Also, implementation and management costs based on various support profiles and commonly accepted risk equations are reviewed. Finally, a formula for calculating ROI for security, otherwise commonly known as Return on Security Investment (ROSI) is devised.

D309 平安京ビューによるIDSデータの視覚化

IDS (Intrusion Detection System) is an active research topic for the purpose of cost reduction of security maintenance of computer network. However, existing IDS technologies still have some issues, including enormous log output data, and lack of analysis technologies of complicated behavior of recent intrusions. This report proposes a technique to support understanding and exploration of such behavior of intrusions, by applying an information visualization technique. The technique constructs hierarchical data according to IP addresses of computers in a target network, and visualizes the data by Heiankyo-view, which is a new technique for hierarchical data visualization. By mapping statistics of intrusions onto the visualization display, we could observe some behavior of real intrusions.