Research on Dynamic Intrusion Detection Model Based on Risk Coefficient

2010 ◽  
Vol 129-131 ◽  
pp. 124-127 ◽  
Author(s):  
Zheng Wei ◽  
Jun Yi Hou ◽  
Hua Tan ◽  
Guang Nan Guo
Keyword(s):  
Intrusion Detection ◽  
Detection Method ◽  
System Call ◽  
Security Technology ◽  
Detection Model ◽  
Risk Coefficient ◽  
Detection Technology ◽  
System Risk ◽  
Effectiveness And Efficiency ◽  
Definition Of

Intrusion detection technology is a kind of network security technology that can protect system from attacks. Based on the definition of system call risk coefficient, the paper brought out a system risk coefficient based dynamic intrusion detection model. Using the model, the drawbacks of traditional intrusion detection method based on system call was solved, which speeds up detection process and decreased false rate and error rate. It can also effectively identify error operations or users. The experiment result also proves the effectiveness and efficiency of the method.

The Application of BP Neural Network in Network Intrusion Detection

2013 ◽  
Vol 765-767 ◽  
pp. 1415-1418 ◽  
Author(s):  
Ya Fang Lou ◽  
Zhi Jun Yuan ◽  
Hao Wu
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Practical Significance ◽  
Network Intrusion Detection ◽  
Critical Problem ◽  
Detection Model ◽  
Detection Systems ◽  
Network Intrusion ◽  
Detection Technology

As the network is impacting enormously to all aspects of society, the network security becomes a critical problem. The traditional intrusion detection technology exists some disadvantages: the imperfection of architecture, the slow detecting of system, the vulnerable of itself architecture, and so on. This paper presents an intrusion detection model based on BP neural network which has the incomparable advantages against traditional intrusion detection systems. Therefore, the study of this subject possesses the practical significance.

Real-Time Intrusion Detection Based on System Call and ARTIS Model

2014 ◽  
Vol 926-930 ◽  
pp. 3157-3160
Author(s):  
Zhan Huang ◽  
Yu Ying Jiang ◽  
Lu Bin Li
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Methods ◽  
System Call ◽  
Detection Model ◽  
Fixed Length ◽  
Computer Intrusion Detection ◽  
Abnormal Behaviors

The main purpose of a computer intrusion detection system is to accurately distinguish between self and non-self. A novel intrusion detection model based on ARTIS model is proposed by introducing the Red Flower and Green Leaf concepts, and by coordinated use of RF variable length and GL fixed length detectors. Intrusion detection methods are optimized to ensure the quick detection of abnormal behaviors making the model more suitable for real-time intrusion detection and more accurately to distinguish between self-and non-self.

A Network Intrusion Detection Method Inspired by Biological Immunology

2010 ◽  
Vol 121-122 ◽  
pp. 482-485
Author(s):  
Rong Deng ◽  
Xiu Yin Zhang
Keyword(s):  
Intrusion Detection ◽  
Detection Method ◽  
Network Intrusion Detection ◽  
Antibody Concentration ◽  
Traffic Patterns ◽  
Immune Systems ◽  
Detection Model ◽  
Network Intrusion ◽  
Proposed Model ◽  
Dynamic Relationships

In this article an Immune Based Intrusion Detection Model (IBIDM) was built to simulate the dynamic relationships between the intrusion antigen intensity and the antibody concentration in the biological immune systems. In IBIDM, traditional detection rules and network traffic patterns are mapped to antibodies and antigens respectively. The network security situation is presented in the form of detector numbers to help reduce false alarm rate. Computer simulations show that the proposed model is effective for intrusion detection.

A Case Study of Distributed Network Fault Detection Technology in Distance Education

2013 ◽  
Vol 760-762 ◽  
pp. 1282-1287
Author(s):  
Qian Jun Tang ◽  
Yan Zhang ◽  
Yong Ju Li
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
High Efficiency ◽  
Detection System ◽  
Attribute Reduction ◽  
Security Technology ◽  
Whole Process ◽  
Detection Technology ◽  
Network Fault

The intrusion detection under the environment of IPv6 is an important security technology along with firewall in system security defense system, which can be used for real-time detection and monitoring of the system in the whole process of system invasion. This paper puts forward an intrusion detection system under IPv6 platform based on intrusion detection feature attribute reduction by using pattern matching, so as to expand the range of application and user group of the security products. By the analysis and comparison of various pattern matching algorithms, the new algorithm realizes the intrusion feature module matching under IPv6, and make detection system be of high efficiency. Later experiments have proved this view.

Analysis of Intrusion Detection System

2011 ◽  
Vol 460-461 ◽  
pp. 451-454
Author(s):  
Yue Sheng Gu ◽  
Hong Yu Feng ◽  
Jian Ping Wang
Keyword(s):  
Information Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Future Research ◽  
Detection Model ◽  
Detection Systems ◽  
Detection Technology ◽  
Technology Concepts

Intrusion detection system is an important device of information security. This article describes intrusion detection technology concepts, classifications and universal intrusion detection model, and analysis of the intrusion detection systems weaknesses and limitations. Finally, some directions for future research are addressed.

scholarly journals Study of Immune-Based Intrusion Detection Technology in Virtual Machines for Cloud Computing Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Ruirui Zhang ◽  
Xin Xiao
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Virtual Machine ◽  
Virtual Machines ◽  
The State ◽  
Signal Acquisition ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Model ◽  
Detection Technology

Cloud computing platforms are usually based on virtual machines as the underlying architecture; the security of virtual machine systems is the core of cloud computing security. This paper presents an immune-based intrusion detection model in virtual machines of cloud computing environment, denoted as IB-IDS, to ensure the safety of user-level applications in client virtual machines. In the model, system call sequences and their parameters of processes are used, and environment information in the client virtual machines is extracted. Then the model simulates immune responses to ensure the state of user-level programs, which can detect attacks on the dynamic runtime of applications and has high real-time performance. There are five modules in the model: antigen presenting module, signal acquisition module, immune response module, signal measurement module, and information monitoring module, which are distributed into different levels of virtual machine environment. Performance analysis and experimental results show that the model brings a small performance overhead for the virtual machine system and has a good detection performance. It is applicable to judge the state of user-level application in guest virtual machine, and it is feasible to use it to increase the user-level security in software services of cloud computing platform.

Anomaly Intrusion Detection Based on Concept Lattice

2012 ◽  
Vol 220-223 ◽  
pp. 2388-2392
Author(s):  
Li Fang Wang
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Concept Lattice ◽  
Attribute Reduction ◽  
Training Data ◽  
Detection Model ◽  
Detection Technology ◽  
Anomaly Intrusion Detection

In order to identify potential and effective intrusion detection rules, and improve the detection rate of intrusion detection system, this paper combines the concept lattice with intrusion detection technology and proposes a anomaly intrusion detection system based on concept lattice theory. The system first pre-treats those collected data, regulates data and builds concept lattice using the minimal set of attributes which are obtained by attribute reduction. And it analyzes the implication relations between concepts and obtains non-redundant classification rules. The anomaly intrusion detection model based compared with other tests can easily get training data. Experimental results show the model reduces the computation amount to achieve classification, improves the intrusion detection rate and effectively controls the false detection rate.

An Artificial Immune-Based Distributed Intrusion Detection Model for the Internet of Things

2011 ◽  
Vol 366 ◽  
pp. 165-168 ◽  
Author(s):  
Run Chen ◽  
Cai Ming Liu ◽  
Chao Chen
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Theory Model ◽  
The Internet ◽  
Artificial Immune ◽  
Detection Model ◽  
Detection Technology ◽  
Proposed Model ◽  
Distributed Intrusion Detection ◽  
The Internet Of Things

Traditional detection technology for network attacks is difficult to adapt the complicated and changeful environment of the Internet of Things (IoT). In the interest of resolving the distributed intrusion detection problem of IoT, this paper proposes an artificial immune-based theory model for distributed intrusion detection in IoT. Artificial immune principles are used to solve the problem of IoT intrusion detection. Antigen, self, non-self and detector in the IoT environment are defined. Good immune mechanisms are simulated. Detector is evolved dynamically to make the proposed model have self-learning and self-adaptation. The outstanding detectors which have accepted training are shared in the whole IoT to adapt the local IoT environment and improve the ability of global intrusion detection in IoT. The proposed model is expected to realize detecting intrusion of IoT in distribution and parallelity.

Sign in / Sign up

Export Citation Format

Share Document