• Journal of Computing Science and Engineering
• /
• v.5 no.4
• /
• pp.305-313
• /
• 2011

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.6
• /
• pp.1273-1293
• /
• 2010

Immune-inspired intrusion detection is a promising technology for network security, and well known for its diversity, adaptation, self-tolerance, etc. However, scalability and coverage are two major drawbacks of the immune-inspired intrusion detection systems (IIDSes). In this paper, we propose an IIDS framework, named GEP-IIDS, with improved basic system elements to address these two problems. First, an additional bio-inspired technique, gene expression programming (GEP), is introduced in detector (corresponding to detection rules) representation. In addition, inspired by the avidity model of immunology, new avidity/affinity functions taking the priority of attributes into account are given. Based on the above two improved elements, we also propose a novel immune algorithm that is capable of integrating two bio-inspired mechanisms (i.e., negative selection and positive selection) by using a balance factor. Finally, a pruning algorithm is given to reduce redundant detectors that consume footprint and detection time but do not contribute to improving performance. Our experimental results show the feasibility and effectiveness of our solution to handle the scalability and coverage problems of IIDS.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.11 no.4
• /
• pp.33-45
• /
• 2015

Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.4
• /
• pp.39-46
• /
• 2011

This study is conducted to examine the optimal integrated security policy based on network in case of attacks by implementing unique security policies of various network security equipments as an algorithm within one system. To this end, the policies conduct the experiment to implement the optimal security system through the process of mutually integrating the unique defense policy of Firewall, VPN(Virtual Private Network), IDS(Intrusion Detection System), and IPS(Intrusion Prevention System). In addition, this study is meaningful in that it designs integrated mechanism for rapid detection of system load caused by establishment of the security policy and rapid and efficient defense and secures basic network infrastructure implementation.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.1
• /
• pp.93-98
• /
• 2012

MANET composed of only moving nodes is concerned to core technology to construct ubiquitous computing environment. Also, it is a lack of security because of no middle infrastructure. So, it is necessary to intrusion detection system which can track malicious attack. In this study, cluster was used to stable intrusion detection, and rule about various attacks was defined to detect accurately attack that seems like network problem. Proposed method through experience was confirmed that stable detection rate was showed although number of nodes increase.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
• /
• 2001.11a
• /
• pp.147-152
• /
• 2001

This research aims to unravel the significant features of the human immune system, which would be successfully employed for a novel network intrusion detection model. Several salient features of the human immune system, which detects intruding pathogens, are carefully studied and the possibility and the advantages of adopting these features for network intrusion detection are reviewed and assessed.

• Journal of KIISE:Information Networking
• /
• v.30 no.3
• /
• pp.397-406
• /
• 2003

As the information technology grows interests in the intrusion detection system (IDS), which detects unauthorized usage, misuse by a local user and modification of important data, has been raised. In the field of anomaly-based IDS several artificial intelligence techniques such as hidden Markov model (HMM), artificial neural network, statistical techniques and expert systems are used to model network rackets, system call audit data, etc. However, there are undetectable intrusion types for each measure and modeling method because each intrusion type makes anomalies at individual measure. To overcome this drawback of single-measure anomaly detector, this paper proposes a multiple-measure intrusion detection method. We measure normal behavior by systems calls, resource usage and file access events and build up profiles for normal behavior with hidden Markov model, statistical method and rule-base method, which are integrated with a rule-based approach. Experimental results with real data clearly demonstrate the effectiveness of the proposed method that has significantly low false-positive error rate against various types of intrusion.

• The KIPS Transactions:PartC
• /
• v.10C no.1
• /
• pp.1-10
• /
• 2003

In this paper, we propose a secure communication framework for interaction and information sharing between a server and agents in DS-NVSA(Detection System of Network Vulnerability Scan Attacks) proposed in〔1〕. For the scalability and interoperability with other detection systems, we design the proposed IDMEF and IAP that have been drafted by IDWG. We adapt IDMEF and IAP to the proposed framework and provide SKTLS(Symmetric Key based Transport Layer Security Protocol) for the network environment that cannot afford to support public-key infrastructure. Our framework provides the reusability of heterogeneous intrusion detection systems and enables the scope of intrusion detection to be extended. Also it can be used as a framework for ESM(Enterprise Security Management) system.

• Journal of Information Processing Systems
• /
• v.6 no.4
• /
• pp.481-500
• /
• 2010

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT's Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.8
• /
• pp.1346-1351
• /
• 2002

It is very complex to construct Intrusion Detection System in distributed network environment than simple ones. Especially, In the collecting and analysis of logdata from out different operating system break out much problem. So In this paper, We present a Intrusion Detection System model applying agent teaming system to solve these problem. We apply the data Mining algorithm for agent learning.