• Journal of Communications and Networks
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• v.14 no.3
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5574-5591
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• 2017

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

• The KIPS Transactions:PartC
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• v.11C no.2
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• pp.157-162
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• 2004

Recently, the growth of information infrastructure is getting fatter and faster. At the same time, the security accidents are increasing together. We have problem that do not handle traffic because we have the Intrusion Detection Systems in low speed environment. In order to overcome this, we need effective security analysis techniques that ran Processed data of high-capacity because high speed network environment. In this paper we proposed the Gigabit Intrusion Detection System for coordinated security function such as intrusion detection, response on the high speed network. We suggested the detection mechanism in high speed network environment that have pattern matching function based packet header and based packet data that is proceeded in system kernel area, we are shown that this mechanism was excellent until maximum 20 times than existing system in traffic processing performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.158-163
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• 2012

This paper proposes an FHIDS(Fuzzy logic based Hybrid Intrusion Detection System) design that detects anomaly and misuse attacks by using a Naive Bayesian algorithm, Data Mining, and Fuzzy Logic. The NB-AAD(Naive Bayesian based Anomaly Attack Detection) technique using a Naive Bayesian algorithm within the FHIDS detects anomaly attacks. The DM-MAD(Data Mining based Misuse Attack Detection) technique using Data Mining within it analyzes the correlation rules among packets and detects new attacks or transformed attacks by generating the new rule-based patterns or by extracting the transformed rule-based patterns. The FLD(Fuzzy Logic based Decision) technique within it judges the attacks by using the result of the NB-AAD and DM-MAD. Therefore, the FHIDS is the hybrid attack detection system that improves a transformed attack detection ratio, and reduces False Positive ratio by making it possible to detect anomaly and misuse attacks.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.35.4-35
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• 2002

$\textbullet$ Intrusion Detection Algorithm based on Artificial Immune System 1. Introduction 2. Research Background 3. The adaptation algorithm of SYN flooding attack 4. SIMULATION 5. Conclusion 6. References

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.31.1-31
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• 2001

Computer security is a very important issue these days. Computer viruses, worms, Trojan horses, and cracking are prevalent and causing serious damages. There are also many ways developed to defend against such attacks including cryptography and firewalls. However, it is not possible to guarantee complete security of computer systems or networks. Recently much attention has been directed to ways to detect intrusions and recover from damages. Although there have been a lot of research efforts to develop efficient intrusion detection systems, little has been done to facilitate the interaction between intrusion detection systems and users ...

• The KIPS Transactions:PartC
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• v.10C no.2
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• pp.119-124
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• 2003

Although there have been many studies on using finite automata for intrusion detection, it has been a difficult problem to generate compact finite automata automatically. In a previous research an approach to profile normal behaviors using finite automata was proposed. They divided the system call sequence of each process into three parts prefix, main portion, and suffix, and then substituted macros for frequently occurring substrings. However, the procedure was not automatic. In this paper we present algorithms to automatically generate intrusion detection automata from the sequence of system calls resulting from the normal runs of the programs. We also show the effectiveness of the proposed method through experiments.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.157-164
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• 2006

The Recently, most of Internet attacks are zero-day types of the unknown attacks by Malware. Using already known Misuse Detection Technology is hard to cope with these attacks. Also, the existing information security technology reached the limits because of various attack's patterns over the Internet, as web based service became more affordable, web service exposed to the internet becomes main target of attack. This paper classifies the traffic type over the internet and suggests the Threat Management System(TMS) including the anomaly intrusion detection technologies which can detect and analyze the anomaly sign for each traffic type.