• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.282-288
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• 2005

In this paper, we propose a new intrusion detection model, which keeps advantages of existing misuse detection model and anomaly detection model and resolves their problems. This new intrusion detection system, named to MMIDS, was designed to satisfy all the following requirements : 1) Fast detection of new types of attack unknown to the system; 2) Provision of detail information about the detected types of attack; 3) cost-effective maintenance due to fast and efficient learning and update; 4) incrementality and scalability of system. The fast and efficient training and updating faculties of proposed novel multi-class SVM which is a core component of MMIDS provide cost-effective maintenance of intrusion detection system. According to the experimental results, our method can provide superior performance in separating similar patterns and detailed separation capability of MMIDS is relatively good.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.1
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• pp.115-122
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• 2006

The most methods for intrusion detection are based on the misuse detection which accumulates hewn intrusion information and makes a decision of an attack against any behavior data. However it is very difficult to detect a new or modified aoack with only the collected patterns of attack behaviors. Therefore, if considering that the method of anomaly behavior detection actually has a high false detection rate, a new approach is required for very huge intrusion patterns based on sequence. The approach can improve a possibility for intrusion detection of known attacks as well as modified and unknown attacks in addition to the similarity measurement of intrusion patterns. This paper proposes a method which applies the multiple sequence alignments technique to the similarity matching of the sequence based intrusion patterns. It enables the statistical analysis of sequence patterns and can be implemented easily. Also, the method reduces the number of detection alerts and false detection for attacks according to the changes of a sequence size.

• Convergence Security Journal
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• v.12 no.4
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• pp.77-84
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• 2012

The control network has been operated in a closed. But it changes to open to external for business convenience and cooperation with several organizations. As the way of connecting with user extends, the risk of control network gets high. Thus, in this paper, proposed the technique of an anomalous event detection using white-list for control network security and minimizing the cyber threats. The proposed method can be collected and cataloged of only normal data from traffic of internal network, control network and field devices. Through way to check the this situation, we can separate normal and abnormal behavior.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1674-1688
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• 2023

The remarkable advancement of quantum steganography offers enhanced security for quantum communications. However, there is a significant concern regarding the potential misuse of this technology. Moreover, the current research on identifying malicious quantum steganography is insufficient. To address this gap in steganalysis research, this paper proposes a specialized quantum steganalysis algorithm. This algorithm utilizes quantum machine learning techniques to detect steganography in general quantum secure communication schemes that are based on pure states. The algorithm presented in this paper consists of two main steps: data preprocessing and automatic discrimination. The data preprocessing step involves extracting and amplifying abnormal signals, followed by the automatic detection of suspicious quantum carriers through training on steganographic and non-steganographic data. The numerical results demonstrate that a larger disparity between the probability distributions of steganographic and non-steganographic data leads to a higher steganographic detection indicator, making the presence of steganography easier to detect. By selecting an appropriate threshold value, the steganography detection rate can exceed 90%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.9-15
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• 2013

Recently, Intrusion detection system is an important technology in computer network system because of has seen a dramatic increase in the number of attacks. The most of intrusion detection methods do not detect intrusion on real-time because difficult to analyze an auditing data for intrusions. A network intrusion detection system is used to monitors the activities of individual users, groups, remote hosts and entire systems, and detects suspected security violations, by both insider and outsiders, as they occur. It is learns user's behavior patterns over time and detects behavior that deviates from these patterns. In this paper has rule-based component that can be used to encode information about known system vulnerabilities and intrusion scenarios. Integrating the two approaches makes Intrusion Detection System a comprehensive system for detecting intrusions as well as misuse by authorized users or Anomaly users (unauthorized users) using RFM analysis methodology and monitoring collect data from sensor Intrusion Detection System(IDS).

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.387-399
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• 2009

Mobile wireless networks continue to be plagued by theft of identify and intrusion. Both problems can be addressed in two different ways, either by misuse detection or anomaly-based detection. In this paper, we propose a dissimilarity-based anomaly detection method which can effectively identify abnormal behavior such as mobility patterns of mobile wireless networks. In the proposed algorithm, a normal profile is constructed from normal mobility patterns of mobile nodes in mobile wireless networks. From the constructed normal profile, a dissimilarity is computed by a weighted dissimilarity measure. If the value of the weighted dissimilarity measure is greater than the dissimilarity threshold that is a system parameter, an alert message is occurred. The performance of the proposed method is evaluated through a simulation. From the result of the simulation, we know that the proposed method is superior to the performance of other anomaly detection methods using dissimilarity measures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.391-394
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• 2004

Change of paradigm of network attack technique was begun by fast extension of the latest Internet and new attack form is appearing. But, Most intrusion detection systems detect informed attack type because is doing based on misuse detection, and active correspondence is difficult in new attack. Therefore, to heighten detection rate for new attack pattern, visibilitys to apply human immunity mechanism are appearing. In this paper, we create self-file from normal behavior profile about network packet and embody self recognition algorithm to use self-nonself discrimination in the human immune system to detect anomaly behavior. Sense change because monitors self-file creating anomaly detector based on Negative Selection Algorithm that is self recognition algorithm's one and detects anomaly behavior. And we achieve simulation to use DARPA Network Dataset and verify effectiveness of algorithm through the anomaly detection rate.

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

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.121-128
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• 2019

Computing systems have various vulnerabilities to cyber attacks. In particular, various cyber attacks that are intelligent in the information society have caused serious social problems and economic losses. Traditional security systems are based on misuse-based technology, which requires the continuous updating of new attack patterns and the real-time analysis of vast amounts of data generated by numerous security devices in order to accurately detect. However, traditional security systems are unable to respond through detection and analysis in real time, which can delay the recognition of intrusions and cause a lot of damage. Therefore, there is a need for a new security system that can quickly detect, analyze, and predict the ever-increasing cyber security threats based on machine learning and big data analysis models. In this paper, we present a IDS model that combines machine learning and big data technology.