• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.47-56
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• 2018

National-scale industrial control systems for gas, electric power, water processing, nuclear power, and traffic control systems increasingly use open networks and open standards protocols based on advanced information and communications technologies. The frequency of cyberattacks increases steadily because of the use of open networks and open standards protocols, but follow-up actions are limited. Therefore, the application of security solutions to an industrial control system is very important. However, it is not possible to apply security solutions to a real system because of the characteristics of industrial control systems. And a security system that can detect attacks without affecting the existing system is imperative. Therefore, in this paper, we propose an intrusion detection system based on packet analysis that can detect anomalous behaviors without affecting the industrial control system, and we verify the effectiveness of the proposed intrusion detection system by applying it in a test bed simulating a real environment.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.12
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• pp.287-296
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• 2019

Due to the development and increased usage of Internet services such as IoT and cloud computing, a large number of packets are being generated on the Internet. In order to create a safe Internet environment, malicious data that may exist among these packets must be processed and detected quickly. In this paper, we apply MongoDB, which is specialized for unstructured data analysis and big data processing, to intrusion detection system for rapid processing of big data security events. In addition, building the intrusion detection system(IDS) using some of the private cloud resources which is the target of protection, elastic and dynamic reconfiguration of the IDS is made possible as the number of security events increase or decrease. In order to evaluate the performance of MongoDB - based IDS proposed in this paper, we constructed prototype systems of IDS based on MongoDB as well as existing relational database, and compared their performance. Moreover, the number of virtual machine has been increased to find out the performance change as the IDS is distributed. As a result, it is shown that the performance is improved as the number of virtual machine is increased to make IDS distributed in MongoDB environment but keeping the overall system performance unchanged. The security event input rate based on distributed MongoDB was faster as much as 60%, and distributed MongoDB-based intrusion detection rate was faster up to 100% comparing to the IDS based on relational database.