• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.103-115
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• 2007

The vulnerability issue on IEEE 802.1x wireless LAN has been permits the malicious attack such as Auth/Deauth flooding more serious rather than we expected. Attacker can generate huge volume of malicious traffic as the same methods on existing wired network. The objective of wireless IP Traceback is to determine the real attack sources, as well as the full path taken by the wireless attack packets. Existing IP Traceback methods can be categorized as proactive or reactive tracing. But, these existing schemes did not provide enhanced performance against DoS attack on wireless traffic. In this paper, we propose a 'TTL based advanced Packet Marking' mechanism for wireless IP Packet Traceback with wireless Classification function. Proposed mechanism can detect and control DoS traffic on AP and can generate marked packet for reconstructing on the real path from the non-spoofed wireless attack source, by which we can construct secure wireless network based on AP with enhance traceback performance.

• KNOM Review
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• v.23 no.1
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• pp.1-9
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• 2020

With the rapid development of science and technology in recent years, the network environment are growing, and a huge amount of traffic is generated. In particular, the development of 5G networks and edge computing will accelerate this phenomenon. However, according to these trends, network malicious behaviors and traffic overloads are also frequently occurring. To solve these problems, network administrators need to build a network management system to implement a high-speed network and should know exactly about the connection topology of network devices through the network management system. However, the existing network topology discovery method is inefficient because it is passively managed by an administrator and it is a time consuming task. Therefore, we proposes a method of network topology discovery according to the amount of in and out network traffic. The proposed method is applied to a real network to verify the validity of this paper.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.89-97
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• 2008

Anomaly detection systems playa significant role in protection mechanism against attacks launched on a network. The greatest challenge in designing systems detecting anomalous exploits is defining what to measure. Effective yet simple, Shannon entropy metrics have been successfully used to detect specific types of malicious traffic in a number of commercially available IDS's. We believe that Renyi entropy measures can also adequately describe the characteristics of a network as a whole as well as detect abnormal traces in the observed traffic. In addition, Renyi entropy metrics might boost sensitivity of the methods when disambiguating certain anomalous patterns. In this paper we describe our efforts to understand how Renyi mutual information can be applied to anomaly detection as an offline computation. An initial analysis has been performed to determine how well fast spreading worms (Slammer, Code Red, and Welchia) can be detected using our technique. We use both synthetic and real data audits to illustrate the potentials of our method and provide a tentative explanation of the results.

• Convergence Security Journal
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• v.5 no.4
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• pp.67-72
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• 2005

The exponential increase of malicious and criminal activities in cyber space is posing serious threat which could destabilize the foundation of modern information society. In particular, unexpected network paralysis or break-down created by the spread of malicious traffic could cause confusion and disorder in a nationwide scale, and unless effective countermeasures against such unexpected attacks are formulated in time, this could develop into a catastrophic condition. In order to solve a same problem, this paper researched early detection techniques for only early warning of cyber threats with separate way the detection due to and existing security equipment from the large network. It researched the cyber example alert system which applies the module of based honeynet from the actual large network and this technique against the malignant traffic how many probably it will be able to dispose effectively from large network.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.818-825
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• 2016

Network management and anomaly detection are challenges in high-speed networks due to the high volume of packets that has to be analysed. Flow-based analysis is a scalable method which reduces the high volume of network traffic by dividing it into flows. As sampling methods are extensively used in flow generators such as NetFlow, the impact of sampling on the performance of flow-based analysis needs to be investigated. Monitoring using sampled traffic is a well-studied research area, however, the impact of sampling on flow-based anomaly detection is a poorly researched area. This paper investigates flow sampling methods and shows that these methods have negative impact on flow-based anomaly detection. Therefore, we propose an efficient probabilistic flow sampling method that can preserve flow traffic distribution. The proposed sampling method takes into account two flow features: Destination IP address and octet. The destination IP addresses are sampled based on the number of received bytes. Our method provides efficient sampled traffic which has the required traffic features for both flow-based anomaly detection and monitoring. The proposed sampling method is evaluated using a number of generated flow-based datasets. The results show improvement in preserved malicious flows.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.29-39
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• 2006

With the increasing popularity of the wireless network, the vulnerability issue on IEEE 802.1x Wireless Local Area Network (WLAN) are more serious than we expected. Security issues range from mis-configured wireless Access Point(AP) such as session hijacking to Denial of Service(DoS) attack. We propose a new system based on intrusion detection or prevention mechanism to protect the wireless network against these attacks. The proposed system has a security solution on AP that includes an intrusion detection and protection system(IDS/IPS) as an embedded module. In this paper, we suggest integrated wireless IDS/IPS module on AP with wireless traffic monitoring, analysis and packet filtering module against malicious wireless attacks. We also present that the system provides both enhanced security and performance such as on the university wireless campus network.

• Smart Media Journal
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• v.13 no.6
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• pp.24-34
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• 2024

If the future can be predicted from network traffic data, which is a time series, it can achieve effects such as efficient resource allocation, prevention of malicious attacks, and energy saving. Many models based on statistical and deep learning techniques have been proposed, and most of these studies have focused on improving model structures and learning algorithms. Another approach to improving the prediction performance of the model is to obtain a good-quality data. With the aim of obtaining a good-quality data, this paper applies a dense sampling technique that augments time series data to the application of network traffic prediction and analyzes the performance improvement. As a dataset, UNSW-NB15, which is widely used for network traffic analysis, is used. Performance is analyzed using RMSE, MAE, and MAPE. To increase the objectivity of performance measurement, experiment is performed independently 10 times and the performance of existing sparse sampling and dense sampling is compared as a box plot. As a result of comparing the performance by changing the window size and the horizon factor, dense sampling consistently showed a better performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.279-292
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• 2015

In this study, we propose an effective network managed security services model that can detect a presence of potential malicious codes inside the energy-based SCADA Systems. Especially, by analyzing the data obtained in the same environment of SCADA Systems, we develop detection factors to applicable to the managed security services and propose the method for the network managed security services. Finally, the proposed network managed security services model through simulation proved possibility to detect malicious traffic in SCADA systems effectively.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.123-132
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• 2021

Malicious activities of Botnets are responsible for huge financial losses to Internet Service Providers, companies, governments and even home users. In this paper, we try to confirm the possibility of detecting botnet traffic by applying the deep learning model Convolutional Neural Network (CNN) using the CTU-13 botnet traffic dataset. In particular, we classify three classes, such as the C&C traffic between bots and C&C servers to detect C&C servers, traffic generated by bots other than C&C communication to detect bots, and normal traffic. Performance metrics were presented by accuracy, precision, recall, and F1 score on classifying both known and unknown botnet traffic. Moreover, we propose a stackable botnet detection system that can load modules for each botnet type considering scalability and operability on the real field.