• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.1
• /
• pp.240-259
• /
• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.937-939
• /
• 2014

Recently, an intelligent APT(Advanced Persistent Threat) attack which aims to a special target is getting to be greatly increased. It is very hard to protect with existing intrusion detection methods because of the difficulties to protect the initial intrusion of malicious code. In this paper, we analyze out-bound traffics to prevent call-back step after malicious code intrusion, and propose an APT malicious traffic detection method with considering of trust. The proposed method is expected to provide a basement to improve the detection rate in comparing with that of existing detection methods.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.3
• /
• pp.37-44
• /
• 2011

Recently malicious activities that is a DDoS, spam, propagation of malware, steeling person information, phishing on the Internet are related malicious botnet. To detect malicious botnet, Many researchers study a detection system for malicious botnet, but these applies specific protocol, action or attack based botnet. In this reason, we study a selection of measurement to detec malicious botnet in this paper. we collect a traffic of malicious botnet and analyze it for feature of network traffic. And we select a feature based measurement. we expect to help a detection of malicious botnet through this study.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.9
• /
• pp.47-54
• /
• 2009

Malicious codes, which are spread through WWW are now evolved with various hacking technologies However, detecting technologies for them are seemingly not able to keep up with the improvement of hacking and newly generated malicious codes. In this paper, we define the requirements of detecting systems based on the analysis of malicious codes and their spreading characteristics, and propose an improved detection scheme which monitors HTTP Outbound traffic and detects spreading malicious codes in real time. Our proposed scheme sets up signatures in IDS with confirmed HTML tags and Java scripts which spread malicious codes. Through the verification analysis under the real-attacked environment, we show that our scheme is superior to the existing schemes in satisfying the defined requirements and has a higher detection rate for malicious codes.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.6 s.44
• /
• pp.211-219
• /
• 2006

As the wireless network is popular and expanded, it is necessary to development the IDS(Intrusion Detection System)/Filtering System from the malicious wireless traffic. We propose the W-Sensor SW which detects the malicious wireless traffic and the W-TMS system which filters the malicious traffic by W-Sensor log in this paper. It is efficient to detect the malicious traffic and adaptive to change the security rules rapidly by the proposed W-Sensor SW. The designed W-Sensor by installing on a notebook supports the mobility of IDS in compare with the existed AP based Sensor.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3771-3792
• /
• 2021

With the rapid development of science and technology, several high-performance networks have emerged with various new applications. Consequently, financially or socially motivated attacks on specific networks have also steadily become more complicated and sophisticated. To reduce the damage caused by such attacks, administration of network traffic flow in real-time and precise analysis of past attack traffic have become imperative. Although various traffic analysis methods have been studied recently, they continue to suffer from performance limitations and are generally too complicated to apply in existing systems. To address this problem, we propose a method to calculate the correlation between the malicious and normal flows and classify attack traffics based on the corresponding correlation values. In order to evaluate the performance of the proposed method, we conducted several experiments using examples of real malicious traffic and normal traffic. The evaluation was performed with respect to three metrics: recall, precision, and f-measure. The experimental results verified high performance of the proposed method with respect to first two metrics.

• Journal of KIISE
• /
• v.41 no.9
• /
• pp.617-624
• /
• 2014

Malicious traffic, such as DDoS attack and botnet communications, refers to traffic that is generated for the purpose of disturbing internet networks or harming certain networks, servers, or hosts. As malicious traffic has been constantly evolving in terms of both quality and quantity, there have been many researches fighting against it. In this paper, we propose an effective malicious traffic detection method that exploits the X-means clustering algorithm. We also suggest how to analyze statistical characteristics of malicious traffic and to define metrics that are used when clustering. Finally, we verify effectiveness of our method by experiments with two released traffic data.

• Convergence Security Journal
• /
• v.6 no.4
• /
• pp.49-57
• /
• 2006

Firewall to block a network access of unauthorized IP system and IDS (Intrusion Detection System) to detect malicious code pattern to be known consisted the main current of the information security system at the past. But, with rapid growth the diffusion speed and damage of malicious code like the worm, study of the unknown attack traffic is processed actively. One of such method is detection technique using traffic statistics information on the network viewpoint not to be an individual system. But, it is very difficult but to reserve traffic raw data or statistics information. Therefore, we present extraction technique of a network traffic Raw data and a statistics information like the time series. Also, We confirm the validity of a mixing traffic and show the evidence which is suitable to the experiment.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.69-78
• /
• 2023

With the global rise of digital data, the uncontrolled quantity of data is susceptible to cyber warfare or cyber attacks. Therefore, it is necessary to improve cyber security systems. This research studies the behavior of malicious acts and uses Higuchi Fractal Dimension (HFD), which is a non-linear mathematical method to examine the intricacy of the behavior of these malicious acts and anomalies within the cyber physical system. The HFD algorithm was tested successfully using synthetic time series network data and validated on real-time network data, producing accurate results. It was found that the highest fractal dimension value was computed from the DoS attack time series data. Furthermore, the difference in the HFD values between the DoS attack data and the normal traffic data was the highest. The malicious network data and the non-malicious network data were successfully classified using the Receiver Operating Characteristics (ROC) method in conjunction with a scaling stationary index that helps to boost the ROC technique in classifying normal and malicious traffic. Hence, the suggested methodology may be utilized to rapidly detect the existence of abnormalities in traffic with the aim of further using other methods of cyber-attack detection.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.9 no.4
• /
• pp.95-101
• /
• 2013

MANET has an advantage that can build a network quickly and easily in difficult environment to build network. In particular, routing protocol that uses in existing mobile environment cannot be applied literally because it consists of only mobile node. Thus, routing protocol considering this characteristic is necessary. Malicious nodes do extensive damage to the whole network because each mobile node has to act as a router. In this paper, we propose technique that can detect accurately the suspected node which causes severely damage to the performance of the network. The proposed technique divides the whole network to zone of constant size and is performed simultaneously detection technique based zone and detection technique by collaboration between nodes. Detection based zone translates the information when member node finishes packet reception or transmission to master node managing zone and detects using this. The collaborative detection technique uses the information of zone table managing in master node which manages each zone. The proposed technique can reduce errors by performing detection which is a reflection of whole traffic of network.