• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.7 s.349
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• pp.84-92
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• 2006

To defeat abnormal traffic driven by DoS (Denial-of-Service) or DDoS (Distributed DoS), there has been a variety of researches or studies in a few decades. In this paper, we present the results of theoretical performance analysis between attack prevention schemes and attack mitigation schemes. The former is a scheme that prevents abnormal incoming traffic from forwarding into a specific network based on filtering rules, and the latter is a scheme that makes some perimeter or intermediate routers, which exist on the traffic forwarding path, prevent abnormal traffic based on their own abnormal traffic information, or that mitigates abnormal traffic by using quality-of-service mechanisms at the gateway of the target network. The aspects of theoretical performance analysis are defined as the transit rates of either normal traffic or false-positive traffic after an attack detection routine processes its job, and we also present the concrete network bandwidth rates to control incoming traffic.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.9 s.339
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• pp.85-96
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• 2005

A ' Virtual Private Network (YPN) over Internet' has the benefits of being cost-effective and flexible. However, given the increasing demands for high bandwidth Internet and for reliable services in a 'VPN over Intemet,' an IP/GMPLS over DWDM backbone network is regarded as a very favorable approach for the future 'Optical VPN (OVPN)' due to the benefits of transparency and high data rate. Nevertheless, OVPN still has survivability issues such that a temporary fault can lose a large amount of data in seconds, moreover unauthorized physical attack can also be made on purpose to eavesdrop the network through physical components. Also, logical attacks can manipulate or stop the operation of GMPLS control messages and menace the network survivability of OVPN. Thus, network survivability in OVPN (i.e. fault/attack tolerant recovery mechanism considering physical structure and optical components, and secured transmission of GMPLS control messages) is rising as a critical issue. In this Paper, we propose a new path establishment scheme under shared risk link group (SRLG) constraint for physical network survivability. And we also suggest a new logical survivability management mechanism by extending resource reservation protocol-traffic engineering extension (RSVP-TE+) and link management protocol (LMP). Finally, according to the results of our simulation, the proposed algorithms are revealed more effective in the view point of survivability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.550-558
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• 2004

In order to design network node for guaranteeing availability against network based DoS attack, some restrictions such as the relationship analysis on upper and lower layer bandwidth, buffer capacity, attack resources, a number of attack session and loss probability are analyzed. And then, to make good use of network resource, the relationship between required resources for satisfying loss probability and cost is discussed. The results of this study are expected to be applied to the effective security node design against network DoS.

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

• The Journal of Information Systems
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• v.29 no.2
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• pp.221-242
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• 2020

Purpose This study collects data of the recently activated online black market and analyzes it to present a specific method for preparing for a hacking attack. This study aims to make safe from the cyber attacks, including hacking, from the perspective of individuals and businesses by closely analyzing hacking methods and tools in a situation where they are easily shared. Design/methodology/approach To prepare for the hacking attack through the online black market, this study uses the routine activity theory to identify the opportunity factors of the hacking attack. Based on this, text mining and social network techniques are applied to reveal the most dangerous areas of security. It finds out suitable targets in routine activity theory through text mining techniques and motivated offenders through social network analysis. Lastly, the absence of guardians and the parts required by guardians are extracted using both analysis techniques simultaneously. Findings As a result of text mining, there was a large supply of hacking gift cards, and the demand to attack sites such as Amazon and Netflix was very high. In addition, interest in accounts and combos was in high demand and supply. As a result of social network analysis, users who actively share hacking information and tools can be identified. When these two analyzes were synthesized, it was found that specialized managers are required in the areas of proxy, maker and many managers are required for the buyer network, and skilled managers are required for the seller network.

• ETRI Journal
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• v.33 no.5
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• pp.770-779
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• 2011

Security quad and cube (SQC) is a network attack analyzer that is capable of aggregating many different events into a single significant incident and visualizing these events in order to identify suspicious or illegitimate behavior. A network administrator recognizes network anomalies by analyzing the traffic data and alert messages generated in the security devices; however, it takes a lot of time to inspect and analyze them because the security devices generate an overwhelming amount of logs and security events. In this paper, we propose SQC, an efficient method for analyzing network security through visualization. The proposed method monitors anomalies occurring in an entire network and displays detailed information of the attacks. In addition, by providing a detailed analysis of network attacks, this method can more precisely detect and distinguish them from normal events.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.1
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• pp.49-57
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• 2017

IoT can be connected through a single network not only objects which can be connected to existing internet but also objects which has communication capability. This IoT environment will be a huge change to the existing communication paradigm. However, the big security problem must be solved in order to develop further IoT. Security mechanisms reflecting these characteristics should be applied because devices participating in the IoT have low processing ability and low power. In addition, devices which perform abnormal behaviors between objects should be also detected. Therefore, in this paper, we proposed D-IDS technique for efficient detection of malicious attack nodes between devices participating in the IoT. The proposed technique performs the central detection and distribution detection to improve the performance of attack detection. The central detection monitors the entire network traffic at the boundary router using SVM technique and detects abnormal behavior. And the distribution detection combines RSSI value and reliability of node and detects Sybil attack node. The performance of attack detection against malicious nodes is improved through the attack detection process. The superiority of the proposed technique can be verified by experiments.

• Convergence Security Journal
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• v.11 no.6
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• pp.25-30
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• 2011

MANET has a weak construction in security more because it is consisted of only moving nodes and doesn't have central management system. The DDoS attack is a serious attack among these attacks which threaten wireless network. The DDoS attack has various object and trick and become intelligent. In this paper, we propose the technique to raise DDoS detection rate by classifying abnormal traffic pattern. Cluster head performs sentinel agent after nodes which compose MANET are made into cluster. The decision tree is applied to detect abnormal traffic pattern after the sentinel agent collects all traffics and it judges traffic pattern and detects attack also. We confirm high attack detection rate of proposed detection technique in this study through experimentation.