• The KIPS Transactions:PartC
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• v.12C no.7 s.103
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• pp.1007-1014
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• 2005

A generation of rules or patterns for detecting attacks from network is very difficult. Detection rules and patterns are usually generated by Expert's experiences that consume many man-power, management expense, time and so on. This paper proposes statistical methods that effectively detect intrusion and attacks without expert's experiences. The methods are to select useful measures in measures of network connection(session) and to detect attacks. We extracted the network session data of normal and each attack, and selected useful measures for detecting attacks using relative entropy. And we made probability patterns, and detected attacks using likelihood ratio testing. The detecting method controled detection rate and false positive rate using threshold. We evaluated the performance of the proposed method using KDD CUP 99 Data set. This paper shows the results that are to compare the proposed method and detection rules of decision tree algorithm. So we can know that the proposed methods are useful for detecting Intrusion and attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.5
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• pp.507-526
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• 2009

Since SIP uses a text-based message format and is open to the public Internet, it provides a number of potential opportunities for Denial of Service (DoS) attacks in a similar manner to most Internet applications. In this paper, we propose an effective detection method for SIP flooding attacks in order to deal with the problems of conventional schemes. We derive the upper bound of the possible number of SIP messages, considering not only the network congestion status but also the different properties of individual SIP messages such as INVITE, BYE and CANCEL. The proposed method can be easily extended to detect flooding attacks by other SIP messages.

• International journal of advanced smart convergence
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• v.5 no.4
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• pp.54-56
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• 2016

Code-reuse attacks are very dangerous in various systems. This is because they do not inject malicious codes into target systems, but reuse the instruction sequences in executable files or libraries of target systems. Moreover, code-reuse attacks could be more harmful to IoT systems in the sense that it may not be easy to devise efficient and effective mechanism for code-reuse attack detection in resource-restricted IoT devices. In this paper, we propose a detection scheme with using Kullback-Leibler (KL) divergence to combat against code-reuse attacks in IoT. Specifically, we detect code-reuse attacks by calculating KL divergence between the probability distributions of the packets that generate from IoT devices and contain code region addresses in memory system and the probability distributions of the packets that come to IoT devices and contain code region addresses in memory system, checking if the computed KL divergence is abnormal.

• ETRI Journal
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• v.40 no.1
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• pp.133-145
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• 2018

Robustness, payload, and imperceptibility of audio watermarking algorithms are contradictory design issues with high-level security of the watermark. In this study, the major issue in achieving high payload along with adequate robustness against challenging signal-processing attacks is addressed. Moreover, a security code has been strategically used for secure transmission of data, providing tamper detection at the receiver end. The high watermark payload in this work has been achieved by using the complementary features of third-level detailed coefficients of discrete wavelet transform where the human auditory system is not sensitive to alterations in the audio signal. To counter the watermark loss under challenging attacks at high payload, Daubechies wavelets that have an orthogonal property and provide smoother frequencies have been used, which can protect the data from loss under signal-processing attacks. Experimental results indicate that the proposed algorithm has demonstrated adequate robustness against signal processing attacks at 4,884.1 bps. Among the evaluators, 87% have rated the proposed algorithm to be remarkable in terms of transparency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2512-2531
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• 2014

We propose a new Distributed Denial of Service (DDoS) defense mechanism that protects http web servers from application-level DDoS attacks based on the two methodologies: whitelist-based admission control and busy period-based attack flow detection. The attack flow detection mechanism detects attach flows based on the symptom or stress at the server, since it is getting more difficult to identify bad flows only based on the incoming traffic patterns. The stress is measured by the time interval during which a given client makes the server busy, referred to as a client-induced server busy period (CSBP). We also need to protect the servers from a sudden surge of attack flows even before the malicious flows are identified by the attack flow detection mechanism. Thus, we use whitelist-based admission control mechanism additionally to control the load on the servers. We evaluate the performance of the proposed scheme via simulation and experiment. The simulation results show that our defense system can mitigate DDoS attacks effectively even under a large number of attack flows, on the order of thousands, and the experiment results show that our defense system deployed on a linux machine is sufficiently lightweight to handle packets arriving at a rate close to the link rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.449-455
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• 2021

This paper proposes an encryption web transaction attack detection system based on the existing web application monitoring system. Although there was difficulty in detecting attacks on the encrypted web traffic because the existing web traffic security systems detect and defend attacks based on encrypted packets in the network area of the encryption section between the client and server, by utilizing the technology of the web application monitoring system, it is possible to detect various intelligent cyber-attacks based on information that is already decrypted in the memory of the web application server. In addition, since user identification is possible through the application session ID, statistical detection of attacks such as IP tampering attacks, mass web transaction call users, and DDoS attacks are also possible. Thus, it can be considered that it is possible to respond to various intelligent cyber attacks hidden in the encrypted traffic by collecting and detecting information in the non-encrypted section of the encrypted web traffic.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.261-267
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• 2001

In this paper, we have illustrated implementations and there results of network attacks and detections. We consider two attacks, smurf attach and network mapping attack, which are one of the typical intrusions using the ICMP The NFR/sup TM/ is used to capture all of our interesting packets within the network traffic. We implement the smurf and network mapping attacks with the UNIX raw socket, and build the NFR's backend for it's detection. The N-Code programming is used to build the backend. The implementing results show the possibility of preventing illegal intruding to network systems.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.294-300
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• 2008

This study was conducted to investigate what to consider for active response in the intrusion detection system, how to implement active response, and 6-phase response models to respond actively, including the active response scheme to detect unknown attacks by using a traffic measuring engine and an anomaly detection engine.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.55-64
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• 2023

The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST²¹ and KDDTEST⁺ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST⁺ than KDDTEST²¹ because there are few attacks from minor samples in KDDTEST⁺. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.93-98
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• 2018

The Internet of Things (IoT) is a form of the emerging 4th industry in the 5G era. IoT is expected to develop naturally in our daily life in the 5G era in which high-speed communication will be completed. Along with the rise of IoT, concerns about security and malicious attacks are also increasing. This paper examines DoS attacks, which are one of the representative security threats of IoT and proposes a local detection and blocking system that are suitable for response to such attacks. First, systems of the LoRaWAN type, which are most actively researched in the IoT system field and DoS attacks that can occur in such systems were examined. Then, the inverse order tree algorithm using regional characteristics was designed as a cluster analysis form. Finally, a system capable of defending denial-of-service attacks in the 5G IoT system using local detection and blocking with the Euclidean distance was designed.