• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.1-17
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• 2014

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce radio spectrum resources. However, most of CSS schemes assume all secondary users (SU) are honest, and thus offering opportunities for malicious SUs to launch the spectrum sensing data falsification attack (SSDF attack). To combat such misbehaved behaviors, recent efforts have been made to trust schemes. In this paper, we argue that powering CSS with traditional trust schemes is not enough. The rational SSDF attack is found in this paper. Unlike the simple SSDF attack, rational SSDF attackers send out false sensing data on a small number of interested primary users (PUs) rather than all PUs. In this case, rational SSDF attackers can keep up high trustworthiness, resulting in difficultly detecting malicious SUs in the traditional trust schemes. Meanwhile, a defense scheme using a novel trust approach is proposed to counter rational SSDF attack. Simulation results show that this scheme can successfully reduce the power of rational SSDF, and thus ensure the performance of CSS.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.107-114
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• 2023

The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.77-80
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• 2015

무선통신을 기반으로 하는 WSN은 통신의 특성상 네트워크보안에 취약점을 가진다. 무선통신의 취약점은 누구나 네트워크에 접근이 가능하다는 것이다. 이에 따라 침입에 강인한 무선 센서 네트워크인 INtrusion-tolerant routing protocol for wireless SEnsor NetworkS(INSENS)가 제안됨으로써 WSN의 초기 라우팅 설정 시 침입하는 공격자를 사전에 차단할 수 있게 되었다. 그러나 라우팅 설정 후에 노드가 공격자에 의해 훼손당하게 된다면, 노드의 주요정보를 이용해 공격자는 또다시 라우팅 공격이 가능해진다. 본 논문에서는 공격자에 의해 훼손된 노드가 라우팅 공격 중 대표적인 공격인 싱크홀 공격 메시지를 방송하였을 때, 페어와이즈 키를 통해 효과적으로 공격메시지를 차단하는 양방향인증기법을 제안한다. 이로써 INSENS에서 발생하는 싱크홀 공격을 차단함으로써 WSN의 보안 강화에 기여한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.267-272
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• 2016

Wireless sensor networks (WSNs) are vulnerable to external intrusions due to the wireless communication characteristics and limited hardware resources. Thus, the attacker can cause sinkhole attack while intruding the network. INSENS is proposed for preventing the sinkhole attack. INSENS uses the three symmetric keys in order to prevent such sinkhole attacks. However, the sinkhole attack occurs again, even in the presence of INSENS, through the compromised node because INSENS does not consider the node being compromised. In this paper, we propose a method to counter the sinkhole attack by considering the compromised node, based on the neighboring nodes' information. The goals of the proposed method are i) network reliability improvement and ii) energy conservation through effective prevention of the sinkhole attack by detecting compromised nodes. The experimental results demonstrate that the proposed method can save up to, on average, 19.90% of energy while increasing up to, on average, 71.50%, the report reliability against internal sinkhole attacks in comparison to INSENS.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.839-850
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• 2014

As the social and financial damages caused by APT attack such as 3.20 cyber terror are increased, the technical solution against APT attack is required. It is, however, difficult to protect APT attack with existing security equipments because the attack use a zero-day malware persistingly. In this paper, we propose a host based anomaly detection method to overcome the limitation of the conventional signature-based intrusion detection system. First, we defined 39 features to identify between normal and abnormal behavior, and then collected 8.7 million feature data set that are occurred during running both malware and normal executable file. Further, each process is represented as 83-dimensional vector that profiles the frequency of appearance of features. the vector also includes the frequency of features generated in the child processes of each process. Therefore, it is possible to represent the whole behavior information of the process while the process is running. In the experimental results which is applying C4.5 decision tree algorithm, we have confirmed 2.0% and 5.8% for the false positive and the false negative, respectively.

• Convergence Security Journal
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• v.7 no.1
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• pp.63-75
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• 2007

Most of computer systems to be connected to network have been exposed to some network attacks and became to targets of system attack. System managers have established the IDS to prevent the system attacks over network. The previous IDS have decided intrusions detecting the requested connection packets more than critical values in order to detect attacks. This techniques have False Positive possibilities and have difficulties to detect the slow scan increasing the time between sending scan probes and the coordinated scan originating from multiple hosts. We propose the port scan detection rules detecting the RST/ACK flag packets to request some abnormal connections and design the data structures capturing some of packets. This proposed system is decreased a False Positive possibility and can detect the slow scan, because a few data can be maintained for long times. This system can also detect the coordinated scan effectively detecting the RST/ACK flag packets to be occurred the target system.

• Convergence Security Journal
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• v.14 no.7
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• pp.17-22
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• 2014

Wireless Ad hoc Network is threatened from many types of attacks because of its open structure, dynamic topology and the absence of infrastructure. Attacks by malicious nodes inside the network destroy communication path and discard packet. The damage is quite large and detecting attacks are difficult. In this paper, we proposed attack detection technique using secure authentication infrastructure for efficient detection and prevention of internal attack nodes. Cluster structure is used in the proposed method so that each nodes act as a certificate authority and the public key is issued in cluster head through trust evaluation of nodes. Symmetric Key is shared for integrity of data between the nodes and the structure which adds authentication message to the RREQ packet is used. ns-2 simulator is used to evaluate performance of proposed method and excellent performance can be performed through the experiment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.316-321
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• 2008

Advanced computer network technology enables computers to be connected in an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, which makes it vulnerable to previously unidentified attack patterns and variations in attack and increases false negatives. Intrusion detection and analysis technologies are thus required. This paper investigates the asymmetric costs of false errors to enhance the performances the detection systems. The proposed method utilizes the network model to consider the cost ratio of false errors. By comparing false positive errors with false negative errors, this scheme achieved better performance on the view point of both security and system performance objectives. The results of our empirical experiment show that the network model provides high accuracy in detection. In addition, the simulation results show that effectiveness of anomaly traffic detection is enhanced by considering the costs of false errors.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.549-559
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• 2023

As mobile edge computing (MEC) is gaining attention as a core technology of 5G networks, edge AI technology of 5G network environment based on mobile user data is recently being used in various fields. However, as in traditional AI security, there is a possibility of adversarial interference of standard 5G network functions within the core network responsible for edge AI core functions. In addition, research on data poisoning attacks that can occur in the MEC environment of standalone mode defined in 5G standards by 3GPP is currently insufficient compared to existing LTE networks. In this study, we explore the threat model for the MEC environment using NWDAF, a network function that is responsible for the core function of edge AI in 5G, and propose a feature selection method to improve the performance of detecting data poisoning attacks for Leaf NWDAF as some proof of concept. Through the proposed methodology, we achieved a maximum detection rate of 94.9% for Slowloris attack-based data poisoning attacks in NWDAF.