• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.691-705
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• 2012

Buffer overflow vulnerability is the most representative one that an attack method and its countermeasure is frequently developed and changed. This vulnerability is still one of the most critical threat since it was firstly introduced in middle of 1990s. Shellcode is a machine code which can be used in buffer overflow attack. Attackers make the shellcode for their own purposes and insert it into target host's memory space, then manipulate EIP(Extended Instruction Pointer) to intercept control flow of the target host system. Therefore, a lot of research to defend have been studied, and attackers also have done many research to bypass security measures designed for the shellcode defense. In this paper, we investigate shellcode defense and attack techniques briefly and we propose our new methodology which can hide shellcode in the 24bit BMP image. With this proposed technique, we can easily hide any shellcode executable and we can bypass the current detection and prevention techniques.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.75-82
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• 2006

The service use management for keeping up stable and effective environment is hard little by little by according to increase of internet user and being complicated network environment of the Internet little by little. being various of the requirements of the service which is provided and the user demand. And the beginning flag security was limited in IDS, But recently the integrated civil management is coming to be considered seriously according to adventting IDS. Firewall , Security or system. The development of integrated security civil management system to analyze widely through observation and detection at Network or host base, the judgment of attack, and integrated analysis of infiltration information is necessary because of detecting the various type attack.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.434-437
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• 2010

NIPS(Network Intrusion Prevention System) is in line at the end of the external and internal networks which performed two kinds of action: Signature-based filtering and anomaly detection and prevention-based on self-learning. Among them, a signature-based filtering is well known to defend against attacks. By using signature-based filtering, intrusion prevention system passing a payload of packets is compared with attack patterns which are signature. If match, the packet is discard. However, when there is packet delay, it will increase the required pattern matching time as the number of signature is increasing whenever there is delay occur. Therefore, to ensure the performance of IPS, we needed more efficient pattern matching algorithm for high-performance ISP. To improve the performance of pattern matching the most important part is to reduce the number of comparisons signature rules and the packet whenever the packets arrive. In this paper, we propose an improve signature hashing-based pattern matching method. We use tuple pruning algorithm with Bloom filters, which effectively remove unnecessary tuples. Unlike other existing signature hashing-based IPS, our proposed method to improve the performance of IPS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2214-2229
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• 2024

Wireless sensor network (WSN) consists of large number of sensor nodes that are deployed in geographical locations to collect sensed information, process data and communicate it to the control station for further processing. Due the unfriendly environment where the sensors are deployed, there exist many possibilities of malicious nodes which performs malicious activities in the network. Therefore, the security threats affect performance and life time of sensor networks, whereas various security aspects are there to address security issues in WSN namely Cryptography, Trust Management, Intrusion Detection System (IDS) and Intrusion Prevention Systems (IPS). However, IDS detect the malicious activities and produce an alarm. These malicious activities exploit vulnerabilities in the network layer and affect all layers in the network. Existing feature selection methods such as filter-based methods are not considering the redundancy of the selected features and wrapper method has high risk of overfitting the classification of intrusion. Due to overfitting, the classification algorithm fails to detect the intrusion in better manner. The main objective of this paper is to provide the efficient feature selection algorithm which was suitable for any type classification algorithm to detect the intrusion in an effective manner. This paper, the security of the network is addressed by proposing Feature Selection Algorithm using Chi Squared with Ensemble Method (FSChE). The proposed scheme employs the combination of decision tree along with the random forest classification algorithm to form ensemble classifier. The experimental results justify the feasibility of the proposed scheme in terms of attack detection, packet delivery ratio and time analysis by employing NSL KDD cup data Set. The obtained results shows that the proposed ensemble method increases the overall performance by 10% to 25% with respect to mentioned parameters.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.79-90
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• 2014

User authentication based on ID and PW has been widely used. As the Internet has become a growing part of people' lives, input times of ID/PW have been increased for a variety of services. People have already learned enough to perform the authentication procedure and have entered ID/PW while ones are unconscious. This is referred to as the adaptive unconscious, a set of mental processes incoming information and producing judgements and behaviors without our conscious awareness and within a second. Most people have joined up for various websites with a small number of IDs/PWs, because they relied on their memory for managing IDs/PWs. Human memory decays with the passing of time and knowledges in human memory tend to interfere with each other. For that reason, there is the potential for people to enter an invalid ID/PW. Therefore, these characteristics above mentioned regarding of user authentication with ID/PW can lead to human vulnerabilities: people use a few PWs for various websites, manage IDs/PWs depending on their memory, and enter ID/PW unconsciously. Based on the vulnerability of human factors, a variety of information leakage attacks such as phishing and pharming attacks have been increasing exponentially. In the past, information leakage attacks exploited vulnerabilities of hardware, operating system, software and so on. However, most of current attacks tend to exploit the vulnerabilities of the human factors. These attacks based on the vulnerability of the human factor are called social-engineering attacks. Recently, malicious social-engineering technique such as phishing and pharming attacks is one of the biggest security problems. Phishing is an attack of attempting to obtain valuable information such as ID/PW and pharming is an attack intended to steal personal data by redirecting a website's traffic to a fraudulent copy of a legitimate website. Screens of fraudulent copies used for both phishing and pharming attacks are almost identical to those of legitimate websites, and even the pharming can include the deceptive URL address. Therefore, without the supports of prevention and detection techniques such as vaccines and reputation system, it is difficult for users to determine intuitively whether the site is the phishing and pharming sites or legitimate site. The previous researches in terms of phishing and pharming attacks have mainly studied on technical solutions. In this paper, we focus on human behaviour when users are confronted by phishing and pharming attacks without knowing them. We conducted an attack experiment in order to find out how many IDs/PWs are leaked from pharming and phishing attack. We firstly configured the experimental settings in the same condition of phishing and pharming attacks and build a phishing site for the experiment. We then recruited 64 voluntary participants and asked them to log in our experimental site. For each participant, we conducted a questionnaire survey with regard to the experiment. Through the attack experiment and survey, we observed whether their password are leaked out when logging in the experimental phishing site, and how many different passwords are leaked among the total number of passwords of each participant. Consequently, we found out that most participants unconsciously logged in the site and the ID/PW management dependent on human memory caused the leakage of multiple passwords. The user should actively utilize repudiation systems and the service provider with online site should support prevention techniques that the user can intuitively determined whether the site is phishing.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Annual Conference of KIPS
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• 2017.04a
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• pp.315-318
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• 2017

Nowadays, Distributed Denial of Service (DDoS) attacks have gained increasing popularity and have been a major factor in a number of massive cyber-attacks. It could easily exhaust the computing and communicating resources of a victim within a short period of time. Therefore, we have to find the method to detect and prevent the DDoS attack. Recently, there have been some researches that provide the methods to resolve above problem, but it still gets some limitations such as low performance of detecting and preventing, scope of method, most of them just use on cloud server instead of network, and the reliability in the network. In this paper, we propose solutions for (1) handling multiple DDoS attacks from multiple IP address and (2) handling the suspicious attacks in the network. For the first solution, we assume that there are multiple attacks from many sources at a times, it should be handled to avoid the conflict when we setup the preventing rule to switches. In the other, there are many attacks traffic with the low volume and same destination address. Although the traffic at each node is not much, the traffic at the destination is much more. So it is hard to detect that suspicious traffic with the sampling based method at each node, our method reroute the traffic to another server and make the analysis to check it deeply.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.35-45
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• 2009

In an open environment such as Home Network, ZigBee Cluster comprising a plurality of Ato-cells is required to provide intense security over the movement of collected, measured data. Against this setting, various security issues are currently under discussion concerning master key control policies, Access Control List (ACL), and device sources, which all involve authentication between ZigBee devices. A variety of authentication methods including Hash Chain Method, token-key method, and public key infrastructure, have been previously studied, and some of them have been reflected in standard methods. In this context, this paper aims to explore whether a new method for searching for neighboring devices in order to detect device replications and Sybil attacks can be applied and extended to the field of security. The neighbor detection applied method is a method of authentication in which ACL information of new devices and that of neighbor devices are included and compared, using information on peripheral devices. Accordingly, this new method is designed to implement detection of malicious device attacks such as Sybil attacks and device replications as well as prevention of hacking. In addition, in reference to ITU-T SG17 and ZigBee Pro, the home network equipment, configured to classify the labels and rules into four categories including user's access rights, time, date, and day, is implemented. In closing, the results demonstrates that the proposed method performs significantly well compared to other existing methods in detecting malicious devices in terms of success rate and time taken.

• Convergence Security Journal
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• v.15 no.4
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• pp.35-41
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• 2015

Internet of Things has a wide range of vulnerabilities are exposed to information security threats. However, this does not deal with the basic solution, the vaccine does not secure encryption for the data transmission. The encryption and authentication message transmitted from one node to the construction of the secure wireless sensor networks is required. In order to satisfy the constraint, and security requirements of the sensor network, lightweight encryption and authentication technologies, the light key management technology for the sensor environment it is required. Mandatory sensor network security technology, privacy protection technology subchannel attack prevention, and technology. In order to establish a secure wireless sensor networks encrypt messages sent between the nodes and it is important to authenticate. Lightweight it shall apply the intrusion detection mechanism functions to securely detect the presence of the node on the network. From the sensor node is not involved will determine the authenticity of the terminal authentication technologies, there is a need for a system. Network security technology in an Internet environment objects is a technique for enhancing the security of communication channel between the devices and the sensor to be the center.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.618-628
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• 2020

The purpose of this paper is to propose a new security orchestration service model by combining various security solutions that have been introduced and operated individually as a basis for cyber security framework. At present, in order to respond to various and intelligent cyber attacks, various single security devices and SIEM and AI solutions that integrate and manage them have been built. In addition, a cyber security framework and a security control center were opened for systematic prevention and response. However, due to the document-oriented cybersecurity framework and limited security personnel, the reality is that it is difficult to escape from the control form of fragmentary infringement response of important detection events of TMS / IPS. To improve these problems, based on the model of this paper, select the targets to be protected through work characteristics and vulnerable asset identification, and then collect logs with SIEM. Based on asset information, we established proactive methods and three detection strategies through threat information. AI and SIEM are used to quickly determine whether an attack has occurred, and an automatic blocking function is linked to the firewall and IPS. In addition, through the automatic learning of TMS / IPS detection events through machine learning supervised learning, we improved the efficiency of control work and established a threat hunting work system centered on big data analysis through machine learning unsupervised learning results.