• Journal of Internet Computing and Services
• /
• v.23 no.6
• /
• pp.27-37
• /
• 2022

Virtual private network (VPN) services are used in various environments related to national security, such as defense companies and defense-related institutions where digital communication environment technologies are diversified and access to network use is increasing. However, the number of cyber attacks that target vulnerable points of the VPN has annually increased through technological advancement. Thus, this study identified security requirements by performing STRIDE threat modeling to prevent potential and new vulnerable points that can occur in the VPN. STRIDE threat modeling classifies threats into six categories to systematically identify threats. To apply the proposed security requirements, this study analyzed functions of the VPN and formed a data flow diagram in the VPN service process. Then, it collected threats that can take place in the VPN and analyzed the STRIDE threat model based on data of the collected threats. The data flow diagram in the VPN service process, which was established by this study, included 96 STRIDE threats. This study formed a threat scenario to analyze attack routes of the classified threats and derived 30 security requirements for each element of the VPN based on the formed scenario. This study has significance in that it presented a security guideline for enhancing security stability of the VPN used in facilities that require high-level security, such as the Ministry of National Defense (MND).

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.25 no.5
• /
• pp.1043-1057
• /
• 2015

In order to recognize intelligent threats quickly and detect and respond to them actively, major public bodies and private institutions operate and administer an Intrusion Detection Systems (IDS), which plays a very important role in finding and detecting attacks. However, most IDS alerts have a problem that they generate false positives. In addition, in order to detect unknown malicious codes and recognize and respond to their threats in advance, APT response solutions or actions based systems are introduced and operated. These execute malicious codes directly using virtual technology and detect abnormal activities in virtual environments or unknown attacks with other methods. However, these, too, have weaknesses such as the avoidance of the virtual environments, the problem of performance about total inspection of traffic and errors in policy. Accordingly, for the effective detection of intrusion, it is very important to enhance security monitoring, consequentially. This study discusses a plan for the reduction of false positives as a plan for the enhancement of security monitoring. As a result of an experiment based on the empirical data of G, rules were drawn in three types and 11 kinds. As a result of a test following these rules, it was verified that the overall detection rate decreased by 30% to 50%, and the performance was improved by over 30%.

• Journal of Intelligence and Information Systems
• /
• v.13 no.3
• /
• pp.119-140
• /
• 2007

Recommender systems enable a user to decide which information is interesting and valuable in our world of information overload. As the recent studies of distributed computing environment have been progressing actively, recommender systems, most of which were centralized, have changed toward a peer-to-peer approach. Collaborative Filtering (CF), one of the most successful technologies in recommender systems, presents several limitations, namely sparsity, scalability, cold start, and the shilling problem, in spite of its popularity. The move from centralized systems to distributed approaches can partially improve the issues; distrust of recommendation and abuses of personal information. However, distributed systems can be vulnerable to attackers, who may inject biased profiles to force systems to adapt their objectives. In this paper, we consider both effective CF in P2P environment in order to improve overall performance of system and efficient solution of the problems related to abuses of personal data and attacks of malicious users. To deal with these issues, we propose a multi-agent framework for a distributed CF focusing on the trust relationships between individuals, i.e. web of trust. We employ an agent-based approach to improve the efficiency of distributed computing and propagate trust information among users with effect. The experimental evaluation shows that the proposed method brings significant improvement in terms of the distributed computing of similarity model building and the robustness of system against malicious attacks. Finally, we are planning to study trust propagation mechanisms by taking trust decay problem into consideration.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.11
• /
• pp.746-754
• /
• 2016

Smart healthcare systems, a convergent industry based on information and communications technologies (ICT), has emerged from personal health management to remote medical treatment as a distinguished industry. The smart healthcare environment provides technology to deliver vital information, such as pulse rate, body temperature, health status, and so on, from wearable devices to the hospital network where the physician is located. However, since it deals with the patient's personal medical information, there is a security issue for personal information management, and the system may be vulnerable to cyber-attacks in wireless networks. Therefore, this study focuses on a key-development and device-management system to generate keys in the smart environment to safely manage devices. The protocol is designed to provide safe communications with the generated key and to manage the devices, as well as the generated key. The security level is analyzed against attack methods that may occur in a healthcare environment, and it was compared with existing key methods and coding capabilities. In the performance evaluation, we analyze the security against attacks occurring in a smart healthcare environment, and the security and efficiency of the existing key encryption method, and we confirmed an improvement of about 15%, compared to the existing cipher systems.

• Journal of Internet Computing and Services
• /
• v.19 no.5
• /
• pp.21-31
• /
• 2018

Cyber penetration attacks can not only damage cyber space but can attack entire infrastructure such as electricity, gas, water, and nuclear power, which can cause enormous damage to the lives of the people. Also, cyber space has already been defined as the fifth battlefield, and strategic responses are very important. Most of recent cyber attacks are caused by malicious code, and since the number is more than 1.6 million per day, automated analysis technology to cope with a large amount of malicious code is very important. However, it is difficult to deal with malicious code encryption, obfuscation and packing, and the dynamic analysis technique is not limited to the performance requirements of dynamic analysis but also to the virtual There is a limit in coping with environment avoiding technology. In this paper, we propose a machine learning based malicious code analysis technique which improve the weakness of the detection performance of existing analysis technology while maintaining the light and high-speed analysis performance applicable to commercial endpoints. The results of this study show that 99.13% accuracy, 99.26% precision and 99.09% recall analysis performance of 71,000 normal file and malicious code in commercial environment and analysis time in PC environment can be analyzed more than 5 per second, and it can be operated independently in the endpoint environment and it is considered that it works in complementary form in operation in conjunction with existing antivirus technology and static and dynamic analysis technology. It is also expected to be used as a core element of EDR technology and malware variant analysis.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.558-564
• /
• 2013

Wireless sensor networks (WSNs) contain limited energy resources and are left in open environments. Since these sensor nodes are self-operated, attacks such as sinkhole attacks are possible as they can be compromised by an adversary. The sinkhole attack may cause to change initially constructed routing paths, and capture of significant information at the compromised node. A localized encryption and authentication protocol (LEAP) has been proposed to authenticate packets and node states by using four types of keys against the sinkhole attack. Even though this novel approach can securely transmits the packets to a base station, the packets are forwarded along the constructed paths without checking the next hop node states. In this paper, we propose the next hop node selection method to cater this problem. Our proposed method evaluates the next hop node considering three factors (i.e., remaining energy level, number of shared keys, and number of filtered false packets). When the suitability criterion for next hop node selection is satisfied against a fix threshold value, the packet is forwarded to the next hop node. We aim to enhance energy efficiency and a detour of attacked areas to be effectively selected Experimental results demonstrate validity of the proposed method with up to 6% energy saving against the sinkhole attack as compared to the LEAP.

• Korean Security Journal
• /
• no.18
• /
• pp.1-20
• /
• 2009

Nowadays most nations around the world including Korea have experienced absolute shortages of available urban space. To solve various problems of the city, each nation constantly tends to extend the underground space. However there is a serious problem in making use of the underground space. Especially new terrorism coming into existence after 9.11 terror turns into the so-called ‘soft target’ which has something to do with public transportation facilities available to most people. Good examples are like these: poisonous gas attacks in Tokyo subway in 1995, Daegu subway station fire in 2003, serial bomb blast of London subway in 2005. In spite of being a concern on incidents related to the underground space it is inevitable to utilize the underground space and the tendency is growing. But Korea lags badly behind in foreign countries in this field and so seeking measures is urgently needed. Therefore the aim of this study is to note visible damages stemmed from the domestic and foreign underground space and propose more effective and adequate measures. Safety measures of terrorism are associated to minimize damage out of terrorism and they are as follows. In the first place, preparing protective equipment for saving a life from fire attacks and poisonous gas is needed urgently. In the second place, counterpart management on the spot and systematic security training should be established in order to minimize injury. In the third place, fire escapes must be provided for a rapid evacuation of potential unspecified individuals. In the fourth place, building up a network of related institutions is required for a systematic omnidirectional counterpart. Finally the Korean government ought to take fast and appropriate actions for the injured and bereaved family of the terror incident.

• Journal of Internet Computing and Services
• /
• v.20 no.6
• /
• pp.11-20
• /
• 2019

Numerous enterprises, organizations and individual users are exposed to large DDoS (Distributed Denial of Service) attacks. DDoS attacks are performed through a BotNet, which is composed of a number of computers infected with a malware, e.g., zombie PCs and a special computer that controls the zombie PCs within a hierarchical chain of a command system. In order to detect a malware, a malware detection software or a vaccine program must identify the malware signature through an in-depth analysis, and these signatures need to be updated in priori. This is time consuming and costly. In this paper, we propose a botnet detection scheme that does not require a periodic signature update using an artificial neural network model. The proposed scheme exploits Word2Vec and accelerated hierarchical density-based clustering. Botnet detection performance of the proposed method was evaluated using the CTU-13 dataset. The experimental result shows that the detection rate is 99.9%, which outperforms the conventional method.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.10
• /
• pp.732-742
• /
• 2016

This study suggests methods of defense against low-level high-bandwidth DDoS attacks by adding a solution with a time limit factor (TLF) to an existing high-bandwidth DDoS defense system. Low-level DDoS attacks cause faults to the service requests of normal users by acting as a normal service connection and continuously positioning the connected session. Considering this, the proposed method makes it possible for users to show a down-related session by considering it as a low-level DDoS attack if the abnormal flow is detected after checking the amount of traffic. However, the service might be blocked when misjudging a low-level DDoS attack in the case of a communication fault resulting from a network fault, even with a normal connection status. Thus, we made it possible to reaccess the related information through a certain period of blocking instead of a drop through blacklist. In a test of the system, it was unable to block the session because it recognized sessions that are simply connected with a low-level DDoS attack as a normal communication.

• Journal of Internet Computing and Services
• /
• v.22 no.4
• /
• pp.1-11
• /
• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.