• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.1
• /
• pp.177-186
• /
• 2011

Mobile services which are applied PC performance and mobile characteristics are increased with spread of the smartphone. Recently, mobile cloud service is getting the spotlight as a solution of mobile service problems that mobile device is lack of memory, computing power and storage and mobile services are subordinate to a particular mobile device platform. However, mobile cloud service has more potential security threats by the threat inheritance of mobile service, wireless network and cloud computing service. Therefore, security threats of mobile cloud service has to be removed in order to deploy secure mobile cloud services and user and manager should be able to respond appropriately in the event of threat. In this paper, We define mobile cloud service threats by threat analysis of mobile device, wireless network and cloud computing and we propose mobile cloud service countermeasures in order to respond mobile cloud service threats and threat scenarios in order to respond and predict to potential mobile cloud service threats.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.2
• /
• pp.393-399
• /
• 2019

In a situation where an unauthorized SW brought into the organization without being authorized is emerging as a threat to the network security, the security of the network based on the SDN(Software-Defined Network) can be strengthened through the development of the security application considering the organization's characteristics. Security technology of existing SDN environment has been studied to protect internal network from external networks such as firewalls and Intrusion Detection Systems, but the research for resolving insider threat was insufficient. Therefore, We propose a system that protects the internal network from unauthorized SW, which is one of the insider threats in the SDN environment.

• Journal of Convergence for Information Technology
• /
• v.8 no.6
• /
• pp.149-157
• /
• 2018

In this paper, we propose an attack detection technology using SDN Controller to study security threats in IoT environment. The research methodology has been developed by applying IoT security threat management technology to the IoT layer and analyzing the research trend of applied security technology. The study results show that the effectiveness of the detection method using the sampling method is studied by adding OpenFlow based SDN Controller to the network switch equipment of the existing IoT network. This method can detect the monitoring and attack of the whole network by interworking with IDS and IPS without affecting the performance of existing IoT devices. By applying such improved security threat countermeasure technology, we expect to be able to relieve anxiety of IoT security threat and increase service reliability.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.20 no.6
• /
• pp.261-270
• /
• 2010

As the usage of social network service(SNS) increases recently, great attention has been shown to the information security in SNS. However, there has been little investment in SNS environment for security while preferential investment to attract subscribers has been made so far. Moreover, there is still a lack of confidence for investment effect and an absence of framework to analyze the threat factors of information security in SNS. In this paper, we propose to model for decision-making standard of SNS information security investment by the AHP. The result shows that 'service image' is the most important criterion for the decision of SNS information security. It also shows that 'Profile-squatting and reputation slander through ID thefts' and 'Corporate espionage' are important threat factors in SNS information security.

• Convergence Security Journal
• /
• v.6 no.1
• /
• pp.1-11
• /
• 2006

The exponential increase of malicious and criminal activities in cyber space is posing serious threat which could destabilize the foundation of modem information society. In particular, unexpected network paralysis or break-down created by the spread of malicious traffic could cause confusion and disorder in a nationwide scale, and unless effective countermeasures against such unexpected attacks are formulated in time, this could develop into a catastrophic condition. As a result, there has been vigorous effort and search to develop a functional state-level cyber-threat early-warning system however, the efforts have not yielded satisfying results or created plausible alternatives to date, due to the insufficiency of the existing system and technical difficulties. The existing cyber-threat forecasting and early-warning depend on the individual experience and ability of security manager whose decision is based on the limited security data collected from ESM (Enterprise Security Management) and TMS (Threat Management System). Consequently, this could result in a disastrous warning failure against a variety of unknown and unpredictable attacks. It is, therefore, the aim of this research to offer a conceptual design for "Knowledge-based Real-Time Cyber-Threat Early-Warning System" in order to counter increasinf threat of malicious and criminal activities in cyber suace, and promote further academic researches into developing a comprehensive real-time cyber-threat early-warning system to counter a variety of potential present and future cyber-attacks.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.9
• /
• pp.2342-2353
• /
• 1997

According to the outstanding development of information industry, a study of firewall is progressing as one of methods to cope with threat and loss of the data through computer network. For the secure network, this paper proposes the method diminishing threat and loss of the network using the correlation firewall with network audit trail system. Also, this paper suggests not only the audit analyzer execution model but also the type of databases used in audit analyzer to analyze the audit data. Network audit trail system has the function of identifing and analyzing of all intruder actions using audit records created by users.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.5
• /
• pp.1-8
• /
• 2018

The latest cyber attack utilizing advanced technologies is more rapidly advancing than developing speed of defense technology, thereby escalates the security risk. In responding to this recent threat, academia and industries are developing some sophisticated security technologies applying various methods. Based on these technologies, security systems are used in many fields. This article aims to select noticeable network security related technologies for the security systems. In particular, we compared and analyzed the trend, performance, and functions of both foreign and domestic technologies in regard to UTM having the largest portions among network security systems so far. We will also discuss the prospect for the change in network infrastructure due to the emergence of the next-generation network technology.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.5062-5079
• /
• 2017

Malicious insider threats have increased recently, and methods of the threats are diversifying every day. These insider threats are becoming a significant problem in corporations and governments today. From a technology standpoint, detecting potential insider threats is difficult in early stage because it is unpredictable. In order to prevent insider threats in early stage, it is necessary to collect all of insiders' data which flow in network systems, and then analyze whether the data are potential threat or not. However, analyzing all of data makes us spend too much time and cost. In addition, we need a large repository in order to collect and manage these data. To resolve this problem, we develop an indicator-based behavior ontology (IB2O) that allows us to understand and interpret insiders' data packets, and then to detect potential threats in early stage in network systems including social networks and company networks. To show feasibility of the behavior ontology, we developed a prototype platform called Insider Threat Detecting Extractor (ITDE) for detecting potential insider threats in early stage based on the behavior ontology. Finally, we showed how the behavior ontology would help detect potential inside threats in network system. We expect that the behavior ontology will be able to contribute to detecting malicious insider threats in early stage.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.249-256
• /
• 2021

An issue of security and threat is urgent as well as it concerns everyone: a person, community, state, etc. Today, the question of cybersecurity has become especially relevant due to general digitalization and the spread of the cyberculture. In terms of it, the growing popularity of videogames can be observed. Their impact on society differs significantly, therefore, it needs thorough consideration. The purpose of the article is to disclose the role of videogames in cybersecurity. To achieve the stated purpose, such methods as analysis, synthesis, systematization and practical involvement of videogames have been used. As a result, three levels of possible threat of videogames has been distinguished: videogames as a possibly dangerous software, as a tool of propaganda and spread of stereotypes, as a space for the creation of virtual communities. In conclusion, it is stated that videogames can be not only a threat, but also a tool for strengthening the cybersecurity.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.177-189
• /
• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.