• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1396-1405
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• 2020

Cyber threats such as forced personal information collection and distribution of malicious codes using malicious URLs continue to occur. In order to cope with such cyber threats, a security technologies that quickly detects malicious URLs and prevents damage are required. In a web environment, malicious URLs have various forms and are created and deleted from time to time, so there is a limit to the response as a method of detecting or filtering by signature matching. Recently, researches on detecting and predicting malicious URLs using machine learning techniques have been actively conducted. Existing studies have proposed various features and machine learning algorithms for predicting malicious URLs, but most of them are only suggesting specialized algorithms by supplementing features and preprocessing, so it is difficult to sufficiently reflect the strengths of various machine learning algorithms. In this paper, a system for predicting malicious URLs using multiple machine learning algorithms was proposed, and an experiment was performed to combine the prediction results of multiple machine learning models to increase the accuracy of predicting malicious URLs. Through experiments, it was proved that the combination of multiple models is useful in improving the prediction performance compared to a single model.

• Convergence Security Journal
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• v.11 no.3
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• pp.3-10
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• 2011

Recently, due to a series of DDoS attacks, government agencies have enhanced security measures and business-related legislation. However, service attack and large network violations or accidents are most likely to occur repeatedly in the near future. In order to prevent this problem, researches must be conducted to predict the vulnerability in advance. The existing research methods do not state the specific data used for the base of the prediction, making the method more complex and imprecise. Therefore this study was conducted using the vulnerability data used for the basis of machine learning technology prediction, which were retrieved from a reputable organization. Also, the study suggested ways to predict the future vulnerabilities based on the weaknesses found in prior methods, and certified the efficiency using experiments.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.216-222
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• 2021

COVID-19 pandemic brings attentions to the nonmilitary and transnational non-traditonal security threats, as the scales of such damage by these threats are beyond expectation. The Republic of Korea Army tries to forecast non-traditional security threat which may be occurred in Korean peninsula by using collective intelligence platform. In coming years, climate change, social changes and technology development caused by the 4th industrial revolution will diversify non-traditional security threat. Considering urbanization, internet distribution rate, and geopolitical location where atmosphere from continent and ocean meet, Korea would may face the most lethal ones compared to those of other countries may face. Therefore, to predict such threats in pangovernment scale using collective intelligence platforms which embrace civil, public, military, industry, academy and research center is the most important than anything.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.4
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• pp.3-14
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• 2002

In this paper we classify the possible threat and introduce the method that measures the loss resulted from the threat in the university. This is the method that the amount of the loss minimized in the case of the same quality in damage as establish a economical prediction model. The method of measuring the loss is as follows. First, asset should be clearly identified and valued. Second, threats which may result in harm to asset should be classified. Third, vulnerabilities which is weaknesses associated with asset should be analyzed. Fourth, measure the value of the loss. we explain the valued method by the example.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.407-414
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• 2014

The Internet is a highly distributed and complex system consisting of billion devices and has become the field of various kinds of conflicts during the last two decades. As a matter of fact, various actors utilise the Internet for illicit purposes, such as for performing distributed denial of service attacks (DDoS) and for spreading various types of aggressive malware. Despite the fact that numerous services provide information regarding the threat level of the Internet, they are mostly based on information acquired by their sensors or on offline statistical sampling of various security applications (antivirus software, intrusion detection systems, etc.). This paper introduces proactive threat observatory system (PROTOS), an open-source early warning system that does not require a commercial license and is capable of estimating the threat level across the Internet. The proposed system utilises both a global and a local approach, and is thus able to determine whether a specific host is under an imminent threat, as well as to provide an estimation of the malicious activity across the Internet. Apart from these obvious advantages, PROTOS supports a large-scale installation and can be extended even further to improve the effectiveness by incorporating prediction and forecasting techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.800-803
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• 2003

Recently the number of internet users has very fast increased, and the number of intrusions has also increased very much. Hence, Security production developed to prevent systems and network from being hacked and intruded. However, hacking and virus getting on automation, intelligence, decentralization, large-scale and concealment. Therefore, Network security service necessary to deal with such threat. In this paper, we investigate about new network security services of trace-back, recovery, network vulnerability analysis, attack prediction and consideration for these services.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.369-375
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• 2016

Most companies are willing to spend money on security systems such as DRM, Mail filtering, DLP, USB blocking, etc., for data leakage prevention. However, in many cases, it is difficult that legal team take action for data case because usually the company recognized that after the employee had left. Therefore perceiving one's resignation before the action and building up adequate response process are very important. Throughout analyzing DRM log which records every single file's changes related with user's behavior, the company can predict one's resignation and prevent data leakage before those happen. This study suggests how to prevent for the damage from leaked confidential information throughout building the DRM monitoring process which can predict employee's resignation.

• The Journal of Bigdata
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• v.6 no.2
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• pp.99-108
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• 2021

Recently, unexpected and more advanced cyber medical treat attacks are on the rise. However, in responding to various patterns of cyber medical threat attack, rule-based security methodologies such as physical blocking and replacement of medical devices have the limitations such as lack of the man-power and high cost. As a way to solve the problems, the medical community is also paying attention to artificial intelligence technology that enables security threat detection and prediction by self-learning the past abnormal behaviors. In this study, there has collecting and learning the medical information data from integrated Medical-Information-Systems of the medical center and introduce the research methodology which is to develop the AI-based Net-Working Behavior Adaptive Information data. By doing this study, we will introduce all technological matters of rule-based security programs and discuss strategies to activate artificial intelligence technology in the medical information business with the various restrictions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.347-363
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• 2019

Malware has its own unique behavior characteristics, like DNA for living things. To respond APT (Advanced Persistent Threat) attacks in advance, it needs to extract behavioral characteristics from malware. To this end, it needs to do classification for each malware based on its behavioral similarity. In this paper, various similarity of Windows malware is estimated; and based on these similarity values, malware's family is predicted. The similarity measures used in this paper are as follows: 'TF-IDF cosine similarity', 'Nilsimsa similarity', 'malware function cosine similarity' and 'Jaccard similarity'. As a result, we find the prediction rate for each similarity measure is widely different. Although, there is no similarity measure which can be applied to malware classification with high accuracy, this result can be helpful to select a similarity measure to classify specific malware family.