• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3301-3318
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• 2016

Online social networks (OSNs) have changed the way people communicate with each other. An OSN usually encourages the participants to provide personal information such as real names, birthdays and educational background to look for and establish friendships among them. Some users are unwilling to reveal personal information on their personal pages due to potential privacy concerns, but their friends may inadvertently reveal that. In this work, we investigate the possibility of leaking personal information on Facebook in an unintentional and involuntary manner. The revealed information may be useful to malicious users for social engineering and spear phishing. We design the inference methods to find birthdays and educational background of Facebook users based on the interactions among friends on Facebook pages and groups, and also leverage J-measure to find the inference rules. The inference improves the finding rate of birthdays from 71.2% to 87.0% with the accuracy of 92.0%, and that of educational background from 75.2% to 91.7% with the accuracy of 86.3%. We also suggest the sanitization strategies to avoid the private information leakage.

• Convergence Security Journal
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• v.14 no.5
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• pp.31-36
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• 2014

As security management center of financial area, financial ISAC monitors financial corporations and provides security services. Financial ISAC minimizes damage by responding quickly to external attack such as hacking, virus but it is poor at handling internal attack. For the efficient management and stable operation of information source, also to respond jointly to online hacking, the necessity of information sharing system increases day by day in and outside country. This paper proposes financial ISAC that can prevent a secondary damage of leakage information as well as providing security services. The proposed financial ISAC provides new password to financial corporation in which the same ID and password as leakage information are used and in case of financial information leakage it warns customers against phishing etc.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.427-432
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• 2010

The fast-emerging of cloud computing technology today has sufficiently benefited its wide range of users from individuals to large organizations. It carries an attractive characteristic by renting myriad virtual storages, computing resources and platform for users to manipulate their data or utilize the processing resources conveniently over Internet without the need to know the exact underlying infrastructure which is resided remotely at cloud servers. However due to the loss of direct control over the systems/applications, users are concerned about the risks of cloud services if it is truly secured. In the literature, there are cases where attackers masquerade as cloud users, illegally access to their accounts, by stealing the static login password or breaking the poor authentication gate. In this paper, we propose a two-factor authentication framework to enforce cloud services' authentication process, which are Public Key Infrastructure (PKI) authentication and mobile out-of-band (OOB) authentication. We discuss the framework's security analysis in later session and conclude that it is robust to phishing and replay attacks, prohibiting fraud users from accessing to the cloud services.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.37-44
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• 2011

Recently malicious activities that is a DDoS, spam, propagation of malware, steeling person information, phishing on the Internet are related malicious botnet. To detect malicious botnet, Many researchers study a detection system for malicious botnet, but these applies specific protocol, action or attack based botnet. In this reason, we study a selection of measurement to detec malicious botnet in this paper. we collect a traffic of malicious botnet and analyze it for feature of network traffic. And we select a feature based measurement. we expect to help a detection of malicious botnet through this study.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.153-158
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• 2022

The Internet of Things (IoT) has gotten a lot of research attention in recent years. IoT is seen as the internet's future. IoT will play a critical role in the future, transforming our lifestyles, standards, and business methods. In the following years, the use of IoT in various applications is likely to rise. In the world of information technology, cyber security is critical. In today's world, protecting data has become one of the most difficult tasks. Different type of emerging cyber threats such as malicious, network based and abuse of network have been identified in the IoT. These can be done by virus, Phishing, Spam and insider abuse. This paper focuses on emerging threats, various challenges and vulnerabilities which are faced by the cyber security in the field of IoT and its applications. It focuses on the methods, ethics, and trends that are reshaping the cyber security landscape. This paper also focuses on an attempt to classify various types of threats, by analyzing and characterizing the intruders and attacks facing towards the IoT devices and its services.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.131-146
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• 2022

The Internet is becoming a basic need for many individuals globally in this digital age. The youths became more active online than before, with the majority relying on different platforms to communicate and interact with peers. Saudi Arabia is one of the nations where internet usage is high, with an increasing number of active internet users. The youth in Saudi Arabia are engaged in various online platforms. However, they lack adequate knowledge about cybersecurity and the dangers of internet usage, which exposes them to the risk of falling victims to cybercriminals. The most common dangers of internet usage include viruses, malware, phishing, and hacking, compromising users' sensitive information. Increased awareness of these potential threats helps protect Internet users and secure their data. The understanding of the dangers of Internet usage among youths varies across countries. In this regard, our study explores the risks of internet usage among youth in Saudi Arabia compared to the United States, South Africa, and New Zealand.

• Convergence Security Journal
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• v.23 no.1
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• pp.55-61
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• 2023

QR Code is a two-dimensional code in the form of a matrix that contains data in a square-shaped black-and-white grid pattern, and has recently been used in various fields. In particular, in order to prevent the spread of COVID-19, the usage increased rapidly by identifying the movement path in the form of a QR code that anyone can easily and conveniently use. As such, Qshing attacks and damages using QR codes are increasing in proportion to the usage of QR codes. Therefore, in this paper, a system was implemented to block movement to harmful sites and installation of malicious codes when scanning QR codes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.354-356
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• 2022

Profiles are the most basic means of representing who you are, and you represent yourself. In SNS and chatting apps that use profiles the most, profiles are used to express their experiences, moods, and thoughts. However, in voice phishing, impersonation crimes, etc., they steal and impersonate a photo such as a profile, and then deceive the victim into committing a crime. In this paper, to prevent the crime of impersonation, the authenticity of the original is determined using NFT that can distinguish the original. Minting the original profile picture with NFT, registering it on the blockchain network, and storing the original file in off-chain IPFS. This is expected to be effective in preventing impersonation crimes because it is possible to easily determine the authenticity of the profile picture.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.881-891
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• 2023

Recently, various studies on malicious URL detection using artificial intelligence have been conducted, and most of the research have shown great detection performance. However, not only does classical machine learning require a process of analyzing features, but the detection performance of a trained model also depends on the data analyst's ability. In this paper, we propose a DL-ML Fusion Hybrid Model for malicious web site URL detection based on URL lexical features. the propose model combines the automatic feature extraction layer of deep learning and classical machine learning to improve the feature engineering issue. 60,000 malicious and normal URLs were collected for the experiment and the results showed 23.98%p performance improvement in maximum. In addition, it was possible to train a model in an efficient way with the automation of feature engineering.