• Journal of Information Processing Systems
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• v.18 no.5
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• pp.650-664
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• 2022

Mobile applications can be easily downloaded and installed via markets. However, malware and malicious applications containing unwanted advertisements exist in these application markets. Therefore, smartphone users install applications with reference to the application review to avoid such malicious applications. An application review typically comprises contents for evaluation; however, a false review with a specific purpose can be included. Such false reviews are known as fake reviews, and they can be generated using artificial intelligence (AI)-based text-generating models. Recently, AI-based text-generating models have been developed rapidly and demonstrate high-quality generated texts. Herein, we analyze the features of fake reviews generated from Generative Pre-Training-2 (GPT-2), an AI-based text-generating model and create a model to detect those fake reviews. First, we collect a real human-written application review from Kaggle. Subsequently, we identify features of the fake review using natural language processing and statistical analysis. Next, we generate fake review detection models using five types of machine-learning models trained using identified features. In terms of the performances of the fake review detection models, we achieved average F1-scores of 0.738, 0.723, and 0.730 for the fake review, real review, and overall classifications, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3870-3884
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• 2020

The proposed framework of Four Layer Robust Storage in Cloud (FLRSC) architecture involves host server, local host and edge devices in addition to Virtual Machine Monitoring (VMM). The goal is to protect the privacy of stored data at edge devices. The computational intelligence (CI) part of our algorithm distributes blocks of data to three different layers by partially encoded and forwarded for decoding to the next layer using hash and greed Solomon algorithms. VMM monitoring uses snapshot algorithm to detect intrusion. The proposed system is compared with Tiang Wang method to validate efficiency of data transfer with security. Hence, security is proven against the indexed efficiency. It is an important study to integrate communication between local host software and nearer edge devices through different channels by verifying snapshot using lamport mechanism to ensure integrity and security at software level thereby reducing the latency. It also provides thorough knowledge and understanding about data communication at software level with VMM. The performance evaluation and feasibility study of security in FLRSC against three-layered approach is proven over 2³² blocks of data with 98% accuracy. Practical implications and contributions to the growing knowledge base are highlighted along with directions for further research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.523-549
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• 2018

The increasing number of subscribers and demand of multiplicity of services has turned Multi-Server Authentication (MSA) into an integral part of remote authentication paradigm. MSA not only offers an efficient mode to register the users by engaging a trusted third party (Registration Centre), but also a cost-effective architecture for service procurement, onwards. Recently, Lu et al.'s scheme demonstrated that Mishra et al.'s scheme is unguarded to perfect forward secrecy compromise, server masquerading, and forgery attacks, and presented a better scheme. However, we discovered that Lu et al.'s scheme is still susceptible to malicious insider attack and non-compliant to perfect forward secrecy. This study presents a critical review on Lu et al.'s scheme and then proposes a secure multi-server authentication scheme. The security properties of contributed work are validated with automated Proverif tool and proved under formal security analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1466-1488
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• 2022

Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.757-759
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• 2024

Software vulnerabilities represent security weaknesses in software systems that attackers exploit for malicious purposes, resulting in potential system compromise and data breaches. Despite the increasing prevalence of these vulnerabilities, manual repair efforts by security analysts remain time-consuming. The emergence of deep learning technologies has provided promising opportunities for automating software vulnerability repairs, but existing AIbased approaches still face challenges in effectively handling complex vulnerabilities. This paper explores the potential of large language models (LLMs) in addressing these limitations, examining their performance in code vulnerability repair tasks. It introduces the latest research on utilizing LLMs to enhance the efficiency and accuracy of fixing security bugs.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.257-262
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• 2015

As software grows more complex, it contains more bugs that are not recognized by developers. Attackers can then use exploitable bugs to penetrate systems or spread malicious code. As a representative method, attackers manipulated documents or multimedia files in order to make the software engage in unanticipated behavior. Recently, this method has gained frequent use in A.P.T. In this paper, an automatic analysis method to find software security bugs was proposed. This approach aimed at finding security bugs in the software which can arise from input data such as documents or multimedia. Through dynamic taint analysis, how input data propagation to vulnerable code occurred was tracked, and relevant instructions in relation to input data were found. Next, the relevant instructions were translated to a formula and vulnerable input data were found via the formula using an SMT solver. Using this approach, 6 vulnerable codes were found, and data were input to crash applications such as HWP and Gomplayer.

• The KIPS Transactions:PartC
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• v.15C no.1
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• pp.9-18
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• 2008

Prevention of attacks being made through program modification in sensor nodes is one of the important security issues. The software-based attestation technology that verifies the running code by checking whether it is modified or not in sensor nodes is being used to solve the attack problem. However, the current software-based attestation techniques are not appropriate in sensor networks because not only they are targeting static networks that member nodes does not move, but also they lacks consideration on the environment that the trusted verifier may not exist. This paper proposes a mutual attestation protocol that is suitable for sensor networks by using SWATT(Software-based ATTestation) technique. In the proposed protocol, sensor nodes periodically notify its membership to neighbor nodes and carry out mutual attestation procedure with neighbor nodes by using SWATT technique. With the proposed protocol, verification device detects the sensor nodes compromised by malicious attacks in the sensor network environments without trusted verifier and the sensor networks can be composed of only the verified nodes.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.706-715
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• 2014

It is used to lead to serious structural vulnerability of the system security of security-critical system when we have quickly developed software system according to urgent release schedule without appropriate security planning, management, and assurance processes. The Data Set and Provider of DataSnap, which is a middleware of Delphi XE2 of the Embarcadero Technologies Co., certainly help to develop an easy and fast-paced procedure, but it is difficult to apply security program and vulnerable to control software system security when the connection structure Database-DataSnap server-SQL Connection-SQL Data set-Provider is applied. This is due to that all kinds of information of Provider are exposed on the moment when DataSnap Server Port is sure to malicious attackers. This exposure becomes a window capable of running SQL Command. Thus, it should not be used Data Set and Provider in the DataSnap Server in consideration of all aspects of security management. In this paper, we study on the verification of the security vulnerabilities for Client and Server DataSnap in Dlephi XE2, and we propose a secure coding method to improve security vulnerability in the DataSnap server system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2591-2611
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• 2020

Botnets have become one of the most significant threats to Internet-connected smartphones. A botnet is a combination of infected devices communicating through a command server under the control of botmaster for malicious purposes. Nowadays, the number and variety of botnets attacks have increased drastically, especially on the Android platform. Severe network disruptions through massive coordinated attacks result in large financial and ethical losses. The increase in the number of botnet attacks brings the challenges for detection of harmful software. This study proposes a smart framework for mobile botnet detection using static analysis. This technique combines permissions, activities, broadcast receivers, background services, API and uses the machine-learning algorithm to detect mobile botnets applications. The prototype was implemented and used to validate the performance, accuracy, and scalability of the proposed framework by evaluating 3000 android applications. The obtained results show the proposed framework obtained 98.20% accuracy with a low 0.1140 false-positive rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1369-1374
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• 2014

In case of APT attacks, the update server is being used as a means of dissemination, the update program is running malicious code or data in applications such as anti-virus signature is vulnerable to manipulation, SW Update threat identification and prevention measures are urgently required. This paper presents a natiional and international SW update structure, update process exploits and response measures to examine, Through the extraction/analysis of a domestic famous SW update log, we are willing to select the necessary component of the normal program update to identify a white list.