• Convergence Security Journal
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• v.10 no.4
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• pp.21-30
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• 2010

Wireless network is one of the 2010's most important security issues. As smartphone is popularize, the number of Wireless Internet users is really growing and wireless AP spring up everywhere. But most wireless AP haven't being managed properly in terms of security, Wireless Internet users also don't recognize important of security. This situation causes grave security threats. This paper design and analyze a new cyber attack whose it circulates malware via QR code and activates Mobile AP to induce service charge. The new vulnerability we suggest forces to activate Mobile AP of smartphone based on Android and responds to all Probe Request are generated around, and brings induction of service charge and communication problems in its train.

• Convergence Security Journal
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• v.13 no.1
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• pp.79-86
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• 2013

The objective of DDoS Attacks is to simply disturb the services. In recent years, the DDoS attacks have been evolved into Multi-Vector Attacks which use diversified and mixed attacking techniques. Multi-Vector Attacks start from DDoS Attack and Malware Infection, obtain inside information, and make zombie PC to reuse for the next DDoS attacks. These forms of Multi-Vector Attacks are unable to be prevented by the existing security strategies for DDoS Attacks and Malware Infection. This paper presents an approach to effectively defend against diversified Multi-Vector attacks by using Reverse Proxy Group and PMS(Patch Management Server).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.641-649
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• 2020

As the usage of mobile devices extremely increases, malicious mobile apps(applications) that target mobile users are also increasing. It is challenging to detect these malicious apps using traditional malware detection techniques due to intelligence of today's attack mechanisms. Deep learning (DL) is an alternative technique of traditional signature and rule-based anomaly detection techniques and thus have actively been used in numerous recent studies on malware detection. In order to develop DL-based defense mechanisms against intelligent malicious apps, feeding recent datasets into DL models is important. In this paper, we develop a DL-based model for detecting intelligent malicious apps using KU-CISC 2018-Android, the most up-to-date dataset consisting of benign and malicious Android apps. This dataset has hardly been addressed in other studies so far. We extract OPcode sequences from the Android apps and preprocess the OPcode sequences using an N-gram model. We then feed the preprocessed data into LSTM and apply the concept of Information Gain to improve performance of detecting malicious apps. Furthermore, we evaluate our model with numerous scenarios in order to verify the model's design and performance.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.2
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• pp.87-94
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• 2017

Microsoft Office is an office suite of applications developed by Microsoft. Recently users with malicious intent customize Office files as a container of the Malware because MS Office is most commonly used word processing program. To attack target system, many of malicious office files using a variety of skills and techniques like macro function, hiding shell code inside unused area, etc. And, people usually use two techniques to detect these kinds of malware. These are Signature-based detection and Sandbox. However, there is some limits to what it can afford because of the increasing complexity of malwares. Therefore, this paper propose methods to detect malicious MS office files in Computer forensics' way. We checked Macros and potential problem area with structural analysis of the MS Office file for this purpose.

• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.1-8
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• 2021

With the rapid increase in the use of IoT and mobile devices, cyber criminals targeting IoT devices are also on the rise. Among IoT devices, when using a wireless access point (AP), problems such as packets being exposed to the outside due to their own security vulnerabilities or easily infected with malicious codes such as bots, causing DDoS attack traffic, are being discovered. Therefore, in this study, in order to actively respond to cyber attacks targeting IoT devices that are rapidly increasing in recent years, we proposed a method to collect traces of intrusion incidents artifacts from IoT devices, and to improve the validity of intrusion analysis data. Specifically, we presented a method to acquire and analyze digital forensics artifacts in the compromised system after identifying the causes of vulnerabilities by reproducing the behavior of the sample IoT malware. Accordingly, it is expected that it will be possible to establish a system that can efficiently detect intrusion incidents on targeting large-scale IoT devices.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.55-63
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• 2021

Recently, the incidence rate of malicious codes is over tens of thousands of cases, and it is known that it is almost impossible to detect/respond all of them. This study proposes a method for detecting multiple behavior patterns based on a graph database as a new method for dealing with malicious codes. Traditional dynamic analysis techniques and has applied a method to design and analyze graphs of representative associations malware pattern(process, PE, registry, etc.), another new graph model. As a result of the pattern verification, it was confirmed that the behavior of the basic malicious pattern was detected and the variant attack behavior(at least 5 steps), which was difficult to analyze in the past. In addition, as a result of the performance analysis, it was confirmed that the performance was improved by about 9.84 times or more compared to the relational database for complex patterns of 5 or more steps.

• Journal of KIISE
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• v.45 no.2
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• pp.106-112
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• 2018

Recently, the number of cyber attacks using malicious code has increased. Various types of malicious code detection techniques have been researched for several years as the damage has increased. In recent years, profiling techniques have been used to identify attack groups. This paper focuses on the identification of attack groups using a detection technique that does not involve malicious code detection. The attacker is identified by using a string or a code signature of the malicious code. In addition, the detection rate is increased by adding a technique to confirm the packing file. We use Yara as a detection technique. We have research about RAT (remote access tool) that is mainly used in attack groups. Further, this paper develops a ruleset using malicious code and packer main feature signatures for RAT which is mainly used by the attack groups. It is possible to detect the attacker by detecting RAT based on the newly created ruleset.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.1
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• pp.177-185
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• 2016

As the fintech industry is growing at an incredible rate, mobile phones are positioned as the most important tool for financial transaction. However, with a rising number of malware applications, the types of attack and illegal access to mobile device are becoming more diverse and sophisticated. This paper studies the potential keylogger attack by exploiting the Accessibility Service in Android framework. This type of attack allows the malicious individual to use keylogger on the victim's Android mobile phone to steal passwords during mobile financial transaction regardless of security keypad setting. Lastly the paper proposes solutions to counter these types of attack by verifying the accessibility usage and amending the application guideline for accessibility.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1021-1026
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• 2015

Increasing of damage by phishing, government and organization response more rapidly. So phishing use malware and vulnerability for attack. Recently attack that use patch analysis is increased when Microsoft announce patches. Cause of that, researcher for security on defense need technology of patch analysis. But most patch analysis are develop for Microsoft's product. Increasing of mobile environment, necessary of patch analysis on mobile is increased. But ordinary patch analysis can not use mobile environment that there is many file and small size. So we suggest this research that use code filtering instead of Control Flow Graph and Abstract Syntax Tree.