• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.285-288
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• 2010

Chinese hackers hack the e-commerce site by bypass South Korea IP to connect to the third country, finance damaging a violation incident that fake account. 7.7.DDoS attack was the case of a hacker attack that paralyzed the country's main site. In this paper, the analysis is about vulnerabilities that breaches by hackers and DDoS attacks. Hacker's attacks and attacks on the sign of correlation analysis is share the risk rating for in real time, Red, Orange, Yellow, Green. Create a blacklist of hackers and real-time attack will be studied security and air conditioning systems that attacks and defend. By studying generate forensic data and confirmed in court as evidence of accountability through IP traceback and detection about packet after Incident, contribute to the national incident response and development of forensic techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.703-711
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• 2016

IDS (Intrusion Detection System) is used to detect network attacks through network data analysis. The system requires a high accuracy and detection rate, and low false alarm rate. In addition, the system uses a range of techniques, such as expert system, data mining, and state transition analysis to analyze the network data. The purpose of this study was to compare the performance of two data mining methods for detecting network attacks. They are Support Vector Machine (SVM) and a neural network called Forward Additive Neural Network (FANN). The well-known KDD Cup 99 training and test data set were used to compare the performance of the two algorithms. The accuracy, detection rate, and false alarm rate were calculated. The FANN showed a slightly higher false alarm rate than the SVM, but showed a much higher accuracy and detection rate than the SVM. Considering that treating a real attack as a normal message is much riskier than treating a normal message as an attack, it is concluded that the FANN is more effective in intrusion detection than the SVM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2236-2257
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• 2020

Ransomware is malicious software that encrypts the user-related files and data and holds them to ransom. Such attacks have become one of the serious threats to cyberspace. The avoidance techniques that ransomware employs such as obfuscation and/or packing makes it difficult to analyze such programs statically. Although many ransomware detection studies have been conducted, they are limited to a small portion of the attack's characteristics. To this end, this paper proposed a framework for the behavioral-based dynamic analysis of high survivable ransomware (HSR) with integrated valuable feature sets. Term Frequency-Inverse document frequency (TF-IDF) was employed to select the most useful features from the analyzed samples. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to develop and implement a machine learning-based detection model able to recognize certain behavioral traits of high survivable ransomware attacks. Experimental evaluation indicates that the proposed framework achieved an area under the ROC curve of 0.987 and a few false positive rates 0.007. The experimental results indicate that the proposed framework can detect high survivable ransomware in the early stage accurately.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.1
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• pp.51-58
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• 2020

The use of malware in cyber threats continues to be used in all ages, and will continue to be a major attack method even if IT technology advances. Therefore, researches for detecting such malicious codes are constantly tried in various ways. Recently, with the development of AI-related technology, many researches related to machine learning have been conducted to detect malware. In this paper, we propose a method to detect malware using machine learning. For machine learning detection, we create a feature around each call interval, ie Time Interval, in which API calls occur among dynamic analysis data, and then apply the result to machine learning techniques.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.39-48
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• 2022

In this paper, we propose an anomaly detection system (ADS) to detect anomalies of the in-vehicle network for unmanned aerial vehicle (UAV). The proposed ADS detects the anomalies by measuring the similarity of status messages sequences periodically sent by the UAV to the ground control system. We defined three types of malicious message injection attacks that can be performed on the in-vehicle network of UAV and simulated those attack techniques in the Pixhawk4 quadcopter. The proposed ADS can detect abnormal sequences with accuracy of higher than 96%.

• Convergence Security Journal
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• v.11 no.6
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• pp.3-8
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• 2011

Advanced Persistent Threat (APT), aims a specific business or political targets, is rapidly growing due to fast technological advancement in hacking, malicious code, and social engineering techniques. One of the most important characteristics of APT is persistence. Attackers constantly collect information by remaining inside of the targets. Enterprise Security Management (EMS) system can misidentify APT as normal pattern of an access or an entry of a normal user as an attack. In order to analyze this misidentification, a new system development and a research are required. This study suggests the way of forecasting APT and the effective countermeasures against APT attacks by categorizing misidentified data in data-mining through threshold ratings. This proposed technique can improve the detection of future APT attacks by categorizing the data of long-term attack attempts.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.27-33
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• 2019

One of serious security threats is a botnet-based attack. A botnet in general consists of numerous bots, which are computing devices with networking function, such as personal computers, smartphones, or tiny IoT sensor devices compromised by malicious codes or attackers. Such botnets can launch various serious cyber-attacks like DDoS attacks, propagating mal-wares, and spreading spam e-mails over the network. To establish a botnet, attackers usually inject malicious URLs into web source codes stealthily by using data hiding methods like Javascript obfuscation techniques to avoid being discovered by traditional security systems such as Firewall, IPS(Intrusion Prevention System) or IDS(Intrusion Detection System). Meanwhile, it is non-trivial work in practice for software developers to manually find such malicious URLs which are hidden in numerous web source codes stored in web servers. In this paper, we propose a security defense system to discover such suspicious, malicious URLs hidden in web source codes, and present experiment results that show its discovery performance. In particular, based on our experiment results, our proposed system discovered 100% of URLs hidden by Javascript encoding obfuscation within sample web source files.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.17-22
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• 2021

The attack trend on end-users via mobile devices is increasing in both the danger level and the number of attacks. Especially, mobile devices using the Android operating system are being recognized as increasingly being exploited and attacked strongly. In addition, one of the recent attack methods on the Android operating system is to take advantage of Android Package Kit (APK) files. Therefore, the problem of early detecting and warning attacks on mobile devices using the Android operating system through the APK file is very necessary today. This paper proposes to use the method of analyzing abnormal behavior of APK files and use it as a basis to conclude about signs of malware attacking the Android operating system. In order to achieve this purpose, we propose 2 main tasks: i) analyzing and extracting abnormal behavior of APK files; ii) detecting malware in APK files based on behavior analysis techniques using machine learning or deep learning algorithms. The difference between our research and other related studies is that instead of focusing on analyzing and extracting typical features of APK files, we will try to analyze and enumerate all the features of the APK file as the basis for classifying malicious APK files and clean APK files.

• 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.