• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.459-469
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• 2023

The number of IoT devices is explosively increasing due to the development of embedded equipment and computer networks. As a result, cyber threats to IoT are increasing, and currently, malicious codes are being distributed and infected to IoT devices and exploited for DDoS. Currently, IoT devices that are the target of such an attack have various installation environments and have limited resources. In addition, IoT devices have a characteristic that once set up, the owner does not care about management. Because of this, IoT devices are becoming a blind spot for management that is easily infected with malicious codes. Because of these difficulties, the threat of malicious codes always exists in IoT devices, and when they are infected, responses are not properly made. In this paper, we will design an malware detection system for IoT in consideration of the characteristics of the IoT environment and present detection rules suitable for use in the system. Using this system, it will be possible to construct an IoT malware detection system inexpensively and efficiently without changing the structure of IoT devices that are already installed and exposed to cyber threats.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.186-192
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• 2023

Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3756-3777
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• 2019

Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1119-1139
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• 2021

Among the scientific literature, it has not been possible to find a consensus on the definition of the limits or properties that allow differentiating or grouping the cyber-physical systems (CPS) and the Internet of Things (IoT). Despite this controversy the papers reviewed agree that both have become crucial elements not only for industry but also for society in general. The impact of a malware attack affecting one of these systems may suppose a risk for the industrial processes involved and perhaps also for society in general if the system affected is a critical infrastructure. This article reviews the state of the art of the application of machine learning in the automation of malware detection in cyberphysical systems, evaluating the most representative articles in this field and summarizing the results obtained, the most common malware attacks in this type of systems, the most promising algorithms for malware detection in cyberphysical systems and the future lines of research in this field with the greatest potential for the coming years.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.87-93
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• 2022

Since 99% of PCs operating in the defense domain use the Windows operating system, detection and response of Window-based malware is very important to keep the defense cyberspace safe. This paper proposes a model capable of detecting malware in a Windows PE (Portable Executable) format. The detection model was designed with an emphasis on rapid update of the training model to efficiently cope with rapidly increasing malware rather than the detection accuracy. Therefore, in order to improve the training speed, the detection model was designed based on a Bidirectional LSTM (Long Short Term Memory) network that can detect malware with minimal sequence data without complicated pre-processing. The experiment was conducted using the EMBER2018 dataset, As a result of training the model with feature sets consisting of three type of sequence data(Byte-Entropy Histogram, Byte Histogram, and String Distribution), accuracy of 90.79% was achieved. Meanwhile, it was confirmed that the training time was shortened to 1/4 compared to the existing detection model, enabling rapid update of the detection model to respond to new types of malware on the surge.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.159-168
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• 2017

Smartphone malware has increased because Smartphone users has increased and smartphones are widely used in everyday life. Since 2012, Android has been the most mobile operating system. Owing to the open nature of Android, countless malware are in Android markets that seriously threaten Android security. Most of Android malware detection program does not detect malware to which bypass techniques apply and also does not detect unknown malware. In this paper, we propose lightweight method for detection of Android malware using static analysis and deep learning techniques. For experiments we crawl 7,000 apps from the Google Play Store and collect 6,120 malwares. The result show that proposed method can achieve 98.05% detection accuracy. Also, proposed method can detect about unknown malware families with good performance. On smartphones, the method requires 10 seconds for an analysis on average.

• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.77-85
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• 2014

Recently, several environment damage caused by malicious or suspicious code is increasing. We study comprehensive response system actively for malware detection. Suspicious code is installed on your PC without your consent, users are unaware of the damage. Also, there are need to technology for realtime processing of Big Data. We must develope advanced technology for malware detection. We must analyze the static, dynamic of executable file for fundamentally malware detection in recently and verified by a reputation for verification. It is need to judgment of similarity for realtime response with big data. In this paper, we proposed realtime detection and verification technology using multiple filter. Our malware study suggests a new direction of realtime malware detection.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.69-76
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• 2013

In the recent years, cyber attacks by malicious codes called malware has become a social problem. With the explosive appearance and increase of new malware, innumerable disasters caused by metaphoric malware using the existing malicious codes have been reported. To secure more effective detection of malicious codes, in other words, to make a more accurate judgment as to whether suspicious files are malicious or not, this study introduces the malware analysis system, which is based on a profiling technique using the Finite Automata. This new analysis system enables realtime automatic detection of malware with its optimized partial execution method. In this paper, the functions used within a file are expressed by finite automata to find their correlation, and a realtime malware analysis system enabling us to give an immediate judgment as to whether a file is contaminated by malware is suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2610-2628
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• 2019

Advanced persistent threats (APTs) are constant attacks of specific targets by hackers using intelligent methods. All current internal infrastructures are constantly subject to APT attacks created by external and unknown malware. Therefore, information security officers require a framework that can assess whether information security systems are capable of detecting and blocking APT attacks. Furthermore, an on-line evaluation of information security systems is required to cope with various malicious code attacks. A regular evaluation of the information security system is thus essential. In this paper, we propose a dynamic updated evaluation framework to improve the detection rate of internal information systems for malware that is unknown to most (over 60 %) existing static information security system evaluation methodologies using non-updated unknown malware.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.365-367
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• 2014

최근 웹 서비스의 증가와 악성코드는 그 수를 판단 할 수 없을 정도로 빠르게 늘어나고 있다. 매년 늘어나는 악성코드는 금전적 이윤 추구가 악성코드의 주된 동기가 되고 있으며 이는 공공기관 및 보안 업체에서도 악성코드를 탐지하기 위한 연구가 활발히 진행되고 있다. 본 논문에서는 실시간으로 패킷을 분석할수 있는 필터링과 웹 크롤링을 통해 도메인 및 하위 URL까지 자동적으로 탐지할 수 있는 악성코드 탐지 시스템을 제안한다.