• Smart Media Journal
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• v.8 no.1
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• pp.27-34
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• 2019

Ransomware is a malicious program code evolved into various forms of attack. Unlike traditional Ransomware that is being spread out using email attachments or infected websites, a new type of Ransomware, such as WannaCryptor, may corrupt files just for being connected to the Internet. Due to global Ransomware damage, there are many studies conducted to detect and defense Ransomware. However, existing research on Ransomware detection only uses Ransomware signature database or monitors specific behavior of process. Additionally, existing Ransomware detection methods hardly detect and defense a new Ransomware that behaves differently from the traditional ones. In this paper, we propose a method to detect Ransomware by arranging decoy files and analyzing the method how Ransomware accesses and operates files in the file system. Also, we conduct experiments using proposed method and provide the results of detection and defense of Ransomware in this paper.

• Convergence Security Journal
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• v.23 no.1
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• pp.69-77
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• 2023

Recently, ransomware damage has been increasing rapidly around the world, including Irish health authorities and U.S. oil pipelines, and is causing damage to all sectors of society. In particular, research using machine learning as well as existing detection methods is increasing for ransomware detection and response. However, traditional machine learning has a problem in that it is difficult to extract accurate predictions because the model tends to predict in the direction where there is a lot of data. Accordingly, in an imbalance class consisting of a large number of non-Ransomware (normal code or malware) and a small number of Ransomware, a technique for resolving the imbalance and improving ransomware detection performance is proposed. In this experiment, we use two scenarios (Binary, Multi Classification) to confirm that the sampling technique improves the detection performance of a small number of classes while maintaining the detection performance of a large number of classes. In particular, the proposed mixed sampling technique (SMOTE+ENN) resulted in a performance(G-mean, F1-score) improvement of more than 10%.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.55-62
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• 2023

Recent ransomware attacks employ various techniques and pathways, posing significant challenges in early detection and defense. Consequently, the scale of damage is continually growing. This paper introduces a machine learning-based approach for effective ransomware detection by focusing on file encryption and encryption patterns, which are pivotal functionalities utilized by ransomware. Ransomware is identified by analyzing password behavior and encryption patterns, making it possible to detect specific ransomware variants and new types of ransomware, thereby mitigating ransomware attacks effectively. The proposed machine learning-based encryption behavior detection technique extracts encryption and encryption pattern characteristics and trains them using a machine learning classifier. The final outcome is an ensemble of results from two classifiers. The classifier plays a key role in determining the presence or absence of ransomware, leading to enhanced accuracy. The proposed technique is implemented using the numpy, pandas, and Python's Scikit-Learn library. Evaluation indicators reveal an average accuracy of 94%, precision of 95%, recall rate of 93%, and an F1 score of 95%. These performance results validate the feasibility of ransomware detection through encryption behavior analysis, and further research is encouraged to enhance the technique for proactive ransomware detection.

• 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 Digital Contents Society
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• v.19 no.2
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• pp.363-370
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• 2018

Ransomware detection has become a hot topic in computer security for protecting digital contents. Unfortunately, current signature-based and static detection models are often easily evadable by compress, and encryption. For overcoming the lack of these detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as RF, SVM, SL and NB algorithms. We monitor the actual behaviors of software to generate API calls flow graphs. Thereafter, data normalization and feature selection were applied to select informative features. We improved this analysis process. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. We conduct our experiment using more suitable real ransomware samples. and it's results show that our proposed system can be more effective to improve the performance for ransomware detection.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.41-48
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• 2023

Recently, there has been a sharp increase in the damages caused by ransomware across various sectors of society, including individuals, businesses, and nations. Ransomware is a malicious software that infiltrates user computer systems, encrypts important files, and demands a ransom in exchange for restoring access to the files. Due to its diverse and sophisticated attack techniques, ransomware is more challenging to detect than other types of malware, and its impact is significant. Therefore, there is a critical need for accurate detection and mitigation methods. To achieve precise ransomware detection, an inference engine of a detection system must possess knowledge of ransomware features. In this paper, we propose a model to extract and classify the characteristics of ransomware for accurate detection of ransomware, calculate the similarity of the extracted characteristics, reduce the dimension of the characteristics, group the reduced characteristics, and classify the characteristics of ransomware into attack tools, inflow paths, installation files, command and control, executable files, acquisition rights, circumvention techniques, collected information, leakage techniques, and state changes of the target system. The classified characteristics were applied to the existing ransomware to prove the validity of the classification, and later, if the inference engine learned using this classification technique is installed in the detection system, most of the newly emerging and variant ransomware can be detected.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.10
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• pp.251-258
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• 2018

Ransomware, a malicious software that requires a ransom by encrypting a file, is becoming more threatening with its rapid propagation and intelligence. Rapid detection and risk analysis are required, but real-time analysis and reporting are lacking. In this paper, we propose a ransomware infection detection system using social big data mining technology to enable real-time analysis. The system analyzes the twitter stream in real time and crawls tweets with keywords related to ransomware. It also extracts keywords related to ransomware by crawling the news server through the news feed parser and extracts news or statistical data on the servers of the security company or search engine. The collected data is analyzed by data mining algorithms. By comparing the number of related tweets, google trends (statistical information), and articles related wannacry and locky ransomware infection spreading in 2017, we show that our system has the possibility of ransomware infection detection using tweets. Moreover, the performance of proposed system is shown through entropy and chi-square analysis.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.497-505
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• 2018

Attacking computer systems using ransomware is very common all over the world. Since antivirus and detection methods are constantly improved in order to detect and mitigate ransomware, the ransomware itself becomes equally better to avoid detection. Several new methods are implemented and tested in order to optimize the protection against ransomware. In our work, 582 of ransomware and 942 of normalware sample data along with 30,967 dynamic action sequence variables are used to detect ransomware efficiently. Several variable selection techniques combined with various machine learning based classification techniques are tried to protect systems from ransomwares. Among various combinations, chi-square variable selection and random forest gives the best detection rates and accuracy.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.24-32
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• 2021

Recently, ransomware attacks have been infected through various channels such as e-mail, phishing, and device hacking, and the extent of the damage is increasing rapidly. However, existing known malware (static/dynamic) analysis engines are very difficult to detect/block against novel ransomware that has evolved like Advanced Persistent Threat (APT) attacks. This work proposes a method for modeling ransomware malicious behavior based on graph databases and detecting novel multi-complex malicious behavior for ransomware. Studies confirm that pattern detection of ransomware is possible in novel graph database environments that differ from existing relational databases. Furthermore, we prove that the associative analysis technique of graph theory is significantly efficient for ransomware analysis performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1581-1588
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• 2017

Recently, circulating ransomware is becoming intelligent and sophisticated through a spreading new viruses and variants, targeted spreading using social engineering attack, malvertising that circulate a large quantity of ransomware by hacking advertising server, or RaaS(Ransomware-as-a- Service), from the existing attack way that encrypt the files and demand money. In particular, it makes it difficult to track down attackers by bypassing security solutions, disabling parameter checking via file encryption, and attacking target-based ransomware with APT(Advanced Persistent Threat) attacks. For remove the threat of ransomware, various detection techniques are developed, but, it is very hard to respond to new and varietal ransomware. Accordingly, in this paper, find out a making Signature-based Detection Patterns and problems, and present a pattern automation model of ransomware detecting for responding to ransomware more actively. This study is expected to be applicable to various forms in enterprise or public security control center.