• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.37-43
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• 2017

A registry is a hierarchy database which is designed to store information necessary for operating system and application programs in Windows operating system, and it is involved in all activities such as booting, logging, service execution, application execution, and user behavior. Digital forensic is widely used. In recent years, malicious codes have penetrated into systems in a way that is not recognized by the user, and valuable information is leaked or stolen, causing financial damages. Therefore, this study proposes a method to detect malicious code by using a shareware application without using expensive digital forensic program, so as to analysis hacking methods and prevent hacking damage in advance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.91-101
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• 2017

API call sequence analysis is a kind of analysis using API call information extracted in target program. Compared to other techniques, this is advantageous as it can characterize the behavior of the target. However, existing API call sequence analysis has an issue of identifying same characteristics to different function during the analysis. To resolve the identification issue and improve performance of analysis, this study includes the method of API abstraction technique in addition to existing analysis. From there on, similarity between target programs is computed and clustered into similar types by applying LSH to abstracted API call sequence from analyzed target. Thus, this study can attribute in improving the accuracy of the malware analysis based on discovered information on the types of malware identified.

• Journal of IKEEE
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• v.26 no.3
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• pp.380-388
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• 2022

The traditional malware detection technologies collect known malicious programs and analyze their characteristics. Then such a detection technology makes a blacklist based on the analyzed malicious characteristics and checks programs in the user's system based on the blacklist to determine whether each program is malware. However, such an approach can detect known malicious programs, but responding to unknown or variant malware is challenging. In addition, since such detection technologies generally monitor all programs in the system in real-time, there is a disadvantage that they can degrade the system performance. In order to solve such problems, various methods have been proposed to analyze major behaviors of malicious programs and to respond to them. The main characteristic of ransomware is to access and encrypt the user's file. So, a new approach is to produce the whitelist of programs installed in the user's system and allow the only programs listed on the whitelist to access the user's files. However, although it applies such an approach, attackers can still perform malicious behavior by performing a DLL(Dynamic-Link Library) injection attack on a regular program registered on the whitelist. This paper proposes a method to respond effectively to attacks using DLL injection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.321-330
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• 2015

There have been found many modified malwares which could avoid detection simply by replacing a sequence of characters or a part of code. Since the existing anti-virus program performs signature-based analysis, it is difficult to detect a malware which is slightly different from the well-known malware. This paper suggests a method of detecting modified malwares by extending a hash-value based code comparison. We generated hash values for individual functions and individual code blocks as well as the whole code, and thus use those values to find whether a pair of codes are similar in a certain degree. We also eliminated some numeric data such as constant and address before generating hash values to avoid incorrectness incurred from them. We found that the suggested method could effectively find inherent similarity between original malware and its derived ones.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.99-104
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• 2018

Recently, there are increasing damages by ransomware in the world. Ransomware is a malicious software that infects computer systems and restricts user's access to them by locking the system or encrypting user's files saved in the hard drive. Victims are forced to pay the 'ransom' to recover from the damage and regain access to their personal files. Strong countermeasure is needed due to the extremely vicious way of attack with enormous damage. Malware analysis method can be divided into two approaches: static analysis and dynamic analysis. Recent malwares are usually equipped with elaborate packing techniques which are main obstacles for static analysis of malware. Therefore, this paper suggests a dynamic analysis method to monitor activities of ransomware. The proposed method can analyze ransomwares more accurately. The suggested method is comprised of extracting signatures of benign program, malware, and ransomware, and selecting the most appropriate signatures for ransomware detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.2
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• pp.169-177
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• 2020

Malware analysis is one of the important concerns of computer security, and advances in analysis techniques have become important for computer security. In the past, the signature-based method was used to detect malware. However, as the percentage of packed malware increased, it became more difficult to detect using the conventional method. In this paper, we propose a method for identifying packers of packed programs using machine learning. The proposed method parses the packed program to extract specific PE information that can identify the packer and identifies the packer using the Adaptive Boosting algorithm among the machine learning models. To verify the accuracy of the proposed method, we collected and tested 391 programs packed with 12 types of packers and found that the packers were identified with an accuracy of about 99.2%. In addition, we presented the results of identification using PEiD, a signature-based PE identification tool, and existing machine learning method. The proposed method shows better performance in terms of accuracy and speed in identifying packers than existing methods.

• Convergence Security Journal
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• v.10 no.1
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• pp.19-27
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• 2010

Nowadays, the network's speed and internet technology are progressing rapidly but mill wares are occurring frequently through the Window's weak point. Since the malwares have various infection types and characteristics, it is hard to detect them by the virus vaccine and to cure them. This paper analyzes the type and characteristics of the mal ware and proposes a script technology that can find the location of the mal ware by the program and respond rapidly to the new kind of malwares.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.53-59
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• 2020

Malicious file upload attacks mean that the attacker to upload or transfer files of dangerous types that can be automatically processed within the web server's environment. Uploaded file content can include exploits, malware and malicious scripts. An attacker can user malicious content to manipulate the application behavior. As a method of detecting a malicious file upload attack, it is generally used to find a file type by detecting a file extension or a signature of the file. However, this type of file type detection has the disadvantage that it can not detect files that are not encoded with a specific program, such as PHP files. Therefore, in this paper, research was conducted on how to detect and block any program by using essential commands or variable names used in the corresponding program when writing a specific program. The performance evaluation results show that it detected specific files effectively using the suggested method.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.47-54
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• 2014

Anti-debugging is one of the techniques implemented within the computer code to hinder attempts at reverse engineering so that attackers or analyzers will not be able to use debuggers to analyze the program. The technique has been applied to various programs and is still commonly used in order to prevent malware or malicious code attacks or to protect the programs from being analyzed. In this paper, we will suggest an automatic detection scheme for anti-debugging routines. With respect to the automatic detection, debuggers and a simulator were used by which trace information on the Application Program Interface(API) as well as executive instructions were extracted. Subsequently, the extracted instructions were examined and compared so as to detect points automatically where suspicious activity was captured as anti-debugging routines. Based on experiments to detect anti-debugging routines using such methods, 21 out of 25 anti-debugging techniques introduced in this paper appear to be able to detect anti-debugging routines properly. The technique in the paper is therefore not dependent upon a certain anti-debugging method. As such, the detection technique is expected to also be available for anti-debugging techniques that will be developed or discovered in the future.