• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• Journal of Information Technology Services
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• v.23 no.2
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• pp.13-29
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• 2024

Recently, with the rapid spread of mobile terminals such as smartphones and tablet PCs, social demand for mobile information security is increasing as new security issues that are difficult to predict as well as service evolution and lifestyle changes are raised. Smart terminals include smartphones, smart pads, chromebooks, laptops, etc. that provide various functions such as phone calls, text messages, Internet browsing, social media apps, games, and education. Along with the explosive spread of these smart terminals, they are naturally being used in our daily life and educational environment. In the mobile environment, behind the convenience of portability, there are more various security threats and vulnerabilities than in the general PC environment, and threats such as device loss, information leakage, and malicious codes exist, so it is necessary to take fundamental security measures at a higher level. In this study, we suggest ways to improve security by identifying trends in mobile smart information security and effectively responding to security threats to the mobile environment. In addition, it presents implications for various measures for effective class utilization along with correct security management methods and security measures related to the supply of smart devices that the Office of Education is promoting for schools at each level.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.13-21
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• 2020

The Android author identification study can be interpreted as a method for revealing the source in a narrow range, but if viewed in a wide range, it can be interpreted as a study to gain insight to identify similar works through known works. The problem found in the Android author identification study is that it is an important code on the Android system, but it is difficult to find the important feature of the author due to the meaningless codes. Due to this, legitimate codes or behaviors were also incorrectly defined as malicious codes. To solve this, we introduced the concept of survival network to solve the problem by removing the features found in various Android apps and surviving unique features defined by authors. We conducted an experiment comparing the proposed framework with a previous study. From the results of experiments on 440 authors' identified apps, we obtained a classification accuracy of up to 92.10%, and showed a difference of up to 3.47% from the previous study. It used a small amount of learning data, but because it used unique features without duplicate features for each author, it was considered that there was a difference from previous studies. In addition, even in comparative experiments with previous studies according to the feature definition method, the same accuracy can be shown with a small number of features, and this can be seen that continuously overlapping meaningless features can be managed through the concept of a survival network.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1027-1041
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• 2015

Recently, there are rapidly increasing cases of APT (Advanced Persistent Threat) attacks such as Verizon(2010), Nonghyup(2011), SK Communications(2011), and 3.20 Cyber Terror(2013), which cause leak of confidential information and tremendous damage to valuable assets without being noticed. Several anomaly detection technologies were studied to defend the APT attacks, mostly focusing on detection of obvious anomalies based on known malicious codes' signature. However, they are limited in detecting APT attacks and suffering from high false-negative detection accuracy because APT attacks consistently use zero-day vulnerabilities and have long latent period. Detecting APT attacks requires long-term analysis of data from a diverse set of sources collected over the long time, real-time analysis of the ingested data, and correlation analysis of individual attacks. However, traditional security systems lack sophisticated analytic capabilities, compute power, and agility. In this paper, we propose a Fast Data based real-time abnormal behavior detection system to overcome the traditional systems' real-time processing and analysis limitation.

• Journal of KIISE:Information Networking
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• v.37 no.4
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• pp.255-262
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• 2010

Recently, automated signature generation systems(ASGSs) have been developed in order to cope with zero-day attacks with malicious codes exploiting vulnerabilities which are not yet publically noticed. To enhance the usefulness of the signatures generated by (ASGSs) it is essential to identify signatures only with the high accuracy of intrusion detection among a number of generated signatures and to provide them to target security systems in a timely manner. This automated signature exchange, distribution, and update operations have to be performed in a secure and universal manner beyond the border of network administrations, and also should be able to eliminate the noise in a signature set which causes performance degradation of the security systems. In this paper, we present a system architecture to support the identification of high quality signatures and to share them among security systems through a scheme which can evaluate the detection accuracy of individual signatures, and also propose a set of algorithms dealing with exchanging, distributing and updating signatures. Though the experiment on a test-bed, we have confirmed that the high quality signatures are automatically saved at the level that the noise rate of a signature set is reduced. The system architecture and the algorithm proposed in the paper can be adopted to a automated signature sharing framework.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.339-348
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• 2006

Recently, many researches on detecting and responding worms due to the fatal infrastructural damages explosively damaged by automated attack tools, particularly worms. Network service vulnerability exploiting worms have high propagation velocity, exhaust network bandwidth and even disrupt the Internet. Previous worm researches focused on signature-based approaches however these days, approaches based on behavioral features of worms are more highlighted because of their low false positive rate and the attainability of early detection. In this paper, we propose a Distributed Worm Detection Model based on packet marking. The proposed model detects Worm Cycle and Infection Chain among which the behavior features of worms. Moreover, it supports high scalability and feasibility because of its distributed reacting mechanism and low processing overhead. We virtually implement worm propagation environment and evaluate the effectiveness of detecting and responding worm propagation.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.41-50
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• 2011

Code obfuscation has been proposed to protect codes in a program from malicious software reverse engineering. It converts a program into an equivalent one that is more difficult to understand the program. Code obfuscation has been classified into various obfuscation technique such as layout, data, control, by obfuscating goals. In those obfuscation techniques, control obfuscation is intended to complicate the control flow in a program to protect abstract information of control flow. For protecting control flow in a program, various control obfuscation transformation techniques have been proposed. However, strategies for effectively applying a control flow obfuscation to program have not been proposed yet. In this paper, we proposed a obfuscation strategy that effectively applies a control flow obfuscation transformation to a program. We conducted experiment to show that the proposed obfuscation strategy is useful for applying a control flow transformation to a program.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.257-262
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• 2015

As software grows more complex, it contains more bugs that are not recognized by developers. Attackers can then use exploitable bugs to penetrate systems or spread malicious code. As a representative method, attackers manipulated documents or multimedia files in order to make the software engage in unanticipated behavior. Recently, this method has gained frequent use in A.P.T. In this paper, an automatic analysis method to find software security bugs was proposed. This approach aimed at finding security bugs in the software which can arise from input data such as documents or multimedia. Through dynamic taint analysis, how input data propagation to vulnerable code occurred was tracked, and relevant instructions in relation to input data were found. Next, the relevant instructions were translated to a formula and vulnerable input data were found via the formula using an SMT solver. Using this approach, 6 vulnerable codes were found, and data were input to crash applications such as HWP and Gomplayer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.5
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• pp.377-384
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• 2013

In order to improve the security strength in the password based user authentication, in which the security vulnerability is increased while the same password is repeatedly used, the OTP(One-Time Password) system has been introduced. In the OTP systems, however, the user account information and OTP value may be hacked if the user PC is infected by the malicious codes, because the user types the OTP value, which is generated by the mobile device synchronized with the server, directly onto the user PC. In this paper, we propose a new method, called DTOTP(Dual Transmission OTP), to solve this security problem. The DTOTP system is an improved two-factor authentication method by using the dual transmission, in which the user performs the server authentication by typing the user account and password information onto the PC, and then for the OTP authentication the mobile device scans the QR code displayed on the PC and the OTP value is sent to the server directly. The proposed system provides more improved security strength than that of the existing OTP system, and also can adopt the existing OTP algorithm without any modification. As a result, the proposed system can be safely applied to various security services such like banking, portal, and game services.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1327-1337
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• 2019

With the growth of the IoT market, malware security threats are steadily increasing for devices that use the linux architecture. However, except for the major malware causing serious security damage such as Mirai, there is no related technology or research of security community about linux malware. In addition, the diversity of devices, vendors, and architectures in the IoT environment is further intensifying, and the difficulty in handling linux malware is also increasing. Therefore, in this paper, we propose an analysis system based on ELF which is the main format of linux architecture, and a binary based analysis system considering IoT environment. The ELF-based analysis system can be pre-classified for a large number of malicious codes at a relatively high speed and a relatively low-speed binary-based analysis system can classify all the data that are not preprocessed. These two processes are supposed to complement each other and effectively classify linux-based malware.