• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.69-78
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• 2023

With the global rise of digital data, the uncontrolled quantity of data is susceptible to cyber warfare or cyber attacks. Therefore, it is necessary to improve cyber security systems. This research studies the behavior of malicious acts and uses Higuchi Fractal Dimension (HFD), which is a non-linear mathematical method to examine the intricacy of the behavior of these malicious acts and anomalies within the cyber physical system. The HFD algorithm was tested successfully using synthetic time series network data and validated on real-time network data, producing accurate results. It was found that the highest fractal dimension value was computed from the DoS attack time series data. Furthermore, the difference in the HFD values between the DoS attack data and the normal traffic data was the highest. The malicious network data and the non-malicious network data were successfully classified using the Receiver Operating Characteristics (ROC) method in conjunction with a scaling stationary index that helps to boost the ROC technique in classifying normal and malicious traffic. Hence, the suggested methodology may be utilized to rapidly detect the existence of abnormalities in traffic with the aim of further using other methods of cyber-attack detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.3
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• pp.479-490
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• 2013

Recently, the number of attacks by malicious application has significantly increased, targeting Android-platform mobile terminal such as Samsung Galaxy Note and Galaxy Tab 10.1. The malicious application can be distributed to currently used mobile devices through open market masquerading as an normal application. An attacker inserts malicious code into an application, which might threaten privacy by rooting attack. Once the rooting attack is successful, malicious code can collect and steal private data stored in mobile terminal, for example, SMS messages, contacts list, and public key certificate for banking. To protect the private information from the malicious attack, malicious code detection, rooting attack detection and countermeasure method are required. To meet this end, this paper investigates rooting attack mechanism for Android-platform mobile terminal. Based on that, this paper proposes countermeasure system that enables to extract and collect events related to attacks occurring from mobile terminal, which contributes to active protection from malicious attacks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3B
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• pp.431-439
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• 2010

The security systems using signatures cannot protect servers from new types of Internet worms. To protect servers from Internet worms, this paper proposes a system removing malicious processes and executable files without using signatures. The proposed system consists of control servers which offer the same services as those on protected servers, and agents which are installed on the protected servers. When a control server detects multicasting attacks of Internet worm, it sends information about the attacks to an agent. The agent kills malicious processes and removes executable files with this information. Because the proposed system do not use signatures, it can respond to new types of Internet worms effectively. When the proposed system is integrated with legacy security systems, the security of the protected server will be further enhanced.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.2
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• pp.113-122
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• 2005

In this paper, we propose a multi-watermarking algorithm to satisfy two purposes: fragility against malicious attacks and robustness against non-malicious attacks. The algorithm can be used for image authentication using coefficients of Discrete Wavelet Transform(DWT). In the proposed method, watermarks are generated by combining binary image with some features extracted from the subband LL3, and then they are embedded into both the spatial and frequency domain. That is, on the spatial domain they are embedded into the Least Significant Bit(LSB) of all pixels of image blocks, and on the frequency domain the coefficients of the subband LH2 and HL2 are adjusted according to the watermarks. Thus the algorithm not only resists malicious attack but also permits non-malicious attacks such as blurring, sharpening, and JPEG compression.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4909-4926
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• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.24-28
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• 2017

In the Internet of Things (IoT), resource-limited smart devices communicate with each other while performing sensing and computation tasks. Thus, these devices can be exposed to various attacks being launched and spread through network. For instance, attacker can reuse the codes of IoT devices for malicious activity executions. In the sense that attacker can craft malicious codes by skillfully reusing codes stored in IoT devices, code-reuse attacks are generally considered to be dangerous. Although a variety of schemes have been proposed to defend against code-reuse attacks, code randomization is regarded as a representative defense technique against code-reuse attacks. Indeed, many research have been done on code randomization technique, however, there are little work on analysis of the interactions between code randomization defenses and code-reuse attacks although it is imperative problem to be explored. To provide the better understanding of these interactions in IoT, we analyze how code randomization defends against code-reuse attacks in IoT and perform simulation on it. Both analysis and simulation results show that the more frequently code randomizations occur, the less frequently code-reuse attacks succeed.

• Electronics and Telecommunications Trends
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• v.36 no.6
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• pp.78-87
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• 2021

Information technology (IT) has been applied to various fields, and currently, IT devices and systems are used in very important areas, such as aviation, industry, and national defense. Such devices and systems are subject to various types of malicious attacks, which can be software or hardware based. Compared to software-based attacks, hardware-based attacks are known to be much more difficult to detect. A hardware Trojan horse is a representative example of hardware-based attacks. A hardware Trojan horse attack inserts a circuit into an IC chip. The inserted circuit performs malicious actions, such as causing a system malfunction or leaking important information. This has increased the potential for attack in the current supply chain environment, which is jointly developed by various companies. In this paper, we discuss the future direction of research by introducing attack cases, the characteristics of hardware Trojan horses, and countermeasure trends.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.30-37
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• 2021

Various devices are connected to the Internet, and attacks using the Internet are occurring. Among such attacks, there are attacks that use malicious URLs to make users access to wrong phishing sites or distribute malicious viruses. Therefore, how to detect such malicious URL attacks is one of the important security issues. Among recent deep learning technologies, neural networks are showing good performance in image recognition, speech recognition, and pattern recognition. This neural network can be applied to research that analyzes and detects patterns of malicious URL characteristics. In this paper, performance analysis according to various parameters was performed on a method of detecting malicious URLs using neural networks. In this paper, malicious URL detection performance was analyzed while changing the activation function, learning rate, and neural network structure. The experimental data was crawled by Alexa top 1 million and Whois to build the data, and the machine learning library used TensorFlow. As a result of the experiment, when the number of layers is 4, the learning rate is 0.005, and the number of nodes in each layer is 100, the accuracy of 97.8% and the f1 score of 92.94% are obtained.

• Journal of the Korea Society of Computer and Information
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• v.18 no.3
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• pp.35-45
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• 2013

MANET has a weak point because it allows access from not only legal nodes but also illegal nodes. Most of the MANET researches had been focused on attack on routing path or packet forwarding. Nevertheless, there are insuffcient studies on a comprehensive approach to detect various attacks on malicious nodes at packet forwarding processes. In this paper, we propose a technique, named DTecBC (detection technique of malicious node behaviors based on collaboration), which can handle more effciently various types of malicious node attacks on MANET environment. The DTecBC is designed to detect malicious nodes by communication between neighboring nodes, and manage malicious nodes using a maintain table. OPNET tool was used to compare with Watchdog, CONFIDANT, SRRPPnT for verifying effectiveness of our approach. As a result, DTecBC detects various behaviors of malicious nodes more effectively than other techniques.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.61-75
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• 2011

Recently, there is significant increasing of massive attacks, which try to infect PCs that visit websites containing pre-implanted malicious code. When visiting the websites, these hidden malicious codes can gain monetary profit or can send various cyber attacks such as BOTNET for DDoS attacks, personal information theft and, etc. Also, this kind of malicious activities is continuously increasing, and their evasion techniques become professional and intellectual. So far, the current signature-based detection to detect websites, which contain malicious codes has a limitation to prevent internet users from being exposed to malicious codes. Since, it is impossible to detect with only blacklist when an attacker changes the string in the malicious codes proactively. In this paper, we propose a novel approach that can detect unknown malicious code, which is not well detected by a signature-based detection. Our method can detect new malicious codes even though the codes' signatures are not in the pattern database of Anti-Virus program. Moreover, our method can overcome various obfuscation techniques such as the frequent change of the included redirection URL in the malicious codes. Finally, we confirm that our proposed system shows better detection performance rather than MC-Finder, which adopts pattern matching, Google's crawling based malware site detection, and McAfee.