• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.37-44
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• 2011

Recently malicious activities that is a DDoS, spam, propagation of malware, steeling person information, phishing on the Internet are related malicious botnet. To detect malicious botnet, Many researchers study a detection system for malicious botnet, but these applies specific protocol, action or attack based botnet. In this reason, we study a selection of measurement to detec malicious botnet in this paper. we collect a traffic of malicious botnet and analyze it for feature of network traffic. And we select a feature based measurement. we expect to help a detection of malicious botnet through this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2881-2894
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• 2018

Cyber attacks are increasing continuously. On average about one million malicious codes appear every day, and attacks are expanding gradually to IT convergence services (e.g. vehicles and television) and social infrastructure (nuclear energy, power, water, etc.), as well as cyberspace. Analysis of large-scale cyber incidents has revealed that most attacks are started by PCs infected with malicious code. This paper proposes a method of detecting an attack IP automatically by analyzing the characteristics of the e-mail transfer path, which cannot be manipulated by the attacker. In particular, we developed a system based on the proposed model, and operated it for more than four months, and then detected 1,750,000 attack IPs by analyzing 22,570,000 spam e-mails in a commercial environment. A detected attack IP can be used to remove spam e-mails by linking it with the cyber removal system, or to block spam e-mails by linking it with the RBL(Real-time Blocking List) system. In addition, the developed system is expected to play a positive role in preventing cyber attacks, as it can detect a large number of attack IPs when linked with the portal site.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3244-3260
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• 2013

A three-party password-based authenticated key exchange (PAKE) protocol allows two clients registered with a trusted server to generate a common cryptographic key from their individual passwords shared only with the server. A key requirement for three-party PAKE protocols is to prevent an adversary from mounting a dictionary attack. This requirement must be met even when the adversary is a malicious (registered) client who can set up normal protocol sessions with other clients. This work revisits three existing three-party PAKE protocols, namely, Guo et al.'s (2008) protocol, Huang's (2009) protocol, and Lee and Hwang's (2010) protocol, and demonstrates that these protocols are not secure against offline and/or (undetectable) online dictionary attacks in the presence of a malicious client. The offline dictionary attack we present against Guo et al.'s protocol also applies to other similar protocols including Lee and Hwang's protocol. We conclude with some suggestions on how to design a three-party PAKE protocol that is resistant against dictionary attacks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.55-62
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• 2018

The MTD-based approach changes various operating parameters dynamically so that the vulnerability of the system can be protected from the malicious attack. In this paper, random/serial scanning/jamming attack success probabilities have been mathematically analyzed and verified through simulation to improve the security of the wireless communication systems in which the MTD-SDR technologies are applied. As a result, for random scanning attacks, attack success probability increases as the change period of transmission channel increases, while for random jamming attacks there is no change. The attack success probability patterns for serial attacks are similar to those of random attacks, but when the change period of transmission channel approaches to the total number of transmission channels, the success probability of serial attack is getting greater than that of random attack, up to twice in jamming attacks and up to 36% in scanning attacks.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.25-36
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• 2023

Today, as AI (Artificial Intelligence) technology is introduced in various fields, including security, the development of technology is accelerating. However, with the development of AI technology, attack techniques that cleverly bypass malicious behavior detection are also developing. In the classification process of AI models, an Adversarial attack has emerged that induces misclassification and a decrease in reliability through fine adjustment of input values. The attacks that will appear in the future are not new attacks created by an attacker but rather a method of avoiding the detection system by slightly modifying existing attacks, such as Adversarial attacks. Developing a robust model that can respond to these malware variants is necessary. In this paper, we propose two methods of generating Adversarial attacks as efficient Adversarial attack generation techniques for improving Robustness in AI models. The proposed technique is the XAI-based attack technique using the XAI technique and the Reference based attack through the model's decision boundary search. After that, a classification model was constructed through a malicious code dataset to compare performance with the PGD attack, one of the existing Adversarial attacks. In terms of generation speed, XAI-based attack, and reference-based attack take 0.35 seconds and 0.47 seconds, respectively, compared to the existing PGD attack, which takes 20 minutes, showing a very high speed, especially in the case of reference-based attack, 97.7%, which is higher than the existing PGD attack's generation rate of 75.5%. Therefore, the proposed technique enables more efficient Adversarial attacks and is expected to contribute to research to build a robust AI model in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4661-4680
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• 2016

Cognitive radio (CR) technology is an effective solution to the spectrum scarcity issue. Collaborative spectrum sensing is known as a promising technique to improve the performance of spectrum sensing in cognitive radio networks (CRNs). However, collaborative spectrum sensing is vulnerable to spectrum data falsification (SSDF) attack, where malicious users (MUs) may send false sensing data to mislead other secondary users (SUs) to make an incorrect decision about primary user (PUs) activity, which is one of the key adversaries to the performance of CRNs. In this paper, we propose a coalition based malicious users detection (CMD) algorithm to detect the malicious user in CRNs. The proposed CMD algorithm can efficiently detect MUs base on the Geary'C theory and be modeled as a coalition formation game. Specifically, SSDF attack is one of the key issues to affect the resource allocation process. Focusing on the security issues, in this paper, we analyze the power allocation problem with MUs, and propose MUs detection based power allocation (MPA) algorithm. The MPA algorithm is divided into two steps: the MUs detection step and the optimal power allocation step. Firstly, in the MUs detection step, by the CMD algorithm we can obtain the MUs detection probability and the energy consumption of MUs detection. Secondly, in the optimal power allocation step, we use the Lagrange dual decomposition method to obtain the optimal transmission power of each SU and achieve the maximum utility of the whole CRN. Numerical simulation results show that the proposed CMD and MPA scheme can achieve a considerable performance improvement in MUs detection and power allocation.

• Smart Media Journal
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• v.8 no.1
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• pp.19-26
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• 2019

At the opening ceremony of 2018 Winter Olympics in PyeongChang, an unknown cyber-attack occurred. The malicious code used in the attack is based on in-memory malware, which differs from other malicious code in its concealed location and is spreading rapidly to be found in more than 140 banks, telecommunications and government agencies. In-memory malware accounts for more than 15% of all malicious codes, and it does not store its own information in a non-volatile storage device such as a disk but resides in a RAM, a volatile storage device and penetrates into well-known processes (explorer.exe, iexplore.exe, javaw.exe). Such characteristics make it difficult to analyze it. The most recently released in-memory malicious code bypasses the endpoint protection and detection tools and hides from the user recognition. In this paper, we propose a method to efficiently extract the payload by unpacking injection through IDA Pro debugger for Dorkbot and Erger, which are in-memory malicious codes.

• 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.34 no.3
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• pp.451-461
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• 2024

Recently, various AI chatbots based on large language models have been released. Chatbots have the advantage of providing users with quick and easy information through interactive prompts, making them useful in various fields such as question answering, writing, and programming. However, a vulnerability in chatbots called "prompt injection attacks" has been proposed. This attack involves injecting instructions into the chatbot to violate predefined guidelines. Such attacks can be critical as they may lead to the leakage of confidential information within large language models or trigger other malicious activities. However, the vulnerability of Korean prompts has not been adequately validated. Therefore, in this paper, we aim to generate malicious Korean prompts and perform attacks on the popular chatbot to analyze their feasibility. To achieve this, we propose a system that automatically generates malicious Korean prompts by analyzing existing prompt injection attacks. Specifically, we focus on generating malicious prompts that induce harmful expressions from large language models and validate their effectiveness in practice.

• Journal of Convergence Society for SMB
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• v.4 no.2
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• pp.13-18
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• 2014

Due to the development of information systems and the Internet, the Internet and smart phones can access networking in any where and any time. This causes the program to exploit various vulnerabilities and malicious code created to go out information, the disclosure of such crime increasing day by day. The proposed countermeasure model will be able to contribute to block all kinds of malicious code activities.