• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4011-4027
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• 2021

Cloud Computing has emerged as an extensively used technology not only in the IT sector but almost in all sectors. As the nature of the cloud is distributed and dynamic, the jeopardies present in the current implementations of virtualization, numerous security threats and attacks have been reported. Considering the potent architecture and the system complexity, it is indispensable to adopt fundamentals. This paper proposes a secure authentication and data sharing scheme for providing security to the cloud data. An efficient IPSO-KELM is proposed for detecting the malicious behaviour of the user. Initially, the proposed method starts with the authentication phase of the data sender. After authentication, the sender sends the data to the cloud, and the IPSO-KELM identifies if the received data from the sender is an attacked one or normal data i.e. the algorithm identifies if the data is received from a malicious sender or authenticated sender. If the data received from the sender is identified to be normal data, then the data is securely shared with the data receiver using SHA256-RSA algorithm. The upshot of the proposed method are scrutinized by identifying the dissimilarities with the other existing techniques to confirm that the proposed IPSO-KELM and SHA256-RSA works well for malicious user detection and secure data sharing in the cloud.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.481-491
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• 2020

The malicious URLs which can be used for sending malicious codes and illegally acquiring private information is one of the biggest threat of information security field. Particularly, recent prevalence of smart-phone increases the possibility of the user's exposing to malicious URLs. Since the way of hiding the URL from the user is getting more sophisticated, it is getting harder to detect it. In this paper, after conducting a survey of the user experiences related to malicious URLs, we are proposing the rule-based malicious URL detection method. In addition, we have developed java library which can be applied to any other applications which need to handle the malicious URL. Each class of the library is implementation of a rule for detecting a characteristics of a malicious URL and the library itself is the set of rule which can have the chain of rule for deteciing more complicated situation and enhancing the accuracy. This kinds of rule based approach can enhance the extensibility considering the diversity of malicious URLs.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.74-80
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• 2015

In cognitive radios, spectrum sensing plays an important role in accurately detecting the presence or absence of a licensed user. However, the intervention of malicious users (MUs) degrades the performance of spectrum sensing. Such users manipulate the local results and send falsified data to the data fusion center; this process is called spectrum sensing data falsification (SSDF). Thus, MUs degrade the spectrum sensing performance and increase uncertainty issues. In this paper, we propose a method based on the Hausdorff distance and a similarity measure matrix to measure the difference between the normal user evidence and the malicious user evidence. In addition, we use the Dempster-Shafer theory to combine the sets of evidence from each normal user evidence. We compare the proposed method with the k-means and Jaccard distance methods for malicious user detection. Simulation results show that the proposed method is effective against an SSDF attack.

• 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 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.

• Advances in nano research
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• v.15 no.6
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• pp.513-520
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• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.31-43
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• 2014

Large number of Android mobile application has been developed and deployed through the Android open market by increasing android-based smart work device users recently. But, it has been discovered security vulnerabilities on malicious applications that are developed and deployed through the open market or 3rd party market. There are issues to leak user's personal and financial information in mobile devices to external server without the user's knowledge in most of malicious application inserted Trojan Horse forms of malicious code. Therefore, in order to minimize the damage caused by malignant constantly increasing malicious application, it is required a proactive detection mechanism development. In this paper, we analyzed the existing techniques' Pros and Cons to detect a malicious application and proposed discrimination and detection result using malicious application discrimination mechanism based on Jaccard similarity after collecting events occur in real-time execution on android-mobile devices.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.711-717
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• 2018

The breakthrough in computer and network has facilitated a variety of information exchange. However, at the same time, malicious users and groups are attacking vulnerable systems. Intrusion Detection System(IDS) detects malicious behaviors through network packet analysis. However, it has a burden of processing a large amount of packets in a short time. Therefore, in order to solve these problem, we propose a network intrusion detection system that operates at kernel level to improve detection performance at user level. In fact, we confirmed that the network intrusion detection system implemented at kernel level improves packet analysis and detection performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1215-1224
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• 2014

Various research was done to protect user's private data from malicious application which expose user's private data and abuse exposed data. However, a new type of malicious application were appeared. And these malicious applications use a smart phone as a new tools to perform secondary attack. Therefore, in this paper, we propose a method to detect the DDoS attack application installed inside the mobile device using the Android logging system.