• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.617-622
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• 2005

Lately, the MANET research has focused on providing routing services with security and previous schemes have been proposed for detecting malicious nodes in a MANET. However, they still include some problems which malicious nodes cannot be found when they falsely report other nodes as malicious. Accordingly, we propose a novel and efficient scheme for detecting malicious nodes using report messages and a report table which is consisted of node ID both for suspecting and reporting when the malicious nodes behave normally during the route discovery, but the other hand they drop and modify packets, or falsely report other nodes as malicious during the data transmission. Our proposed scheme is applicable to not only DSR but aiso AODV. And we provide some simulation results of our proposed scheme by comparing general AODV with our proposed scheme. Simulation results show that our Proposed scheme outperforms general AODV in terms of average packet loss ratio and packet delivery ratio.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.641-649
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• 2020

As the usage of mobile devices extremely increases, malicious mobile apps(applications) that target mobile users are also increasing. It is challenging to detect these malicious apps using traditional malware detection techniques due to intelligence of today's attack mechanisms. Deep learning (DL) is an alternative technique of traditional signature and rule-based anomaly detection techniques and thus have actively been used in numerous recent studies on malware detection. In order to develop DL-based defense mechanisms against intelligent malicious apps, feeding recent datasets into DL models is important. In this paper, we develop a DL-based model for detecting intelligent malicious apps using KU-CISC 2018-Android, the most up-to-date dataset consisting of benign and malicious Android apps. This dataset has hardly been addressed in other studies so far. We extract OPcode sequences from the Android apps and preprocess the OPcode sequences using an N-gram model. We then feed the preprocessed data into LSTM and apply the concept of Information Gain to improve performance of detecting malicious apps. Furthermore, we evaluate our model with numerous scenarios in order to verify the model's design and performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.593-603
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• 2020

The advent of malicious code has increased exponentially due to the spread of malicious code generation tools in accordance with the development of the network, but there is a limit to the response through existing malicious code detection methods. According to this situation, a machine learning-based malicious code detection method is evolving, and in this paper, the feature of data is extracted from the PE header for machine-learning-based malicious code detection, and then it is used to automate the malware through autoencoder. Research on how to extract the indicated features and feature importance. In this paper, 549 features composed of information such as DLL/API that can be identified from PE files that are commonly used in malware analysis are extracted, and autoencoder is used through the extracted features to improve the performance of malware detection in machine learning. It was proved to be successful in providing excellent accuracy and reducing the processing time by 2 times by effectively extracting the features of the data by compressively storing the data. The test results have been shown to be useful for classifying malware groups, and in the future, a classifier such as SVM will be introduced to continue research for more accurate malware detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.785-796
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• 2012

In this paper, we propose an effective Behavior-based detection technique using the frequency of system calls to detect malicious code, when the number of training data is fewer than the number of properties on system calls. In this study, we collect the Native APIs which are Windows kernel data generated by running program code. Then we adopt the normalized freqeuncy of Native APIs as the basic properties. In addition, the basic properties are transformed to new properties by GLDA(Generalized Linear Discriminant Analysis) that is an effective method to discriminate between malicious code and normal code, although the number of training data is fewer than the number of properties. To detect the malicious code, kNN(k-Nearest Neighbor) classification, one of the bayesian classification technique, was used in this paper. We compared the proposed detection method with the other methods on collected Native APIs to verify efficiency of proposed method. It is presented that proposed detection method has a lower false positive rate than other methods on the threshold value when detection rate is 100%.

• Convergence Security Journal
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• v.21 no.4
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• pp.109-115
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• 2021

Currently, with the development of 5G and IoT technology, it is being used in connection with the things used in real life through a network. However, attempts to use networked computers for malicious purposes are increasing, and attacks using malicious codes that infringe the confidentiality and integrity of user information are becoming more intelligent. As a countermeasure to this, research is being conducted on a method of detecting malicious packets using a security control system and AI technology, supervised learning. The cyber security control system is being operated inefficiently in terms of manpower and cost. In addition, in the era of the COVID-19 pandemic, remote work has increased, making it difficult to respond immediately. In addition, malicious code detection using the existing AI technology, supervised learning, does not detect variant malicious code, and has an inaccurate malicious code detection rate depending on the quantity and quality of data. Therefore, in this study, by converging malicious packet detection technologies through various machine learning and deep learning models, the accuracy of malicious packet detection is increased, the false positive rate and the false positive rate are reduced, and a new type of malicious packet can be efficiently detected when intrusion. We propose a malicious packet detection technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.6
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• pp.1133-1146
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• 2011

Cognitive radio (CR) is considered one of the most promising next-generation communication systems; it has the ability to sense and make use of vacant channels that are unused by licensed users. Reliable detection of the licensed users' signals is an essential element for a CR network. Cooperative spectrum sensing (CSS) is able to offer better sensing performance as compared to individual sensing. The presence of malicious users who falsify sensing data can severely degrade the sensing performance of the CSS scheme. In this paper, we investigate a secure CSS scheme, based on the Kullback-Leibler Divergence (KL-divergence) theory, in order to identify malicious users and mitigate their harmful effect on the sensing performance of CSS in a CR network. The simulation results prove the effectiveness of the proposed scheme.

• Convergence Security Journal
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• v.23 no.5
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• pp.101-106
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• 2023

Recently, cyberattacks are increasing in social engineering attacks using intelligent and continuous phishing sites and hacking techniques using malicious code. As personal security becomes important, there is a need for a method and a solution for determining whether a malicious URL exists using a web application. In this paper, we would like to find out each feature and limitation by comparing highly accurate techniques for detecting malicious URLs. Compared to classification algorithm models using features such as web flat panel DB and based URL detection sites, we propose an efficient URL anomaly detection technique.

• Journal of Communications and Networks
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• v.15 no.2
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• pp.164-172
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• 2013

We consider the problem of secure and robust clustering for quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose a new method for jointly activating the best group of candidate sensors that participate in data aggregation, detecting the malicious sensors and estimating the target position. Firstly, we select the appropriate group in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Secondly, we detect the malicious sensor nodes based on the information relevance of their measurements. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The selection of the candidate sensors group is based on multi-criteria function, which is computed by using the predicted target position provided by the QVF algorithm, while the malicious sensor nodes detection is based on Kullback-Leibler distance between the current target position distribution and the predicted sensor observation. The performance of the proposed method is validated by simulation results in target tracking for WSN.

• Journal of IKEEE
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• v.25 no.3
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• pp.451-461
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• 2021

As various smart devices spread and the damage caused by malicious codes becomes more serious, malicious code detection technology using machine learning technology is attracting attention. However, if the training data of machine learning is constructed based on only the fragmentary characteristics of the code, it is still easy to create variants and new malicious codes that avoid it. To solve such a problem, a research using the function call relationship of malicious code as training data is attracting attention. In particular, it is expected that more advanced malware detection will be possible by measuring the similarity of graphs using GNN. This paper proposes an efficient method to generate a function call graph from binary code to utilize GNN for malware detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.431-438
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• 2019

Fileless malware uses memory injection attacks to hide traces of payloads to perform malicious works. During the memory injection attack, an attack named "process hollowing" is a method of creating paused benign process like system processes. And then injecting a malicious payload into the benign process allows malicious behavior by pretending to be a normal process. In this paper, we propose a method to detect the memory injection regardless of whether or not the malicious action is actually performed when a process hollowing attack occurs. The replication process having same execution condition as the process of suspending the memory injection is executed, the data set belonging to each process virtual memory area is compared using the fuzzy hash, and the similarity is calculated.