• ETRI Journal
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• v.39 no.5
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• pp.729-736
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• 2017

This work investigates secure cluster-aided multi-hop randomize-and-forward networks. We present a hop-by-hop multi-hop transmission scheme with relay selection, which evaluates for each cluster the relays that can securely receive the message. We propose an analytical model to derive the secure connectivity probability (SCP) of the hop-by-hop transmission scheme. For comparison, we also analyze SCPs of traditional end-to-end transmission schemes with two relay-selection policies. We perform simulations, and our analytical results verify that the proposed hop-by-hop scheme is superior to end-to-end schemes, especially with a large number of hops or high eavesdropper channel quality. Numerical results also show that the proposed hop-by-hop scheme achieves near-optimal performance in terms of the SCP.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3794-3820
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• 2019

In recent years, a cloud environment with the ability to detect illegal behaviours along with a secured data storage capability is much needed. This study presents a cloud storage framework, wherein a 128-bit encryption key has been generated by combining deoxyribonucleic acid (DNA) cryptography and the Hill Cipher algorithm to make the framework unbreakable and ensure a better and secured distributed cloud storage environment. Moreover, the study proposes a DNA-based encryption technique, followed by a 256-bit secure socket layer (SSL) to secure data storage. The 256-bit SSL provides secured connections during data transmission. The data herein are classified based on different qualitative security parameters obtained using a specialized fuzzy-based classification technique. The model also has an additional advantage of being able to decide on selecting suitable storage servers from an existing pool of storage servers. A fuzzy-based technique for order of preference by similarity to ideal solution (TOPSIS) multi-criteria decision-making (MCDM) model has been employed for this, which can decide on the set of suitable storage servers on which the data must be stored and results in a reduction in execution time by keeping up the level of security to an improved grade.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.147-162
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• 2017

With the advent of various services and devices in the change of IT environment, increasing the complexity of the data, and increasing scale of IT, Many organizations are experiencing the difficulty of analyzing and processing with a large amounts of data for security situations awareness. Therefore, propose the enhancement of security situational awareness through visualization in order to solve the problems of slow response and security situational awareness in organizational risk management. In this paper, we selected the evaluation factors and alternatives for effective visualization by considering user type, situational awareness step, and information visualization attributes through various studies on visualization. And established AHP layer model. Based on this, by using the AHP method for solving the problem of multi-criteria decision making, by calculating the factors for effectively visualizing and the importance of alternative by factor, try to propose a visualization method that can improve the effectiveness of the security situational awareness according to the purpose of visualization and the type of user.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.179-186
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• 2023

For the future prosperity of any society, the sound growth of children is essential. Autism Spectrum Disorder (ASD) is a neurobehavioral disorder which has an impact on social interaction of autistic child and has an undesirable effect on his learning, speaking, and responding skills. These children have over or under sensitivity issues of touching, smelling, and hearing. Its symptoms usually appear in the child of 4- to 11-year-old but parents did not pay attention to it and could not detect it at early stages. The process to diagnose in recent time is clinical sessions that are very time consuming and expensive. To complement the conventional method, machine learning techniques are being used. In this way, it improves the required time and precision for diagnosis. We have applied TFLite model on image based dataset to predict the autism based on facial features of child. Afterwards, various machine learning techniques were trained that includes Logistic Regression, KNN, Gaussian Naïve Bayes, Random Forest and Multi-Layer Perceptron using Autism Spectrum Quotient (AQ) dataset to improve the accuracy of the ASD detection. On image based dataset, TFLite model shows 80% accuracy and based on AQ dataset, we have achieved 100% accuracy from Logistic Regression and MLP models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.527-536
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• 2020

It has been found that an attacker can extract the secret key embedded in a security device and recover the operation instruction using power consumption traces which are some kind of side channel information. Many profiling-based side channel attacks based on a deep learning model such as MLP(Multi-Layer Perceptron) method are recently researched. In this paper, we implemented a disassembler for operation instruction set used in the micro-controller AVR XMEGA128-D4. After measuring the template traces on each instruction, we automatically made the pre-processing process and classified the operation instruction set using a deep learning model CNN. As an experimental result, we showed that all instructions are classified with 87.5% accuracy and some core instructions used frequently in device operation are with 99.6% respectively.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.71-80
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• 2024

The Internet of Things (IoT) is an emerging element that is becoming increasingly indispensable to the Internet and shaping our current understanding of the future of the Internet. IoT continues to extend deeper into the daily lives of people, offering distributed and critical services. In contrast with current Internet, IoT depends on a dynamic architecture where physical objects with embedded sensors will communicate via cloud to send and analyze data [1-3]. Its security troubles will surely impinge all aspects of civilization. Mobile agents are widely used in the context of the IoT and due to the possibility of transmitting their execution status from one device to another in an IoT network, they offer many advantages such as reducing network load, encapsulating protocols, exceeding network latency, etc. Also, cryptographic technologies, like PKI and Blockchain technology, and Artificial Intelligence are growing rapidly allowing the addition of an approved security layer in many areas. Security issues related to mobile agent migration can be resolved with the use of these technologies, thus allowing increased reliability and credibility and ensure information collecting, sharing, and processing in IoT environments, while ensuring maximum autonomy by relying on the AI to allow the agent to choose the most secure and optimal path between the nodes of an IoT environment. This paper aims to present a new model to secure mobile agents in the context of the Internet of Things based on Public Key Infrastructure (PKI), Ethereum Blockchain Technology and Q-learning. The proposed model provides a secure migration of mobile agents to ensure security and protect the IoT application against malevolent nodes that could infiltrate these IoT systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2815-2822
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• 2011

Clustering method in wireless sensor networks is the technique that forms the cluster to aggregate the data and transmit them at the same time that they can use the energy efficiently. In this paper, I propose the hierarchical unequal clustering method using cluster group model. This divides the entire network into two layers. The data aggregated from layer 2 consisted of cluster group is sent to layer 1, after re-aggregation the total data is sent to base station. This method decreases whole energy consumption by using cluster group model with multi-hop communication architecture. Hot spot problem can be solved by establishing unequal cluster. I also show that proposed hierarchical unequal clustering method is better than previous clustering method at the point of network energy efficiency.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.33-39
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• 2020

Interest in 5G communication security has been growing recently amid growing expectations for 5G technology with faster speed and stability than LTE. However, 5G has so far included disparate areas, so it has not yet fully supported the issues of security. This paper proposes a blockchain-based IoT management model in order to efficiently provide the authentication of users using IoT in 5G In order to efficiently fuse the authentication of IoT users with probabilistic theory and physical structure, the proposed model uses two random keys in reverse direction at different layers so that two-way authentication is achieved by the managers of layers and layers. The proposed model applied blockchain between grouped IoT devices by assigning weights to layer information of IoT information after certification of IoT users in 5G environment is stratified on a probabilistic basis. In particular, the proposed model has better functions than the existing blockchain because it divides the IoT network into layered, multi-layered networks.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.35-42
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• 2024

In this study, we propose a method for real-time recognition and analysis of dog behavior using a motion sensor and deep learning techonology. The existing home CCTV (Closed-Circuit Television) that recognizes dog behavior has privacy and security issues, so there is a need for new technologies to overcome them. In this paper, we propose a system that can analyze and care for a dog's behavior based on the data measured by the motion sensor. The study compares the MLP (Multi-Layer Perceptron) and CNN (Convolutional Neural Network) models to find the optimal model for dog behavior analysis, and the final model, which has an accuracy of about 82.19%, is selected. The model is lightened to confirm its potential for use in embedded environments.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.