• Convergence Security Journal
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• v.18 no.2
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• pp.49-58
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• 2018

As the demand for Internet of Things(IoT) increases, the need for the security of IoT devices is increasing steadily. It is difficult to apply the conventional security theory to IoT devices because IoT devices are subject to be constrained by some factors such as hardware, processor, and energy. Nowadays we have several security guidelines and related documents on IoT device. Most of them, however, do not consider the characteristics of specific IoT devices. Since they describes the security issues comprehensively, it is not easy to explain the specific security level reflecting each characteristics of IoT devices. In addition, most existing guidelines and related documents are described in view of developers and service proposers, and thus ordinary users are not able to assess whether a specific IoT device can protect their information securely or not. We propose an security evaluation model, based on the existing guidelines and related documents, for more specific IoT devices and prove that this approach is more convenient to ordinary users by creating checklists for the smart watch.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.269-274
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• 2016

In this paper We focuses on suggestion to quality model for IoT infrastructure services for Internet of Things. Quality model is suggested on security set out in ISO25000 quality factors and assessment of the existing traditional software application of ISO 9126 quality model. We validated that the proposed model can be realized it was applied to evaluate the 4 elements and related security in Metrics.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.281-288
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• 2024

The sixth generation(6G) wireless communication technology is advancing toward ultra-high speed, ultra-high bandwidth, and hyper-connectivity. With the development of communication technologies, the formation of a hyper-connected society is rapidly accelerating, expanding from the IoT(Internet of Things) to the IoE(Internet of Everything). However, at the same time, security threats targeting IoT devices have become widespread, and there are concerns about security incidents such as unauthorized access and information leakage. As a result, the need for security-enhancing solutions is increasing. In this paper, we implement an autoencoder-based anomaly detection model utilizing real-time collected network traffics in respond to IoT security threats. Considering the difficulty of capturing IoT device traffic data for each attack in real IoT environments, we use an unsupervised learning-based autoencoder and implement 6 different autoencoder models based on the use of noise in the training data and the dimensions of the latent space. By comparing the model performance through experiments, we provide a performance evaluation of the anomaly detection model for detecting abnormal network traffic.

• Convergence Security Journal
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• v.14 no.7
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• pp.45-51
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• 2014

The most important point in the QoS management system to ensure the quality of the IoT system design goal is quality measurement system and the quality evaluation system. This research study is a matrix model for the IoT based on key quality measures by diagnosis system structure and function. Developing for the quality metrics measured Internet of Things environment will provide the foundation for the Internet of Things quality measurement/analysis. IoT matrix system for quality evaluation is a method to describe the functional requirements and the quality requirements in a single unified table for quality estimation performed. Comprehensive functional requirements and quality requirements by assessing the association can improve the reliability and usability evaluation. When applying the proposed method IoT quality can be improved while reducing the QoS signaling, the processing, the basis for more efficient quality assurances as a whole.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1613-1627
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• 2017

The concept of the Internet of Things (IoT) enables physical objects or things to be virtually accessible for both consuming and providing services. Undue access from irresponsible activities becomes an interesting issue to address. Maintenance of data integrity and privacy of objects is important from the perspective of security. Privacy can be achieved through various techniques: password authentication, cryptography, and the use of mathematical models to assess the level of security of other objects. Individual methods like these are less effective in increasing the security aspect. Comprehensive security schemes such as the use of frameworks are considered better, regardless of the framework model used, whether centralized, semi-centralized, or distributed ones. In this paper, we propose a new semi-centralized security framework that aims to improve privacy in IoT using the parameters of trust and reputation. A new algorithm to elect a reputation coordinator, i.e., ConTrust Manager is proposed in this framework. This framework allows each object to determine other objects that are considered trusted before the communication process is implemented. Evaluation of the proposed framework was done through simulation, which shows that the framework can be used as an alternative solution for improving security in the IoT.

• Convergence Security Journal
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• v.17 no.5
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• pp.3-10
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• 2017

Standard Protocol Optimized for Resource-Constrained IoT Environment Constrained Application Protocol (CoAP) supports web-based communication between a sensor node in the IoT environment and a client on the Internet. The CoAP is a Request / Response model that responds to the client's CoAP Request message by responding with a CoAP Response message from the server. CoAP recommends the use of CoAP-DTLS for message protection. However, validation of the use of DTLS in the IoT environment is underway. We analyze CoAP and DTLS security mode, evaluate performance of secure channel creation time, security channel creation step time, and RAM / ROM consumption through Cooja simulator and evaluate the possibility of real environment application.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.187-193
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• 2017

Objects Internet-based healthcare services provide healthcare and healthcare services, including measurement of user's vital signs, diagnosis and prevention of diseases, through a variety of object internet devices. However, there is a problem that new security vulnerability can occur when inter-working with the security weakness of each element technology because the internet service based on the object Internet provides a service by integrating various element technologies. In this paper, we propose a user privacy protection model that can securely process user's healthcare information from a third party when delivering healthcare information of users using wearable equipment based on IoT in a mobile environment to a server. The proposed model provides attribute values for each healthcare sensor information so that the user can safely handle, store, and store the healthcare information, thereby managing the privacy of the user in a hierarchical manner. As a result of the performance evaluation, the throughput of IoT device is improved by 10.5% on average and the server overhead is 9.9% lower than that of the existing model.

• Convergence Security Journal
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• v.22 no.4
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• pp.17-24
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• 2022

Recently, machine learning-based cyber attack detection and classification research has been actively conducted, achieving a high level of detection accuracy. However, low-spec IoT devices and large-scale network traffic make it difficult to apply machine learning-based detection models in IoT environment. Therefore, In this paper, we propose an efficient IoT attack detection and classification method through PCA(Principal Component Analysis) and LightGBM(Light Gradient Boosting Model) using datasets collected in a MQTT(Message Queuing Telementry Transport) IoT protocol environment that is also used in the defense field. As a result of the experiment, even though the original dataset was reduced to about 15%, the performance was almost similar to that of the original. It also showed the best performance in comparative evaluation with the four dimensional reduction techniques selected in this paper.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.181-192
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• 2017

Internet of Things includes the whole process of collected information generated from a variety of objects, as well as analyzing and sharing it, and providing useful information services to people. This study seeks ways to improve security and safety in the areas of service security technology, ID management technology and service access control, all of which take place in the IoT environment. We have implemented the services that can design and issue C&C (Certificate and Capability) service token authentication, which is based on a public key, to improve the service security. In addition, we suggest LCRS (Left Child-Right Sibling) resource model management for the efficient control of resources when generating the resource services from the data collected from node devices. We also implemented an IoT services platform to manage URL security of the resource services and perform access control for services.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.