• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.153-158
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• 2022

The Internet of Things (IoT) has gotten a lot of research attention in recent years. IoT is seen as the internet's future. IoT will play a critical role in the future, transforming our lifestyles, standards, and business methods. In the following years, the use of IoT in various applications is likely to rise. In the world of information technology, cyber security is critical. In today's world, protecting data has become one of the most difficult tasks. Different type of emerging cyber threats such as malicious, network based and abuse of network have been identified in the IoT. These can be done by virus, Phishing, Spam and insider abuse. This paper focuses on emerging threats, various challenges and vulnerabilities which are faced by the cyber security in the field of IoT and its applications. It focuses on the methods, ethics, and trends that are reshaping the cyber security landscape. This paper also focuses on an attempt to classify various types of threats, by analyzing and characterizing the intruders and attacks facing towards the IoT devices and its services.

• International journal of advanced smart convergence
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• v.5 no.4
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• pp.54-56
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• 2016

Code-reuse attacks are very dangerous in various systems. This is because they do not inject malicious codes into target systems, but reuse the instruction sequences in executable files or libraries of target systems. Moreover, code-reuse attacks could be more harmful to IoT systems in the sense that it may not be easy to devise efficient and effective mechanism for code-reuse attack detection in resource-restricted IoT devices. In this paper, we propose a detection scheme with using Kullback-Leibler (KL) divergence to combat against code-reuse attacks in IoT. Specifically, we detect code-reuse attacks by calculating KL divergence between the probability distributions of the packets that generate from IoT devices and contain code region addresses in memory system and the probability distributions of the packets that come to IoT devices and contain code region addresses in memory system, checking if the computed KL divergence is abnormal.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.1
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• pp.34-42
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• 2019

Receiving great attention of IoT and 4th industrial revolution, the necessity comes to the fore of the plant system which aims making it smart and effective. Smart Factory is the key realm of IoT to apply with the concept to optimize the entire process and it presents a new and flexible production paradigm based on the collected data from numerous sensors installed in a plant. Especially, the wireless sensor network technology is receiving attention as the key technology of Smart Factory, researches to interface those technology is actively in progress. In addition, IoT devices for plant industry security and high reliable network protocols are under development to cope with high-risk plant facilities. In the meanwhile, Blockchain can support high security and reliability because of the hash and hash algorithm in its core structure and transaction as well as the shared ledger among all nodes and immutability of data. With the reason, this research presents Blockchain as a method to preserve security and reliability of the wireless communication technology. In regard to that, it establishes some of key concepts of the possibility on the blockchain based IoT Edge devices for Plant O&M (Operations and Maintenance), and fulfills performance verification with test devices to present key indicator data such as transaction elapsed time and CPU consumption rate.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.9-16
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• 2021

Due to the prolonged infectious disease of COVID-19 worldwide, there are various security threats due to network attacks on Internet of Things devices that are vulnerable to telecommuting. Initially, users of Internet of Things devices were exploited for vulnerabilities in Remote Desktop Protocol, spear phishing and APT attacks. Since then, the technology of network attacks has gradually evolved, exploiting the simple service discovery protocol of Internet of Things devices, and DRDoS attacks have continued to increase. Existing SSDPs are accessible to unauthorized devices on the network, resulting in problems with information disclosure and amplification attacks on SSDP servers. To compensate for the problem with the authentication procedure of existing SSDPs, we propose a hash-based SSDP that encrypts server-specific information with hash and adds authentication fields to both Notify and M-Search message packets to determine whether an authorized IoT device is present.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.55-64
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• 2023

The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST²¹ and KDDTEST⁺ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST⁺ than KDDTEST²¹ because there are few attacks from minor samples in KDDTEST⁺. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.303-311
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• 2022

The tremendous growth of the Internet of things is unbelievable. Many IoT devices have emerged on the market over the last decade. This has made our everyday life easier inside our homes. The technology used at home has changed significantly over the past several decades, leading to what is known today as the smart home. However, this growth has also brought new challenges to our home security and privacy. With the smart home becoming more mainstream, cybersecurity issues have become a fundamental concern. The smart home is an environment where heterogeneous devices and appliances are interconnected through the Internet of Things (IoT) to provide smart services to residents. These services include home climate control, energy management, video on demand, music on-demand, remote healthcare, remote control, and other similar services in a ubiquitous manner. Smart home devices can be controlled via the Internet using smartphones. However, connecting smart home appliances to wireless networks and the Internet makes individuals vulnerable to malicious attacks. Remote access within the same environment or over the Internet requires an effective access control mechanism. This paper intends to shed light on how smart home devices are working as well as the type of security and privacy threats of the smart home. It also illustrated the types of authentication methods that can be used with smart home devices. In addition, a comparison of Smart home IoT-based security protocols was presented along with a security countermeasure that can be used in a smart home environment. Finally, a few open problems were mentioned as future research directions for researchers.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.59-63
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• 2023

Various devices have been emerging as a means of measuring indoor air quality, and among them, there are devices that support real-time remote monitoring through IoT technology and a cloud environment. To improve indoor air quality, based on the results determined by measuring devices, air purifiers or ventilation systems may need to be operated, and temperature and humidity control may be required. In this paper, we propose a design of indoor air quality measuring devices required for indoor air quality evaluation, and of the system needed to control relevant devices to improve indoor air quality through the interaction with the measuring devices. Currently, the servers for the interaction of indoor air quality devices and IoT devices are divided into conventional server type and serverless type, comparing the differences in response time of IoT devices to changes of indoor air quality.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.895-896
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• 2015

With the development of sensor, wireless mobile communication, embedded system and cloud computing, the technologies of Internet of Things have been widely used in logistics, Smart devices security, intelligent building and o on. Bridging between wireless sensor networks with traditional communication networks or Internet, IoT gateway plays n important role in IoT applications, which facilitates the integration of wireless sensor networks and mobile communication networks or Internet, and the management and control with wireless sensor networks. The IoT Gateway is a key component in IoT application systems but It has lot of security issues. We analyzed the trends of security and privacy matters.

• 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.