• Smart Media Journal
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• v.13 no.2
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• pp.85-94
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• 2024

Artificial Intelligence of Things (AIoT), which combines AI and the Internet of Things (IoT), has recently gained popularity. Deep neural networks (DNNs) have achieved great success in many applications. Deploying complex AI models on embedded boards, nevertheless, may be challenging due to computational limitations or intelligent model complexity. This paper focuses on an AIoT-based system for smart sewing automation using edge devices. Our technique included developing a detection model and a decision tree for a sufficient testing scenario. YOLOv5 set the stage for our defective sewing stitches detection model, to detect anomalies and classify the sewing patterns. According to the experimental testing, the proposed approach achieved a perfect score with accuracy and F1score of 1.0, False Positive Rate (FPR), False Negative Rate (FNR) of 0, and a speed of 0.07 seconds with file size 2.43MB.

• Journal of KIISE
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• v.44 no.4
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• pp.417-422
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• 2017

Anomaly detection is the identification of data that do not conform to a normal pattern or behavior model in a dataset. It can be utilized for detecting errors among data generated by devices or user behavior change in a social network data set. In this study, we proposed a new approach using rank correlation coefficient to efficiently detect abnormal data in devices of a building. With the increased push for energy conservation, many energy efficiency solutions have been proposed over the years. HVAC (Heating, Ventilating and Air Conditioning) system monitors and manages thousands of sensors such as thermostats, air conditioners, and lighting in large buildings. Currently, operators use the building's HVAC system for controlling efficient energy consumption. By using the proposed approach, it is possible to observe changes of ranking relationship between the devices in HVAC system and identify abnormal behavior in social network.

• Smart Media Journal
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• v.13 no.1
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• pp.18-23
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• 2024

Power efficiency management in smart farms is important due to its link to climate change. As climate change negatively impacts agriculture, future agriculture is expected to utilize smart farms to minimize climate impacts, but smart farms' power consumption may exacerbate the climate crisis due to the current electricity production system. Therefore, it is essential to efficiently manage and optimize the power usage of smart farms. In this study, we propose a system that monitors the power usage of smart farm equipment in real time and predicts the power usage one hour later using GRU. CT sensors are installed to collect power usage data, which are analyzed to detect and prevent abnormal patterns, and combined with IoT technology to efficiently manage and monitor the overall power usage. This helps to optimize power usage, improve energy efficiency, and reduce carbon emissions. The system is expected to improve not only the energy management of smart farms, but also the overall efficiency of energy use.

• Convergence Security Journal
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• v.17 no.1
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• pp.31-38
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• 2017

As the cyber-attacks through the networks advance, it is difficult for the intrusion detection system based on the simple rules to detect the novel type of attacks such as Advanced Persistent Threat(APT) attack. At present, many types of research have been focused on the application of machine learning techniques to the intrusion detection system in order to detect previously unknown attacks. In the case of using the machine learning techniques, the performance of the intrusion detection system largely depends on the feature set which is used as an input to the system. Generally, more features increase the accuracy of the intrusion detection system whereas they cause a problem when fast responses are required owing to their large elapsed time. In this paper, we present a network intrusion detection system based on artificial neural network, which adopts a multi-objective genetic algorithm to satisfy the both requirements: accuracy, and fast response. The comparison between the proposing approach and previously proposed other approaches is conducted against NSL_KDD data set for the evaluation of the performance of the proposing approach.

• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.69-74
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• 2021

With the recent development of biometric authentication technology, the user authentication techniques using biometric authentication are increasing. Various problems arised in certification techniques that use various existing methods such as ID/PW. Therefore, recently, a method of improving security by introducing biometric authentication as secondary authentication has been used. In this thesis, proposal of the user authentication system that can detect user identification and anomalies using ECGs that are extremely difficult to falsify through the electrical biometric signals from the heart among various biometric authentication devices is studied. The system detects user anomalies by comparing ECG data received from a wrist-mounted wearable device-type ECG measurement tool with identification and ECG data stored in blockchain form on the database and identifying the user's location through a beacon system.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.7
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• pp.1030-1037
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• 2013

Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.365-371
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• 2021

With the rapid growth in Internet users, web applications are becoming the main target of hackers. Most previous WAFs (Web Application Firewalls) target every single HTTP request packet rather than the overall behavior of the attacker, and are known to be difficult to detect new types of attacks. In this paper, we propose a web attack detection system based on user behavior using machine learning to detect attacks of unknown patterns. In order to define user behavior, we focus on features excluding areas where an attacker can camouflage as a normal user. The experimental results shows that by using the path and query information to define users' behaviors, best results for an accuracy of 99% with Decision forest.

• The Journal of the Korea Contents Association
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• v.7 no.3
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• pp.93-100
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• 2007

Internet had spread in all fields with the fast speed during the last 10 years. Lately, wireless network is also spreading rapidly. Also, number of times that succeed attack attempt and invasion for wireless network is increasing rapidly TMS system was developed to overcome these threat on wireless network. Existing TMS system supplies active confrontation mechanism on these threats. However, existent TMS has limitation that new form of attack do not filtered efficiently. Therefor this paper proposes a new method that it automatically compute the threat from the imput packets with vector space model and detect anomaly detection of wireless network. Proposed mechanism in this research analyzes similarity degree between packets, and detect something wrong symptom of wireless network and then classify these threats automatically.

• Journal of Biomedical Engineering Research
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• v.22 no.6
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• pp.503-510
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• 2001

In this paper. we describe a 32-channel bioimpedance measurement system It consists of 32 independent constant current sources of 50 kHz sinusoid. The amplitude of each current source can be adjusted using a 12-bit MDAC. After we applied a pattern of injection currents through 32 current injection electrodes. we measured induced boundary voltages using a variable-gain narrow-band instrumentation amplifier. a Phase-sensitive demodulator. and a 12-bit ADC. The system is interfaced to a PC for the control and data acquisition. We used the system to detect anomalies with different resistivity values in a saline Phantom with 290mm diameter The accuracy of the developed system was estimated as 2.42% and we found that anomalies larger than 8mm in diameter can be detected. We Plan to improve the accuracy by using a digital oscillator improved current sources by feedback control, Phase-sensitive A/D conversion. etc. to detect anomalies smaller than 1mm in diameter.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.219-222
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• 2006

In this paper an intrusion detection system technique of wireless Ad Hoc network is explained and the advantage of making them work in IEEE 802.15.4/ZigBee wireless standard is also discussed. The methodology that is mentioned here is intrusion detection architecture based on a local intrusion database [1]. An ad hoc network is a collection of nodes that is connected through a wireless medium forming rapidly changing topologies. Due to increased connectivity (especially on the Internet), and the vast spectrum of financial possibilities that are opening up, more and more systems are subject to attack by intruders. An ideal IDS should able to detect an anomaly caused by the intruders quickly so that the misbehaving node/nodes can be identified and appropriate actions (e.g. punish or avoid misbehaving nodes) can be taken so that further damage to the network is minimized