• Title/Summary/Keyword: Activation of IoT

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Study for Activation Strategy Preemption of IoT Market (IoT 시장 선점을 위한 활성화 방안 연구)

  • Yang, Seung-Su;Shim, Jae-Sung;Park, Seck-Cheon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.04a
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    • pp.93-95
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    • 2015
  • IoT 시대가 도래하면서 다양한 분야에 서비스가 제공되는 미래회의 진입이 가능하게 되었으며 이에 IoT 서비스를 선정하기 위해 선진국에서의 활발한 움직임이 나타나고 있다. 따라서 본 논문에서는 IoT의 발전에 따라서 나타나게 된 CPND 가치 사슬의 변화를 살펴보고 국내외 선진국의 동향 및 이슈 사항을 분석하여 IoT 시장 선점 및 활성화를 위한 방안을 제시하고자 한다.

A comparative study of the performance of machine learning algorithms to detect malicious traffic in IoT networks (IoT 네트워크에서 악성 트래픽을 탐지하기 위한 머신러닝 알고리즘의 성능 비교연구)

  • Hyun, Mi-Jin
    • Journal of Digital Convergence
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    • v.19 no.9
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    • pp.463-468
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    • 2021
  • Although the IoT is showing explosive growth due to the development of technology and the spread of IoT devices and activation of services, serious security risks and financial damage are occurring due to the activities of various botnets. Therefore, it is important to accurately and quickly detect the activities of these botnets. As security in the IoT environment has characteristics that require operation with minimum processing performance and memory, in this paper, the minimum characteristics for detection are selected, and KNN (K-Nearest Neighbor), Naïve Bayes, Decision Tree, Random A comparative study was conducted on the performance of machine learning algorithms such as Forest to detect botnet activity. Experimental results using the Bot-IoT dataset showed that KNN can detect DDoS, DoS, and Reconnaissance attacks most effectively and efficiently among the applied machine learning algorithms.

Effects of Flexible Pole Training Combined with Lumbar Stabilization on Trunk Muscles Activation in Healthy Adults

  • Lim, Jae-Heon
    • The Journal of Korean Physical Therapy
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    • v.30 no.1
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    • pp.1-7
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    • 2018
  • Objective: This study aimed to determine the efficacy of flexible pole training combined with lumbar stabilization in improving trunk muscle activities and to investigate the difference according to posture in young adults. Methods: Twenty-five participants were enrolled in this study. The subjects were randomly allocated into either the flexible pole group or the rigid pole group. Participants performed lumbar stabilization exercises on quadruped and curl-up, with the flexible pole or rigid pole. Electromyography was used to assess the percent maximal voluntary isometric contracion (%MVIC) of the rectus abdominis (RA), external oblique (EO), internal oblique (IO), and erector spine (ES) muscles. All participants completed one 30-minute session per day, 3 days per week, for 6 weeks. The evaluation was performed before and 6 weeks after the training, and follow-up. The data were analyzed using independent t-test and two-way repeated measure analysis of variance to determine the statistical significance. Results: The flexible pole in curl-up showed significant differences in EO and IO muscle activities compared with the rigid pole. The flexible pole in quadruped showed significant differences in IO and ES muscle activities compared with the rigid pole. The RA, EO, IO, and ES muscle activities of both groups were significantly higher after 6 weeks training. Conclusion: The flexible pole in curl-up and quadruped showed an improvement in trunk muscle activation. The flexible pole combined with lumbar stabilization will be useful as an exercise tool to improve activity of trunk muscles.

Chameleon Hash-Based Mutual Authentication Protocol for Secure Communications in OneM2M Environments (OneM2M 환경에서 안전한 통신을 위한 카멜레온 해쉬 기반의 상호인증 프로토콜)

  • Kim, Sung-soo;Jun, Moon-seog;Choi, Do-hyeon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.10
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    • pp.1958-1968
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    • 2015
  • Things intelligence communication (M2M or IoT) service activation and global company of OneM2M-related business on aggressive investing and has led to the acceleration of change in the ICT market. But a variety of hacking security technology because of the possibility of secure communication (data exposure, theft, modification, deletion, etc.) has been issued as an important requirement. In this paper, we propose a mutual authentication protocol for secure communications chameleon hash based on the M2M environment. The results of performance analysis efficiency is encryption and decryption an average of 0.7%, calculated rate showed good results as compared to the target algorithm, equivalent to a 3%(Average 0.003 seconds) difference, mutual authentication and encryption region by using the key update advantage of ECC(Elliptic Curve Cryptography)based Chameleon hash function is signed of the operational efficiency, using a collision message verifiable properties demonstrated strong security of the communication section.

Service Platform of Regional Smart Tour Ecosystem Support (지역중심의 스마트관광 생태계 지원 서비스 플랫)

  • Weon, Dalsoo
    • The Journal of the Convergence on Culture Technology
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    • v.4 no.4
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    • pp.31-36
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    • 2018
  • The tourism industry has a great influence on national economy activation. The development of IT technology has enabled the collection and analysis of personal profile information, location information and activity information based on the characteristics, behavior, purchase propensity and interest of tourists. In order to realize this, the implementation of convergence smart tourism information service platform is completed by developing business model, IoT & Big Data integration management system, big data algorithm development and analysis platform in three stages. The underlying technology of the platform and algorithm needs a process of adopting open source, expanding the service element on the basis of it, and then complementing the problem through the test-bed demonstration test that connects the area. Using this platform, it is possible to develop a smart tourism environment that can provide customized services for each tourist by analyzing various information in an integrated manner. Also, it will be possible to improve the life of tourist destination residents and contribute to regional revitalization and job creation through the creation of smart tourism ecosystem focused on the region.

Effect of Slowly Forced Expiration on Abdominal Muscle Activity During Cross Knee Curl-Up Exercise

  • Yoon, Tae-Lim;Kim, Ki-Song
    • Physical Therapy Korea
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    • v.21 no.1
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    • pp.63-69
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    • 2014
  • Cross knee curl-up is an ideal variation of abdominal curl up exercise to strengthen abdominal musculature without excessive lumbar flexion which can increase the loads on the disc and ligaments. In addition, slowly forced expiration can facilitate the activation of the abdominal musculature. The purpose of this study was to determine the effects of slowly forced expiration on activity of abdominal muscles, such as rectus abdominis (RA), external oblique (EO), and transverse abdominis/internal oblique (TrA/IO), while cross knee curl-up. Eleven young and healthy subjects (6 males and 5 females) participated. All subjects performed the cross knee curl-up slowly forced expiration and natural breathing. Paired t-test was performed in normalized electromyogram (EMG) muscle activity of the bilateral RA, EO, and TrA/IO to compare the differences between the cross curl-up with slowly forced expiration and natural breathing. Statistical significance was set at .05. There were no significant differences in normalized EMG muscle activity of the bilateral RA, EO, and TrA/IO between the cross curl-up with slowly forced expiration and natural breathing. The finding of this study designates that slowly forced expiration does not induce increasing activity of abdominal muscle in cross knee curl-up; hence, learning step of breathing control might not be necessary to strengthen abdominal muscle in cross knee curl-up.

Effects of Shoulder Abduction in Opposite Directions on EMG Activity in the Abdominal Muscles during Single Leg Raising in the Supine Position on the Foam Roller in Healthy Subjects

  • Yun, Sung-Joon;Kim, Moon-Hwan
    • The Journal of Korean Physical Therapy
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    • v.27 no.4
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    • pp.270-274
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    • 2015
  • Purpose: The purpose of this study was to examine the electromyographic (EMG) activity of the abdominal muscles and to compare the activity ratios of the bilateral rectus abdominis (RA) to oblique abdominal muscles during shoulder abduction in opposite directions with single leg raising (SLR) performed in the supine position on a foam roller. Methods: Fifteen healthy subjects were recruited to the study. Each subject lay on the foam roller and performed left single leg raising with right or left shoulder $90^{\circ}$ abduction (Abd); performed in a random order. Surface EMG recordings of selected abdominal muscles (i.e., the RA, external oblique abdominis [EO], internal oblique abdominis [IO], and transverse abdominis [TrA]) were normalized to maximum voluntary isometric contraction. EO/RA and IO and TrA/RA ratios were determined with surface EMG. Data were analyzed by Independent t-test. The statistical significance level was p<0.05. Results: The results were as follows: (1) the right RA, left EO, and right IO and TrA muscle activities increased significantly at the left SLR with left Abd compared to the left SLR with right Abd (p<0.05); and (2) the ratio of right EO/RA activity increased significantly at the left SLR with right Abd compared to left Abd (p<0.05). Conclusion: These findings suggest that left SLR with left Abd on a foam roller is an appropriate exercise for activation of specific oblique abdominal muscles.

Hybrid Tensor Flow DNN and Modified Residual Network Approach for Cyber Security Threats Detection in Internet of Things

  • Alshehri, Abdulrahman Mohammed;Fenais, Mohammed Saeed
    • International Journal of Computer Science & Network Security
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    • v.22 no.10
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    • pp.237-245
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    • 2022
  • The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

Reverse Engineering of Deep Learning Network Secret Information Through Side Channel Attack (부채널 분석을 이용한 딥러닝 네트워크 신규 내부 비밀정보 복원 방법 연구)

  • Park, Sujin;Lee, Juheon;Kim, HeeSeok
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.32 no.5
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    • pp.855-867
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    • 2022
  • As the need for a deep learning accelerator increases with the development of IoT equipment, research on the implementation and safety verification of the deep learning accelerator is actively. In this paper, we propose a new side channel analysis methodology for secret information that overcomes the limitations of the previous study in Usenix 2019. We overcome the disadvantage of limiting the range of weights and restoring only a portion of the weights in the previous work, and restore the IEEE754 32bit single-precision with 99% accuracy with a new method using CPA. In addition, it overcomes the limitations of existing studies that can reverse activation functions only for specific inputs. Using deep learning, we reverse activation functions with 99% accuracy without conditions for input values with a new method. This paper not only overcomes the limitations of previous studies, but also proves that the proposed new methodology is effective.

A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning (IoT 및 딥 러닝 기반 스마트 팜 환경 최적화 및 수확량 예측 플랫폼)

  • Choi, Hokil;Ahn, Heuihak;Jeong, Yina;Lee, Byungkwan
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.6
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    • pp.672-680
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    • 2019
  • This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.