• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.95-102
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• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• Current Optics and Photonics
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• 제8권3호
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• pp.230-238
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• 2024

The refractive index is a key material-design parameter, especially for high-refractive-index glasses, which are used for precision optics and devices. Increased demand for high-precision optical lenses produced by the glass-mold-press (GMP) process has spurred extensive studies of proper glass materials. B₂O₃, SiO₂, and multiple heavy-metal oxides such as Ta₂O₅, Nb₂O₅, La₂O₃, and Gd₂O₃ mostly compose the high-refractive-index glasses for GMP. However, due to many oxides including up to 10 components, it is hard to predict the refractivity solely from the composition of the glass. In this study, the refractive index of optical glasses based on the B₂O₃-La₂O₃-Ta₂O₅-SiO₂ system is predicted using machine learning (ML) and compared to experimental data. A dataset comprising up to 271 glasses with 10 components is collected and used for training. Various ML algorithms (linear-regression, Bayesian-ridge-regression, nearest-neighbor, and random-forest models) are employed to train the data. Along with composition, the polarizability and density of the glasses are also considered independent parameters to predict the refractive index. After obtaining the best-fitting model by R² value, the trained model is examined alongside the experimentally obtained refractive indices of B₂O₃-La₂O₃-Ta₂O₅-SiO₂ quaternary glasses.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제1권1호
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• pp.11-20
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• 2012

스트림 데이터는 시간에 따라 연속적으로 변화하는 일련의 값들로 나타난다. 이러한 스트림 데이터의 특성상 다양한 시간 간격의 기준에 따라 계속적으로 그 동향이 달라질 수 있다. 이 때문에 스트림 데이터의 추세 예측은 간격이 갱신될 때 마다 연속적인 환경에서 여러 간격들을 기준으로 동시에 이루어지는 연속 다중 예측(Continuous Multiple Prediction, CMP)이 지원되어야 한다. 본 논문은 스트림 데이터의 연속 다중 예측을 효과적으로 지원하기 위하여, 신피질 학습 모델인 계층형 시간적 메모리(Hierarchical Temporal Memory, HTM) 모델을 확장하여 연속통합 HTM(Continuous Integrated HTM, CIHTM) 네트워크를 제안한다. 이를 위해 우리는 HTM 네트워크를 구성하는 기존 노드들 외에 새롭게 이동 벡터 파일 센서, 시공간 분류 노드, 다중 통합 노드를 고안하였다. 그리고 이들을 바탕으로 CIHTM 네트워크의 학습과 추론 알고리즘을 개발하였다.

• 해양환경안전학회지
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• 제28권1호
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• pp.184-192
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• 2022

밸브의 내부 누설 현상은 밸브의 내부 부품의 손상에 의해 발생하며 배관 시스템의 사고와 운전정지를 일으키는 주요 요인이다. 본 연구는 버터플라이형 밸브의 내부 누설에 따라 배관계에서 발생하는 음향방출 신호를 이용하여 배관 가동 중 실시간 누설 진단의 가능성을 검토하였다. 이를 위해 밸브의 작동 모드별로 측정한 시간영역의 AE 원시신호를 취득하였으며 이로부터 구축한 데이터셋은 데이터 기반의 인공지능 알고리즘에 적용하여 밸브의 내부 누설 유무를 진단하는 모델을 생성하였다. 누설 유무진단을 분류의 문제로 정의하여 SVM 기반의 머신러닝과 CNN 기반의 딥러닝 분류 알고리즘을 적용하였다. 데이터의 특징 추출에 기반한 SVM 분류 모델의 경우, 이진분류 모델에서 구축된 모델에 따라 83~90%의 정확도를 나타냈으며, 다중 클래스인 경우 분류 정확도가 66%로 감소하였다. 반면, CNN 기반의 다중 클래스 분류 모델의 경우 99.85%의 분류 정확도를 얻을 수 있었다. 결론적으로 밸브 내부 누설 진단을 위한 SVM 분류모델은 다중 클래스의 정확도 향상을 위해 적절한 특징 추출이 필요하며, CNN 기반의 분류모델은 프로세서의 성능 저하만 없다면 누설진단과 밸브 개도 분류에 효율적인 접근방법임을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.212-222
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• 2021

Multiple waves of COVID-19 highlighted one crucial aspect of this pandemic worldwide that factors affecting the spread of COVID-19 infection are evolving based on various regional and local practices and events. The introduction of vaccines since early 2021 is expected to significantly control and reduce the cases. However, virus mutations and its new variant has challenged these expectations. Several countries, which contained the COVID-19 pandemic successfully in the first wave, failed to repeat the same in the second and third waves. This work focuses on COVID-19 pandemic control and management in Saudi Arabia. This work aims to predict new cases using deep learning using various important factors. The proposed method is called Deep Learning and Dynamic Weighing-based (DLDW) COVID-19 cases prediction method. Special consideration has been given to the evolving factors that are responsible for recent surges in the pandemic. For this purpose, two weights are assigned to data instance which are based on feature importance and dynamic weight-based time. Older data is given fewer weights and vice-versa. Feature selection identifies the factors affecting the rate of new cases evolved over the period. The DLDW method produced 80.39% prediction accuracy, 6.54%, 9.15%, and 7.19% higher than the three other classifiers, Deep learning (DL), Random Forest (RF), and Gradient Boosting Machine (GBM). Further in Saudi Arabia, our study implicitly concluded that lockdowns, vaccination, and self-aware restricted mobility of residents are effective tools in controlling and managing the COVID-19 pandemic.

• Computers and Concrete
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• 제12권2호
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• pp.187-209
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• 2013

This paper presents a study on the prediction of transfer length of 13 mm seven-wire prestressing steel strand in pretensioned prestressed concrete members with rectangular cross-section including several material properties and design and manufacture parameters. To this end, a carefully selected database consisting of 207 different cases coming from 18 different sources spanning a variety of practical transfer length prediction situations was compiled. 16 single input features and 5 combined input features are analyzed. A widely used feedforward neural regression model was considered as a representative of several machine learning methods that have already been used in the engineering field. Classical multiple linear regression was also considered in order to comparatively assess performance and robustness in this context. The results show that the implemented model has good prediction and generalization capacity when it is used on large input data sets of practical interest from the engineering point of view. In particular, a neural model is proposed -using only 4 hidden units and 10 input variables-which significantly reduces in 30% and 60% the errors in transfer length prediction when using standard linear regression or fixed formulas, respectively.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권6호
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• pp.386-391
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• 2017

본 논문에서는 실생활에서 수집한 웨어러블 센서 데이터에서 사용자의 체험 기반 감정 태그정보를 자동으로 부여하는 시스템을 제안한다. 사용자 본인의 감정과 사용자가 보고 듣는 정보를 종합적으로 고려하여 네 가지의 감정 태그를 정의한다. 직접 수집한 웨어러블 센서 데이터를 중심으로 기존 감성컴퓨팅 연구를 통해 알려진 보조 정보를 결합하여, 다중 센서 데이터를 입력으로 하고 감정 태그를 구분하는 머신러닝 기반 분류 시스템을 학습하였다. 다중 모달리티 기반 감정 태깅 시스템의 유용성을 보이기 위해, 기존의 단일 모달리티 기반의 감정 인식 접근법과의 정량적, 정성적 비교를 한다.

• International Journal of Internet, Broadcasting and Communication
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• 제8권4호
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• pp.19-25
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• 2016

There are many free applications that need users to sign up before they can use the applications nowadays. It is difficult to choose a suitable password for your account. If the password is too complicated, then it is hard to remember it. However, it is easy to be intruded by other users if we use a very simple password. Therefore, biometric-based approach is one of the solutions to solve the issue. The biometric-based approach includes keystroke dynamics on keyboard, mice, or mobile devices, gait analysis and many more. The approach can integrate with any appropriate machine learning algorithm to learn a user typing behavior for authentication system. Preprocessing phase is one the important role to increase the performance of the algorithm. In this paper, we have proposed ensemble-by-session (EBS) method which to operate the preprocessing phase before the training phase. EBS distributes the dataset into multiple sub-datasets based on the session. In other words, we split the dataset into session by session instead of assemble them all into one dataset. If a session is considered as one day, then the sub-dataset has all the information on the particular day. Each sub-dataset will have different information for different day. The sub-datasets are then trained by a machine learning algorithm. From the experimental result, we have shown the improvement of the performance for each base algorithm after the preprocessing phase.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.541-556
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• 2020

Wireless networks have become integral to society as they provide mobility and scalability advantages. However, their disadvantage is that they cannot control the media, which makes them vulnerable to various types of attacks. One example of such attacks is the evil twin access point (AP) attack, in which an authorized AP is impersonated by mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are a major source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it is necessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for evil twin AP detection. However, clock skew is difficult to calculate precisely because wireless networks are vulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-based machine learning classification algorithm. The features used in the proposed method are clock skew, channel, received signal strength, and duration. The results of experiments conducted indicate that the proposed method has an evil twin AP detection accuracy of 100% using the random forest algorithm.

• 정보보호학회논문지
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• 제29권3호
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• pp.557-564
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• 2019

소프트웨어의 증가에 따라 소프트웨어의 취약점도 함께 증가하고 있다. 다양한 소프트웨어는 다수의 취약점이 존재할 수 있으며 취약점을 통해 많은 피해를 받을 수 있기 때문에 빠르게 탐지하여 제거해야 한다. 현재 소프트웨어의 취약점을 발견하기 위해 다양한 연구가 진행되고 있지만, 수행 속도가 느리거나 예측 정확도가 높지 않다. 따라서 본 논문에서는 신경망 알고리즘을 이용하여 소프트웨어의 취약 여부를 효율적으로 예측하는 방법을 제안하며 나아가 기계학습 알고리즘을 이용한 기존의 시스템과 예측 정확도를 비교한다. 실험 결과 본 논문에서 제안하는 예측 시스템이 가장 높은 예측률을 보였다.