G.3.7. Database Machines
Abdul Aziz Danaa Abukari; Mohammed Daabo Ibrahim; Alhassan Abdul-Barik
Abstract
Hidden Markov Models (HMMs) are machine learning models that has been applied to a range of real-life applications including intrusion detection, pattern recognition, thermodynamics, statistical mechanics among others. A multi-layered HMMs for real-time fraud detection and prevention whilst reducing ...
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Hidden Markov Models (HMMs) are machine learning models that has been applied to a range of real-life applications including intrusion detection, pattern recognition, thermodynamics, statistical mechanics among others. A multi-layered HMMs for real-time fraud detection and prevention whilst reducing drastically the number of false positives and negatives is proposed and implemented in this study. The study also focused on reducing the parameter optimization and detection times of the proposed models using a hybrid algorithm comprising the Baum-Welch, Genetic and Particle-Swarm Optimization algorithms. Simulation results revealed that, in terms of Precision, Recall and F1-scores, our proposed model performed better when compared to other approaches proposed in literature.
B.3. Communication/Networking and Information Technology
S. Mojtaba Matinkhah; Roya Morshedi; Akbar Mostafavi
Abstract
The Internet of Things (IoT) has emerged as a rapidly growing technology that enables seamless connectivity between a wide variety of devices. However, with this increased connectivity comes an increased risk of cyber-attacks. In recent years, the development of intrusion detection systems (IDS) has ...
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The Internet of Things (IoT) has emerged as a rapidly growing technology that enables seamless connectivity between a wide variety of devices. However, with this increased connectivity comes an increased risk of cyber-attacks. In recent years, the development of intrusion detection systems (IDS) has become critical for ensuring the security and privacy of IoT networks. This article presents a study that evaluates the accuracy of an intrusion detection system (IDS) for detecting network attacks in the Internet of Things (IoT) network. The proposed IDS uses the Decision Tree Classifier and is tested on four benchmark datasets: NSL-KDD, BOT-IoT, CICIDS2017, and MQTT-IoT. The impact of noise on the training and test datasets on classification accuracy is analyzed. The results indicate that clean data has the highest accuracy, while noisy datasets significantly reduce accuracy. Furthermore, the study finds that when both training and test datasets are noisy, the accuracy of classification decreases further. The findings of this study demonstrate the importance of using clean data for training and testing an IDS in IoT networks to achieve accurate classification. This research provides valuable insights for the development of a robust and accurate IDS for IoT networks.
H.3. Artificial Intelligence
Seyed Alireza Bashiri Mosavi; Omid Khalaf Beigi
Abstract
A speedy and accurate transient stability assessment (TSA) is gained by employing efficient machine learning- and statistics-based (MLST) algorithms on transient nonlinear time series space. In the MLST’s world, the feature selection process by forming compacted optimal transient feature space ...
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A speedy and accurate transient stability assessment (TSA) is gained by employing efficient machine learning- and statistics-based (MLST) algorithms on transient nonlinear time series space. In the MLST’s world, the feature selection process by forming compacted optimal transient feature space (COTFS) from raw high dimensional transient data can pave the way for high-performance TSA. Hence, designing a comprehensive feature selection scheme (FSS) that populates COTFS with the relevant-discriminative transient features (RDTFs) is an urgent need. This work aims to introduce twin hybrid FSS (THFSS) to select RDTFs from transient 28-variate time series data. Each fold of THFSS comprises filter-wrapper mechanisms. The conditional relevancy rate (CRR) is based on mutual information (MI) and entropy calculations are considered as the filter method, and incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr) formed by kernelized support vector machine (SVM) and twin SVM (TWSVM) are used as wrapper ones. After exerting THFSS on transient univariates, RDTFs are entered into the cross-validation-based train-test procedure for evaluating their efficiency in TSA. The results manifested that THFSS-based RDTFs have a prediction accuracy of 98.87 % and a processing time of 102.653 milliseconds for TSA.
H.6. Pattern Recognition
Sadegh Rahmani Rahmani-Boldaji; Mehdi Bateni; Mahmood Mortazavi Dehkordi
Abstract
Efficient regular-frequent pattern mining from sensors-produced data has become a challenge. The large volume of data leads to prolonged runtime, thus delaying vital predictions and decision makings which need an immediate response. So, using big data platforms and parallel algorithms is an appropriate ...
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Efficient regular-frequent pattern mining from sensors-produced data has become a challenge. The large volume of data leads to prolonged runtime, thus delaying vital predictions and decision makings which need an immediate response. So, using big data platforms and parallel algorithms is an appropriate solution. Additionally, an incremental technique is more suitable to mine patterns from big data streams than static methods. This study presents an incremental parallel approach and compact tree structure for extracting regular-frequent patterns from the data of wireless sensor networks. Furthermore, fewer database scans have been performed in an effort to reduce the mining runtime. This study was performed on Intel 5-day and 10-day datasets with 6, 4, and 2 nodes clusters. The findings show the runtime was improved in all 3 cluster modes by 14, 18, and 34% for the 5-day dataset and by 22, 55, and 85% for the 10-day dataset, respectively.
R. Azizi; A. M. Latif
Abstract
In this work, we show that an image reconstruction from a burst of individually demosaicked RAW captures propagates demosaicking artifacts throughout the image processing pipeline. Hence, we propose a joint regularization scheme for burst denoising and demosaicking. We model the burst alignment functions ...
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In this work, we show that an image reconstruction from a burst of individually demosaicked RAW captures propagates demosaicking artifacts throughout the image processing pipeline. Hence, we propose a joint regularization scheme for burst denoising and demosaicking. We model the burst alignment functions and the color filter array sampling functions into one linear operator. Then, we formulate the individual burst reconstruction and the demosaicking problems into a three-color-channel optimization problem. We introduce a crosschannel prior to the solution of this optimization problem and develop a numerical solver via alternating direction method of multipliers. Moreover, our proposed method avoids the complexity of alignment estimation as a preprocessing step for burst reconstruction. It relies on a phase correlation approach in the Fourier’s domain to efficiently find the relative translation, rotation, and scale among the burst captures and to perform warping accordingly. As a result of these steps, the proposed joint burst denoising and demosaicking solution improves the quality of reconstructed images by a considerable margin compared to existing image model-based methods.
