network Intrusion detection is an essential aspect of implementing secure networks. network Intrusion detection systems help mitigate network attacks preemptively and let the system take preventive measures. machine L...
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ISBN:
(纸本)9781665477062
network Intrusion detection is an essential aspect of implementing secure networks. network Intrusion detection systems help mitigate network attacks preemptively and let the system take preventive measures. machinelearning techniques enable the system to build attack classification models based on previously available real-world data. Advanced security network Metrics dataset(ASNM) is one such dataset that contains derived network features that help create a highly accurate network attack classifier. In this paper, we pre-process the ASNM dataset and use feature selection techniques to filter out unwanted features and retain features that are highly correlated to the classification in the ASNM dataset. We apply the Variance threshold and Chi-square Test feature selection techniques on the ASNM dataset. Finally, a neural network model based attack classifier is developed and shown to have a prediction accuracy of 99%.
The proceedings contain 171 papers. The topics discussed include: area and delay efficient hybrid prefix adders for residue number system applications;compressor using cadence 180nm for image processing applications;h...
ISBN:
(纸本)9781665462617
The proceedings contain 171 papers. The topics discussed include: area and delay efficient hybrid prefix adders for residue number system applications;compressor using cadence 180nm for image processing applications;hexagonal split ring resonator for 5G applications;implementation of dual band slot antenna for wireless applications;multiband tree shaped microstrip antenna for satellite communication;polarization mismatch in terms of rough surface using radar backscattering;sidelobe suppression of antenna array using harmony search algorithm;semicircular-shaped dual-band MIMO antenna for wideband sub-6 GHz wireless applications;an inverted s-shaped microstrip antenna for 5G millimeter-wave applications;a comparative analysis of the machinelearning model for crop yield prediction in Quezon Province, Philippines;a review of power equipment defect detection based on deep learning;adaptive multi-hop deep learning based drug recommendation system with selective coverage mechanism;an efficient machinelearning classification model for diabetes prediction;analysis and predictions of winning Indian Premier League match using machinelearning algorithm;and artificial intelligence and machinelearning based financial risk network assessment model.
The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in i...
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ISBN:
(纸本)9798350319347;9798350319354
The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machinelearning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios.
The transformation of all-optical networks is an important task for communication operators to meet the needs of the new diversified comprehensive information services. The service experience of users was affected by ...
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network security is the main content of network management, but in the process of network security management, it is vulnerable to hacker intrusion and communication interference, which reduces the level of network se...
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The ICT solutions based on machinelearning (ML) and Internet of Things (IoT) are gaining traction in disaster management systems. They have applications in disaster management, early warning and emergency response, s...
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ISBN:
(纸本)9798350319514
The ICT solutions based on machinelearning (ML) and Internet of Things (IoT) are gaining traction in disaster management systems. They have applications in disaster management, early warning and emergency response, search and rescue missions and many more. All the IoT devices ought to ensure a full proof secured environment. Traditional intrusion detection system (IDS) is unable to detect cyber-attacks. Integrating machinelearning with IoT provides a sense of intelligence to ensure security aspects. Basically, the intelligent-IDS framework depends on training & testing of datasets. Using machinelearning-based security classifiers, it can easily obtain high accuracy, reduce false alarms, identify illegal data packets and provide optimal solutions to detect malicious activities. This paper presents a hybrid deep neural network model to detect intrusion in IoT-network. Several classifiers using CICIDS2017 and IoTID20 datasets are utilised in a simulation environment. IoT using machinelearning and neural networks is a novel approach which feeds data packets to automate the entire network. Data traffic management in IoT networks is properly discussed. More interestingly, the uniqueness of this study provides a detailed analysis of techniques evaluating various parameters/metrics.
In the rapid development of information technology, the speed of communication and communication of network information is getting faster and faster. The depth and breadth of cultural exchanges between countries in th...
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machinelearning is significant fourth industrial revolution technology that may be habitual to defend Internetconnected devices against cyber threats, assaults, damage, or illegal access. Cyber security computing may...
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In this paper we explore the use of machinelearning algorithms to accurately estimate the power factor in power grids. The power factor is crucial for an efficient operation of power grids, and its accurate estimatio...
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ISBN:
(纸本)9798350393156
In this paper we explore the use of machinelearning algorithms to accurately estimate the power factor in power grids. The power factor is crucial for an efficient operation of power grids, and its accurate estimation is a major challenge. The analysis of the problem is performed using machinelearning, Random Forest and Neural networks algorithms. Several characteristics and input variables such as voltage, current and power that can influence the estimation were analyzed. We used real data sets taken over a period of one week to support our results and compare the performance of the proposed algorithms with conventional methods. The results provide a comprehensive view on the effectiveness of machinelearning algorithms in power factor estimation, as well as their practical implications in power grid management.
The proceedings contain 284 papers. The topics discussed include: a dynamic anomaly detection approach for fault detection on fire alarm system based on fuzzy-PSO-CNN approach;enhanced skin cancer prediction with anal...
ISBN:
(纸本)9798350313987
The proceedings contain 284 papers. The topics discussed include: a dynamic anomaly detection approach for fault detection on fire alarm system based on fuzzy-PSO-CNN approach;enhanced skin cancer prediction with analysis using machinelearning algorithms;comparison study of different neural network models for assessing employability skills of IT graduates;performance evaluation of machinelearning algorithms for prediction of cardiac failure;design of improvised DCM-based tunable true random number generator;design and implementation of piezoelectric shoe;prediction and processing of environment geography images using deep learning techniques;human activity recognition in video surveillance using long-term recurrent convolutional network;and energy-efficient task offloading for edge computing-based smart grid networks using human urbanization.
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