Multi layer multi objective extreme learning machine

Lekamalage, Chamara Kasun Liyanaarachchi; Song, Kyung‐Sik; Huang, Guang-Bin; Cui, Dongshun; Liang, Ken

doi:10.1109/icip.2017.8296491

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…where W in ∈ R N R ×N U is the input weight table,Ŵ (l) ∈ R N R ×N R is the recurrent weight table for level l, W (l) ∈ R N R ×N R is the table relative to the connection weights between the levels from level l-1 to level l, a (l) is the leaky parameter at level l, and tanh represents the elementary application of the tangent [55][56][57][58].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

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Section: Experiments and Resultsmentioning

confidence: 99%

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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“…e random weights generate approximately rectangular and weakly correlated features at the hidden layer which offers an accurate solution and high generalization abilities. More specifically, the output of the proposed S-hFFNN with random hidden neurons in the hidden layer can be represented as follows [26][27][28]:…”

Section: The Proposed Systemmentioning

confidence: 99%

Online-Semisupervised Neural Anomaly Detector to Identify MQTT-Based Attacks in Real Time

Gao¹,

Cao²,

Wang³

et al. 2021

Security and Communication Networks

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Industry 4.0 focuses on continuous interconnection services, allowing for the continuous and uninterrupted exchange of signals or information between related parties. The application of messaging protocols for transferring data to remote locations must meet specific specifications such as asynchronous communication, compact messaging, operating in conditions of unstable connection of the transmission line of data, limited network bandwidth operation, support multilevel Quality of Service (QoS), and easy integration of new devices. The Message Queue Telemetry Transport (MQTT) protocol is used in software applications that require asynchronous communication. It is a light and simplified protocol based on publish-subscribe messaging and is placed functionally over the TCP/IP protocol. It is designed to minimize the required communication bandwidth and system requirements increasing reliability and probability of successful message transmission, making it ideal for use in Machine-to-Machine (M2M) communication or networks where bandwidth is limited, delays are long, coverage is not reliable, and energy consumption should be as low as possible. Despite the fact that the advantage that MQTT offers its way of operating does not provide a serious level of security in how to achieve its interconnection, as it does not require protocol dependence on one intermediate third entity, the interface is dependent on each application. This paper presents an innovative real-time anomaly detection system to detect MQTT-based attacks in cyber-physical systems. This is an online-semisupervised learning neural system based on a small number of sampled patterns that identify crowd anomalies in the MQTT protocol related to specialized attacks to undermine cyber-physical systems.

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“…For example, in the study by Esfe et al (2015) in predicting thermal conductivity, they found that a network with two hidden layers and five neurons in each layer had the lowest error and highest fitting coefficient for 30 datasets. Moreover, in the study by Lekamalage, Song, Huang, Cui, and Liang (2017) in experimenting with the image classification of 24300 samples, there were two hidden layers with the first hidden node consisting of 200 nodes, while the second hidden layer which consisted of 3000 nodes was found to have better testing accuracy. Besides, in a study by Zhang (2017) in recognizing and predicting mental disease based on 10000 datasets, the best mean square error value was recorded when the number of hidden nodes increased from eight to 16 nodes.…”

Section: Literature Reviewmentioning

confidence: 99%

Variation on the Number of Hidden Nodes Through Multilayer Perceptron Networks to Predict the Cycle Time

Ahmarofi

Ramli

Abidin

et al. 2019

Journal of Information and Communication Technology

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Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line.

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Multi layer multi objective extreme learning machine

Cited by 8 publications

References 12 publications

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Online-Semisupervised Neural Anomaly Detector to Identify MQTT-Based Attacks in Real Time

Variation on the Number of Hidden Nodes Through Multilayer Perceptron Networks to Predict the Cycle Time

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