Performance Comparison of Intrusion Detection System Between Deep Belief Network (DBN)Algorithm and State Preserving Extreme Learning Machine (SPELM) Algorithm

Singh, Kunal; Mathai, K. James

doi:10.1109/icecct.2019.8869492

Cited by 12 publications

(4 citation statements)

References 3 publications

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“…Using effective cluster analysis in big data sets, a new methodology for DDOS assault detection was proposed in 2017. It used the DARPA 2000, CAIDA2007, and CAIDA2008 datasets [14]. In 2017, a model was created using distributed machine learning, ISCX-2012, linear and sigmoid kernel functions to protect a smart gateway network against intrusion [15].…”

Section: Related Workmentioning

confidence: 99%

Intrusion detection using decision tree classifier with feature reduction technique

Shirazi

Shamim

Khan

et al. 2023

Mehran Univ. res. j. eng. technol.

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The number of internet users and network services is increasing rapidly in the recent decade gradually. A Large volume of data is produced and transmitted over the network. Number of security threats to the network has also been increased. Although there are many machine learning approaches and methods are used in intrusion detection systems to detect the attacks, but generally they are not efficient for large datasets and real time detection. Machine learning classifiers using all features of datasets minimized the accuracy of detection for classifier. A reduced feature selection technique that selects the most relevant features to detect the attack with ML approach has been used to obtain higher accuracy. In this paper, we used recursive feature elimination technique and selected more relevant features with machine learning approaches for big data to meet the challenge of detecting the attack. We applied this technique and classifier to NSL KDD dataset. Results showed that selecting all features for detection can maximize the complexity in the context of large data and performance of classifier can be increased by feature selection best in terms of efficiency and accuracy.

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Section: Related Workmentioning

confidence: 99%

Intrusion detection using decision tree classifier with feature reduction technique

Shirazi

Shamim

Khan

et al. 2023

Mehran Univ. res. j. eng. technol.

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“…[223], the authors undertook a thorough study of the application of deep belief networks and other deep learning methods in cybersecurity. Similarly, the authors in [224] compared the overall performance of the deep belief network to that of a state-preserving extreme learning machine method using the NSL-KDD dataset for face recognition, pedestrian detection, and intrusion detection. In Ref.…”

Section: Deep Belief Networkmentioning

confidence: 99%

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Molokomme

Onumanyi

Abu‐Mahfouz

2022

JSAN

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The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs.

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“…Figure 4 shown main of steps of IDS-DBN [24], [25]. The First, define of number of dataset of result preprocessing dataset become two data training and data testing.…”

Section: Ids-dbnmentioning

confidence: 99%

Intrusion detection with deep learning on internet of things heterogeneous network

Sharipuddin

Purnama

Kurniabudi

et al. 2021

IJ-AI

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The difficulty of the intrusion detection system in heterogeneous networks is significantly affected by devices, protocols, and services, thus the network becomes complex and difficult to identify. Deep learning is one algorithm that can classify data with high accuracy. In this research, we proposed deep learning to intrusion detection system identification methods in heterogeneous networks to increase detection accuracy. In this paper, we provide an overview of the proposed algorithm, with an initial experiment of denial of services (DoS) attacks and results. The results of the evaluation showed that deep learning can improve detection accuracy in the heterogeneous internet of things (IoT).

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Performance Comparison of Intrusion Detection System Between Deep Belief Network (DBN)Algorithm and State Preserving Extreme Learning Machine (SPELM) Algorithm

Cited by 12 publications

References 3 publications

Intrusion detection using decision tree classifier with feature reduction technique

Intrusion detection using decision tree classifier with feature reduction technique

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Intrusion detection with deep learning on internet of things heterogeneous network

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