Skip to main content

Document details - Analysis and Classification of Intrusion Detection for Synthetic Neural Networks using Machine Language Strategies

Journal Volume 9, Issue 5, September - October 2020, Article 9582182 Manjunath H , Dr S Saravana kumar , " Analysis and Classification of Intrusion Detection for Synthetic Neural Networks using Machine Language Strategies " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 9, Issue 5, September - October 2020 , pp. 005-009 , ISSN 2278 - 6856.

Analysis and Classification of Intrusion Detection for Synthetic Neural Networks using Machine Language Strategies

    Manjunath H , Dr S Saravana kumar

Abstract

Abstract In the recent yearsdue to the increased throughput and the multi-uniformity of behaviors, the existing network is complex. An intrusion detection system is a critical component for reliable management of information. The advancement in system hardware field and raise in data growth related in new fields with respect to the deep learning technology along with intrusion detection systems. Learning data presentation studied through deep learning which a part of Machine learning. To be successful, network intrusion detection systems, which are part of the layered protection scheme, must be able to fulfill certain organizational objectives. This research paper describes the investigations performed on various neural network architectures using a variety of intrusion detection algorithms. New supervised algorithms in Intrusion Detection System (IDS) have been implemented that have faster convergence and better performance. The goal of this research work is to introduce a new balance of artificial neural networks. Keywords: -Denial of Service, Artificial neural network, Malicious, Intrusion detection system.

  • ISSN: 22786856
  • Source Type: Journal
  • Original language: English

Cited by 0 documents

Related documents

{"topic":{"name":"Order Picking; AS/RS; Warehouses","id":5729,"uri":"Topic/5729","prominencePercentile":98.30173,"prominencePercentileString":"98.302","overallScholarlyOutput":0},"dig":"7972b85ca5bc948c1a2f0423f8150b186ec6bb8cf32afac11c4a324b8d78fb11"}