Computational Intelligent Techniques To Detect DDOS Attacks : A  Survey

Distributed Denial of Service attacks are significant dangers these days over web applications and web administrations. These assaults pushing ahead towards application layer to procure furthermore, squander most extreme CPU cycles. By asking for assets from web benefits in gigantic sum utilizing quick fire of solicitations, assailant robotized programs use all the capacity of handling of single server application or circulated environment application. The periods of the plan execution is client conduct checking and identification. In to beginning with stage by social affair the data of client conduct and computing individual user’s trust score will happen and Entropy of a similar client will be ascertained. HTTP Unbearable Load King (HULK) attacks are also evaluated. In light of first stage, in recognition stage, variety in entropy will be watched and malevolent clients will be recognized. Rate limiter is additionally acquainted with stop or downsize serving the noxious clients. This paper introduces the FAÇADE layer for discovery also, hindering the unapproved client from assaulting the framework.

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Detection and Prevention of DDoS Attacks in WSN using Artificial Neural Network

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i7.562566 ◽

2018 ◽

Vol 6 (7) ◽

pp. 562-566

Author(s):

Sumanjit Kaur ◽

Mohit Marwaha ◽

Guresh Pal Singh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ddos Attacks ◽

Artificial Neural

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DETECTION OF ANOMALY BASED APPLICATION LAYER DDoS ATTACKS USING MACHINE LEARNING APPROACHES

i-manager s Journal on Computer Science ◽

10.26634/jcom.4.2.8120 ◽

2016 ◽

Vol 4 (2) ◽

pp. 6

Author(s):

VANI NIDHI M.S.P.S. ◽

PRASAD K. MUNIVARA ◽

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Keyword(s):

Machine Learning ◽

Learning Approaches ◽

Ddos Attacks ◽

Application Layer

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Detection Approach of DDoS Attacks Based on Conditional Random Fields

Journal of Software ◽

10.3724/sp.j.1001.2011.03960 ◽

2011 ◽

Vol 22 (8) ◽

pp. 1897-1910 ◽

Cited By ~ 12

Author(s):

Yun LIU ◽

Zhi-Ping CAI ◽

Ping ZHONG ◽

Jian-Ping YIN ◽

Jie-Ren CHENG

Keyword(s):

Random Fields ◽

Conditional Random Fields ◽

Ddos Attacks ◽

Detection Approach

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Modified method of detecting DDoS attacks based on entropy

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.01778 ◽

2010 ◽

Vol 30 (7) ◽

pp. 1778-1781 ◽

Cited By ~ 1

Author(s):

Jie ZHANG ◽

Zheng QIN

Keyword(s):

Ddos Attacks ◽

Modified Method

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The inhibition model of DDoS attacks in SDN networks

JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING ◽

10.3724/sp.j.1249.2017.06562 ◽

2017 ◽

Vol 34 (6) ◽

pp. 562

Author(s):

Qiao Yan ◽

Qingxiang Gong ◽

Fei Yu

Keyword(s):

Ddos Attacks ◽

Inhibition Model

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Intelligent Techniques Analysis for Glycosylation Site Prediction

Current Bioinformatics ◽

10.2174/1574893615666210108094847 ◽

2021 ◽

Vol 15 ◽

Author(s):

Alhassan Alkuhlani ◽

Walaa Gad ◽

Mohamed Roushdy ◽

Abdel-Badeeh M. Salem

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Cell Interaction ◽

Glycosylation Site ◽

Machine Learning Classification ◽

Site Prediction ◽

Glycosylation Sites ◽

Wide Range ◽

Feature Extraction And Selection ◽

Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

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