Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/30669
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahmed, Sheeraz | - |
dc.contributor.author | Khan, Zahoor Ali | - |
dc.contributor.author | Mohsin, Syed Muhammad | - |
dc.contributor.author | Latif, Shahid | - |
dc.contributor.author | Aslam, Sheraz | - |
dc.contributor.author | Mujlid, Hana | - |
dc.contributor.author | Adil, Muhammad | - |
dc.contributor.author | Najam, Zeeshan | - |
dc.date.accessioned | 2023-10-19T10:50:44Z | - |
dc.date.available | 2023-10-19T10:50:44Z | - |
dc.date.issued | 2023-02-01 | - |
dc.identifier.citation | Future Internet, 2023, vol. 15, iss. 2 | en_US |
dc.identifier.issn | 19995903 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/30669 | - |
dc.description.abstract | Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naïve Bayes, Decision Stump, Logistic Model Tree, Naïve Bayes Updateable, Naïve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Future Internet | en_US |
dc.rights | © by the authors | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | attack | en_US |
dc.subject | attack detection | en_US |
dc.subject | botnet | en_US |
dc.subject | DDoS attack | en_US |
dc.subject | MLP classifier | en_US |
dc.title | Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron | en_US |
dc.type | Article | en_US |
dc.collaboration | Iqra National University | en_US |
dc.collaboration | Higher Colleges of Technology | en_US |
dc.collaboration | COMSATS University Islamabad | en_US |
dc.collaboration | Virtual University of Pakistan | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Ctl Eurocollege | en_US |
dc.collaboration | Taif University | en_US |
dc.collaboration | Ultimate Engineering Consultants Private Limited | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.journals | Open Access | en_US |
dc.country | Pakistan | en_US |
dc.country | United Arab Emirates | en_US |
dc.country | Cyprus | en_US |
dc.country | Saudi Arabia | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.3390/fi15020076 | en_US |
dc.identifier.scopus | 2-s2.0-85148889612 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85148889612 | - |
dc.relation.issue | 2 | en_US |
dc.relation.volume | 15 | en_US |
cut.common.academicyear | 2022-2023 | en_US |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 1999-5903 | - |
crisitem.journal.publisher | MDPI | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0003-4305-0908 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
futureinternet-15-00076-v3 (4).pdf | Full text | 1.02 MB | Adobe PDF | View/Open |
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