Pre-filters in-transit malware packets detection in the network

Many challenges have appeared, one of these challenges is to prevent the spread of malware through the Internet, which is frequently enhanced over the years. [...]accurate and efficient detection systems became an absolute need to recognize the malware assisted attacks that occur in the network and...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Telkomnika 2019-08, Vol.17 (4), p.1706-1714
Hauptverfasser: Khammas, Ban Mohammed, Ismail, Ismahani, Marsono, M. N.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1714
container_issue 4
container_start_page 1706
container_title Telkomnika
container_volume 17
creator Khammas, Ban Mohammed
Ismail, Ismahani
Marsono, M. N.
description Many challenges have appeared, one of these challenges is to prevent the spread of malware through the Internet, which is frequently enhanced over the years. [...]accurate and efficient detection systems became an absolute need to recognize the malware assisted attacks that occur in the network and computer system. According to Symantec report in 2016, malware that spread currently in the networks is highly mutated and continuously updating them in order to avoid conventional detection systems [1]. The second group enclosed to 911 files which are generated using NGVCK kit [36] and VX Heavens website and used the same configuration setting where the total number of metamorphic files is 1020. Since the proposed method need as much as possible malware packets therefore, the captured traffic traces are obtained from the academic network and student network and it captured for one week. [...]part, the results of the proposed packet-level malware detection are compared with that of [12, 13] to measure the speedup of the proposed technique as compared to the case when all packets are subjected to ML classification.
doi_str_mv 10.12928/telkomnika.v17i4.12065
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2237496680</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2237496680</sourcerecordid><originalsourceid>FETCH-LOGICAL-c200t-95dd739ceaa2d8497e7353a2614e6bfaee183ef1c1f61f31ba30afdef4e5978f3</originalsourceid><addsrcrecordid>eNpFkNtKAzEQhoMouNQ-gwtep-awm2yuRIonKOiFXofp7gTTPdUktfj2LlvBgWFg-Ph_-Ai55mzFhRHVbcKuHfvBt7D65toX05up8oxkQjJBjTDynGRcGUmnZZdkGeOOTaOZKE2Vkbu3gNT5LmGIuR9oCjBEn_IeuiMEzPdQt5hi3mDCOvlxmKA8fWI-YDqOob0iFw66iMu_uyAfjw_v62e6eX16Wd9vaC0YS9SUTaOlqRFANFVhNGpZShCKF6i2DhB5JdHxmjvFneRbkAxcg67A0ujKyQW5OeXuw_h1wJjsbjyEYaq0QkhdGKUqNlH6RNVhjDGgs_vgewg_ljM7C7P_wuwszM7C5C8tF2NO</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2237496680</pqid></control><display><type>article</type><title>Pre-filters in-transit malware packets detection in the network</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Khammas, Ban Mohammed ; Ismail, Ismahani ; Marsono, M. N.</creator><creatorcontrib>Khammas, Ban Mohammed ; Ismail, Ismahani ; Marsono, M. N.</creatorcontrib><description>Many challenges have appeared, one of these challenges is to prevent the spread of malware through the Internet, which is frequently enhanced over the years. [...]accurate and efficient detection systems became an absolute need to recognize the malware assisted attacks that occur in the network and computer system. According to Symantec report in 2016, malware that spread currently in the networks is highly mutated and continuously updating them in order to avoid conventional detection systems [1]. The second group enclosed to 911 files which are generated using NGVCK kit [36] and VX Heavens website and used the same configuration setting where the total number of metamorphic files is 1020. Since the proposed method need as much as possible malware packets therefore, the captured traffic traces are obtained from the academic network and student network and it captured for one week. [...]part, the results of the proposed packet-level malware detection are compared with that of [12, 13] to measure the speedup of the proposed technique as compared to the case when all packets are subjected to ML classification.</description><identifier>ISSN: 1693-6930</identifier><identifier>EISSN: 2302-9293</identifier><identifier>DOI: 10.12928/telkomnika.v17i4.12065</identifier><language>eng</language><publisher>Yogyakarta: Ahmad Dahlan University</publisher><subject>Algorithms ; Artificial intelligence ; Classification ; Computer networks ; International conferences ; Malware ; Neural networks ; Signatures ; Websites</subject><ispartof>Telkomnika, 2019-08, Vol.17 (4), p.1706-1714</ispartof><rights>2019. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c200t-95dd739ceaa2d8497e7353a2614e6bfaee183ef1c1f61f31ba30afdef4e5978f3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Khammas, Ban Mohammed</creatorcontrib><creatorcontrib>Ismail, Ismahani</creatorcontrib><creatorcontrib>Marsono, M. N.</creatorcontrib><title>Pre-filters in-transit malware packets detection in the network</title><title>Telkomnika</title><description>Many challenges have appeared, one of these challenges is to prevent the spread of malware through the Internet, which is frequently enhanced over the years. [...]accurate and efficient detection systems became an absolute need to recognize the malware assisted attacks that occur in the network and computer system. According to Symantec report in 2016, malware that spread currently in the networks is highly mutated and continuously updating them in order to avoid conventional detection systems [1]. The second group enclosed to 911 files which are generated using NGVCK kit [36] and VX Heavens website and used the same configuration setting where the total number of metamorphic files is 1020. Since the proposed method need as much as possible malware packets therefore, the captured traffic traces are obtained from the academic network and student network and it captured for one week. [...]part, the results of the proposed packet-level malware detection are compared with that of [12, 13] to measure the speedup of the proposed technique as compared to the case when all packets are subjected to ML classification.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Classification</subject><subject>Computer networks</subject><subject>International conferences</subject><subject>Malware</subject><subject>Neural networks</subject><subject>Signatures</subject><subject>Websites</subject><issn>1693-6930</issn><issn>2302-9293</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpFkNtKAzEQhoMouNQ-gwtep-awm2yuRIonKOiFXofp7gTTPdUktfj2LlvBgWFg-Ph_-Ai55mzFhRHVbcKuHfvBt7D65toX05up8oxkQjJBjTDynGRcGUmnZZdkGeOOTaOZKE2Vkbu3gNT5LmGIuR9oCjBEn_IeuiMEzPdQt5hi3mDCOvlxmKA8fWI-YDqOob0iFw66iMu_uyAfjw_v62e6eX16Wd9vaC0YS9SUTaOlqRFANFVhNGpZShCKF6i2DhB5JdHxmjvFneRbkAxcg67A0ujKyQW5OeXuw_h1wJjsbjyEYaq0QkhdGKUqNlH6RNVhjDGgs_vgewg_ljM7C7P_wuwszM7C5C8tF2NO</recordid><startdate>20190801</startdate><enddate>20190801</enddate><creator>Khammas, Ban Mohammed</creator><creator>Ismail, Ismahani</creator><creator>Marsono, M. N.</creator><general>Ahmad Dahlan University</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BVBZV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20190801</creationdate><title>Pre-filters in-transit malware packets detection in the network</title><author>Khammas, Ban Mohammed ; Ismail, Ismahani ; Marsono, M. N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c200t-95dd739ceaa2d8497e7353a2614e6bfaee183ef1c1f61f31ba30afdef4e5978f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Classification</topic><topic>Computer networks</topic><topic>International conferences</topic><topic>Malware</topic><topic>Neural networks</topic><topic>Signatures</topic><topic>Websites</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khammas, Ban Mohammed</creatorcontrib><creatorcontrib>Ismail, Ismahani</creatorcontrib><creatorcontrib>Marsono, M. N.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>East &amp; South Asia Database</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Telkomnika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khammas, Ban Mohammed</au><au>Ismail, Ismahani</au><au>Marsono, M. N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pre-filters in-transit malware packets detection in the network</atitle><jtitle>Telkomnika</jtitle><date>2019-08-01</date><risdate>2019</risdate><volume>17</volume><issue>4</issue><spage>1706</spage><epage>1714</epage><pages>1706-1714</pages><issn>1693-6930</issn><eissn>2302-9293</eissn><abstract>Many challenges have appeared, one of these challenges is to prevent the spread of malware through the Internet, which is frequently enhanced over the years. [...]accurate and efficient detection systems became an absolute need to recognize the malware assisted attacks that occur in the network and computer system. According to Symantec report in 2016, malware that spread currently in the networks is highly mutated and continuously updating them in order to avoid conventional detection systems [1]. The second group enclosed to 911 files which are generated using NGVCK kit [36] and VX Heavens website and used the same configuration setting where the total number of metamorphic files is 1020. Since the proposed method need as much as possible malware packets therefore, the captured traffic traces are obtained from the academic network and student network and it captured for one week. [...]part, the results of the proposed packet-level malware detection are compared with that of [12, 13] to measure the speedup of the proposed technique as compared to the case when all packets are subjected to ML classification.</abstract><cop>Yogyakarta</cop><pub>Ahmad Dahlan University</pub><doi>10.12928/telkomnika.v17i4.12065</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1693-6930
ispartof Telkomnika, 2019-08, Vol.17 (4), p.1706-1714
issn 1693-6930
2302-9293
language eng
recordid cdi_proquest_journals_2237496680
source EZB-FREE-00999 freely available EZB journals
subjects Algorithms
Artificial intelligence
Classification
Computer networks
International conferences
Malware
Neural networks
Signatures
Websites
title Pre-filters in-transit malware packets detection in the network
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T20%3A35%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Pre-filters%20in-transit%20malware%20packets%20detection%20in%20the%20network&rft.jtitle=Telkomnika&rft.au=Khammas,%20Ban%20Mohammed&rft.date=2019-08-01&rft.volume=17&rft.issue=4&rft.spage=1706&rft.epage=1714&rft.pages=1706-1714&rft.issn=1693-6930&rft.eissn=2302-9293&rft_id=info:doi/10.12928/telkomnika.v17i4.12065&rft_dat=%3Cproquest_cross%3E2237496680%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2237496680&rft_id=info:pmid/&rfr_iscdi=true