{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T22:38:17Z","timestamp":1740177497691,"version":"3.37.3"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"name":"India. SERB","award":["SRG\/2021\/000066"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Priv. Secur."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"\n Network traffic classification has many applications, such as security monitoring, quality of service, traffic engineering, and so on. For the aforementioned applications, Deep Packet Inspection (DPI) is a popularly used technique for traffic classification because it scrutinizes the payload and provides comprehensive information for accurate analysis of network traffic. However, DPI-based methods reduce network performance because they are computationally expensive and hinder end-user privacy as they analyze the payload. To overcome these challenges, bit-level signatures are significantly used to perform network traffic classification. However, most of these methods still need to improve performance as they perform one-by-one signature matching of unknown payloads with application signatures for classification. Moreover, these methods become stagnant with the increase in application signatures. Therefore, to fill this gap, we propose\n OptiClass<\/jats:italic>\n , an optimized classifier for application protocols using bit-level signatures.\n OptiClass<\/jats:italic>\n performs parallel application signature matching with unknown flows, which results in faster, more accurate, and more efficient network traffic classification.\n OptiClass<\/jats:italic>\n achieves twofold performance gains compared to the state-of-the-art methods. First,\n OptiClass<\/jats:italic>\n generates bit-level signatures of just 32 bits for all the applications. This keeps\n OptiClass<\/jats:italic>\n swift and privacy-preserving. Second,\n OptiClass<\/jats:italic>\n uses a novel data structure called\n BiTSPLITTER<\/jats:italic>\n for signature matching for fast and accurate classification. We evaluated the performance of\n OptiClass<\/jats:italic>\n on three datasets consisting of twenty application protocols. Experimental results report that\n OptiClass<\/jats:italic>\n has an average recall, precision, and F1-score of 97.36%, 97.38%, and 97.37%, respectively, and an average classification speed of 9.08 times faster than five closely related state-of-the-art methods.\n <\/jats:p>","DOI":"10.1145\/3633777","type":"journal-article","created":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T12:42:15Z","timestamp":1700656935000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["OptiClass<\/i>\n : An Optimized Classifier for Application Layer Protocols Using Bit Level Signatures"],"prefix":"10.1145","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7351-0341","authenticated-orcid":false,"given":"Mayank","family":"Swarnkar","sequence":"first","affiliation":[{"name":"Indian Institute of Technology (BHU) Varanasi, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5620-4465","authenticated-orcid":false,"given":"Neha","family":"Sharma","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (BHU) Varanasi, India"}]}],"member":"320","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"[n.d.]. https:\/\/en.wikipedia.org\/wiki\/EDonkey2000. 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