[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126096-en":3,"doc-seo-126096-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126096,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning-based anti-jamming technique at the physical layer","Wireless services that exchange critical data must be protected against attacks that target confidentiality, integrity, and availability. Jamming is a major threat that can prevent reliable decoding, with both intentional and unintentional sources disrupting legitimate transmissions. This paper presents a machine learning-based anti-jamming framework operating at the physical layer, using directive antennas and a spatially dynamic, instantaneous response. A receiver dataset deployable on massive-MIMO hardware is evaluated under severe jamming.","Received: 27 October 2022 Revised: 14 January 2023 Accepted: 25 January 2023  \nDOI: 10.1002/cpe.7649  \nRESEARCH ARTICLE  \nMachine learning-based anti-jamming technique atthe physical layer  \nMahdi Chehimi1  Mohamad KhattarAwad2  Mohammed Al-Husseini3  Ali Chehab4  \n1 Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, Virginia, USA  \n2 Department of Computer Engineering, College of Engineering and Petroleum, Kuwait University, Kuwait City, Kuwait  \n3 Beirut Research and Innovaion Center, Lebanese Center for Studies and Research, Beirut, Lebanon  \n4 Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon  \nCorrespondence  \nMohamad KhattarAwad, Department of Computer Engineering, College of Engineering and Petroleum, Kuwait University, Kuwait City, Kuwait.  \n[Email:](Email: mohamad@ieee.org)[ mohamad@ieee.org](Email: mohamad@ieee.org)  \nFunding information  \nKuwait University Research Grant No. EO-02/20  \nAbstract  \nThe reliance on wireless services to exchange critical data is associated with various threats and attacks, which must be mitigated to ensure integrity and security of those wireless services. Posing a serious challenge to wireless systems, jamming is among these attacks. In order to mitigate jamming, directive antennas are used to minimize the signals that are received from the jammer, while maximizing the received legitimate signal from the authorized transmitter. In this paper, we propose a machine learning-based anti-jamming framework to provide a spatially dynamic and instantaneous anti-jamming performance that is achieved at the physical layer. The proposed framework incorporates a dataset that can be deployed in the hardware of a receiver with a massive Multiple-Inputs Multiple-Outputs–(MIMO) antenna. Our extensive performance evaluation results demonstrate the effective performance of the proposed framework in preserving integrity of a massive–MIMO communication system despite the presence of a hostile jammer. Particularly, due to the tabular nature of the generated dataset, tree-based random forest models achieved the best performance with a signal-to-interference-plus-noise ratio accuracy of 92% and fast anti-jamming response in around 1.66 s under sever jamming conditions.  \nKEYWORDS  \nanti-jamming, machine learning, physical layer security  \n1  INTRODUCTION  \nThe current and emerging future wireless networks include deployment of a massive number of sensors and wearable devices, which demands large capacity connectivity anywhere and anytime.1 Technologies like massive Multiple-Inputs Multiple-Outputs–(MIMO) communication, were developed to supply wireless networks with the required channel capacity.2 The availability of status-related performance metrics and control-ability of various system parameters in wireless networks motivated the application of machine learning (ML) techniques toward optimizing the performance of wireless networks.3 The increased reliance on wireless services in our daily activities raises security and privacy concerns. In particular, the wireless channels are vulnerable to various forms of attacks due to their open nature, which include spoofing,4 eavesdropping,5 and jamming6 attacks. Such attacks make the data transmission process extremely difficult and motivate the design of advanced countermeasures in order to guarantee secure data transmission.  \nDue to its unique features, like being easy to launch and extremely difficult to thwart, jamming attacks present a fundamental challenge to any wireless system. Jamming makes wireless systems incapable of performing secure and successful communications.7 Infact, jamming attacks represent a key example of Denial of Service attacks on wireless communications.8 There are two types of jamming attacks. First, intentional attacks from an adversary agent that sends noise signals toward the receiver under attack to prevent it from decoding received signals.9 Second, u","cbCaikDCyijTQ55h","https://ap.wps.com/l/cbCaikDCyijTQ55h","pdf",1745090,5,1,19,"English","en",105,"# Introduction\n## Prior art","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses jamming attacks that disrupt wireless communications, making secure and successful data transmission difficult.\"},{\"question\":\"How does the proposed method improve anti-jamming performance?\",\"answer\":\"It uses a machine learning-based physical-layer framework with spatially dynamic and instantaneous anti-jamming, supported by directive antennas and a deployable dataset.\"},{\"question\":\"What model type and performance are reported?\",\"answer\":\"Tabular dataset–based tree models, especially random forest, achieve the best results with about 92% signal-to-interference-plus-noise ratio accuracy and an anti-jamming response around 1.66 s under severe jamming.\"}]","Machine learning-based anti-jamming technique at the physical layer | 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problem does the document address?","Question",{"text":77,"@type":78},"It addresses jamming attacks that disrupt wireless communications, making secure and successful data transmission difficult.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the proposed method improve anti-jamming performance?",{"text":82,"@type":78},"It uses a machine learning-based physical-layer framework with spatially dynamic and instantaneous anti-jamming, supported by directive antennas and a deployable dataset.",{"name":84,"@type":75,"acceptedAnswer":85},"What model type and performance are reported?",{"text":86,"@type":78},"Tabular dataset–based tree models, especially random forest, achieve the best results with about 92% signal-to-interference-plus-noise ratio accuracy and an anti-jamming response around 1.66 s under severe 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