[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127884-en":3,"doc-seo-127884-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127884,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Next-Generation Security - Detecting Suspicious Liquids through Software Defined Radio Frequency Sensing and Machine Learning","Hazardous liquids such as nitroglycerin are increasingly used in modern terrorist attacks, while their concealability makes identification difficult. Conventional liquid detection methods are limited by cost, accuracy, and scalability, restricting large-scale deployment in public spaces. The work proposes a detection platform combining software-defined radio frequency sensing with machine learning to detect and classify suspicious and non-suspicious liquids, using OFDM-based channel state information at 900 MHz and 2.45 GHz. Experiments show over 95% correct classification, identifying more than 97% of suspicious types, and up to 98% of non-suspicious liquids. Results indicate the proposed system is accurate, efficient, and practical.","This article has been accepted for publication in IEEE Sensors Journal. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/JSEN.2024.3351226  \nIEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2017 1  \nNext-Generation Security: Detecting Suspicious Liquids through Software Defined Radio Frequency Sensing and Machine Learning  \nAhmad Daud, Muhammad Bilal Khan, Abdul Basit Khattak, Shujaat Ali Khan Tanoli, Ali Mustafa, Mubashir  \nRehman, and Onel L. A. Lpez, Member, IEEE  \nAbstract—Hazardous liquids, such as nitroglycerin, are now pre  \nferred over traditional explosives in modern terrorist attacks. Their inconspicuous nature poses an identification challenge, necessitating urgent large-scale security inspections to prevent terrorism in public spaces. However, conventional liquid detection methods face obstacles in terms of cost, accuracy, and scalability, hindering their widespread use. This article introduces a platform that combines radio frequency sensing based on software-defined radio technology and state-of-the-art machine learning (ML) algorithms  \nto detect and classify suspicious and non-suspicious liquids. The  \ndetection method utilizes fine-grained samples of orthogonal frequency division multiplexing(OFDM) to acquire channel state information of the liquids present in the environment at operating frequencies 900 MHz and 2.45 GHz. ML algorithms are employed for classification purposes based on liquids’ dielectric properties, and their effectiveness is evaluated based on accuracy, prediction speed, and training time. The analysis of experimental results demonstrated that our method successfully classified over 95% of both suspicious and non-suspicious liquids. It also identified more than 97% of suspicious liquid types and classified up to 98% of non-suspicious liquids. These findings confirm the efficacy of the proposed system. The software-defined radio frequency sensing system is versatile, portable, adaptable, and costefficient.  \nIndex Terms—Channel state information (CSI), Machine Learning(ML), orthogonal frequency division multiplex (OFDM), radio frequency (RF), software-defined radio (SDR), suspicious liquids.  \nI. INTRODUCTION  \nTHE unauthorized carriage of highly combustible and  \neruptive liquids poses a great threat to human safety [1] . These explosives can cause severe harm due to their toxicity and unsafe exposure levels. The extent of damage depends on factors such as liquid type, deployment method, area, chemical properties, and concentration [2] . Indeed, liquid bombs are convenient for terrorist activities because i) the material is easy to supply in the form of commercial products, ii) they  \nManuscript submitted on 21 September, 2023 .“This work was supported in part by the COMSATS University Islamabad-Attock Campus, Academy of Finland, 6G Flagship programme (Grant no. 346208) and the Finnish Foundation for Technology Promotion.”  \nAhmad Daud, Muhammad Bilal Khan, Shujaat Ali Khan Tanoli, and Mubashir Rehman are with the Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, 43600, Pakistan (e-mail: [ahmad.seemab@gmail.com](ahmad.seemab@gmail.com) ; engr tanoli@ciit[attock.edu.pk](attock.edu.pk) ; [shujat@cuiatk.edu.pk](shujat@cuiatk.edu.pk) ; mubashir   rehman7@ciit[attock.edu.pk](attock.edu.pk)).  \nAbdul Basit Khattak and Onel L. A. Lpez are with the Centre for Wireless Communications (CWC), University of Oulu, 90014, Finland (e-mail: Abdul.Khattak@oulu.fi; onel.alcarazlopez@oulu.fi) .  \nAli Mustafa is with the Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad Campus, Pakistan (e-mail: [ali.mustafa@comsats.edu.pk](ali.mustafa@comsats.edu.pk)).  \ncan be implemented as binary systems 1 so components can be transported separately and are easy to mix and combine different proportions, and iii) the ability to initiate explosive ch","cbCaiedI2dsSTJjx","https://ap.wps.com/l/cbCaiedI2dsSTJjx","pdf",13412306,1,13,"English","en",105,"# Introduction\n## Threat background and detection challenges\n## Limitations of conventional methods\n# Proposed approach\n## SDR-based RF sensing and OFDM CSI acquisition\n## Machine learning classification using dielectric properties\n# Experimental evaluation\n## Accuracy, prediction speed, and training time\n## Results on suspicious and non-suspicious liquids\n# Conclusion","[{\"question\":\"Why is detecting suspicious liquids important in security screening?\",\"answer\":\"Hazardous liquids can be concealed easily and are used in terrorist attacks, creating a serious safety risk. Their inconspicuous nature makes identification challenging in public places.\"},{\"question\":\"How does the proposed method detect suspicious liquids?\",\"answer\":\"It uses software-defined radio frequency sensing with OFDM to acquire fine-grained channel state information at 900 MHz and 2.45 GHz. Machine learning then classifies liquids based on their dielectric properties.\"},{\"question\":\"What performance does the system achieve in experiments?\",\"answer\":\"The method classifies over 95% of both suspicious and non-suspicious liquids. It identifies more than 97% of suspicious liquid types and classifies up to 98% of non-suspicious liquids.\"}]","Next-Generation Security - Detecting Suspicious Liquids through Software Defined Radio Frequency Sensing and Machine Learning | PDF",1785942601,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"next-generation-security-detecting-suspicious-liquids-through-software-defined-radio-frequency-sensing-and-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/next-generation-security-detecting-suspicious-liquids-through-software-defined-radio-frequency-sensing-and-machine-learning/127884/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is detecting suspicious liquids important in security screening?","Question",{"text":76,"@type":77},"Hazardous liquids can be concealed easily and are used in terrorist attacks, creating a serious safety risk. Their inconspicuous nature makes identification challenging in public places.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method detect suspicious liquids?",{"text":81,"@type":77},"It uses software-defined radio frequency sensing with OFDM to acquire fine-grained channel state information at 900 MHz and 2.45 GHz. Machine learning then classifies liquids based on their dielectric properties.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance does the system achieve in experiments?",{"text":85,"@type":77},"The method classifies over 95% of both suspicious and non-suspicious liquids. 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