[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86643-en":3,"doc-seo-86643-105":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":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":13,"seo_description":14,"update_tm":28,"read_time":29},86643,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Explainable Artificial Intelligence for Intrusion Detection in IoT Networks","The document addresses security challenges in Internet of Things (IoT) networks, where rapid device growth and massive data increase exposure to new attacks. It proposes a deep learning intrusion detection framework to classify multiple attack types using two publicly available datasets, NSL-KDD and UNSW-NB15. Two deep models are built and evaluated: a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN), with DNN achieving higher accuracy. Because deep models are hard to interpret, explainable AI methods—LIME and SHAP—are applied to support model understanding and confidence.","Expert Systems With Applications 238 (2024) 121751  \n| Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach\u003Cbr>Bhawana Sharma a, Lokesh Sharma a,∗, Chhagan Lalb, Satyabrata Royaa Manipal University Jaipur, Jaipur, Rajasthan, India\u003Cbr>b Department of Intelligent Systems, Cybersecurity Group, TU Delft, Netherlands |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Intrusion detection system\u003Cbr>DL\u003Cbr>Deep neural network Convolution neural network\u003Cbr>XAI\u003Cbr>Local interpretable model-agnostic explanations\u003Cbr>Shapley additive explanations |  | The Internet of Things (IoT) is currently seeing tremendous growth due to new technologies and big data. Research in the field of IoT security is an emerging topic. IoT networks are becoming more vulnerable to new assaults as a result of the growth in devices and the production of massive data. In order to recognize the attacks, an intrusion detection system is required. In this work, we suggested a Deep Learning (DL) model for intrusion detection to categorize various attacks in the dataset. We used a filter-based approach to pick out the most important aspects and limit the number of features, and we built two different deep-learning models for intrusion detection. For model training and testing, we used two publicly accessible datasets, NSL-KDD and UNSW-NB 15. First, we applied the dataset on the Deep neural network (DNN) model and then the same dataset on Convolution Neural Network (CNN) model. For both datasets, the DL model hada better accuracy rate. Because DL models are opaque and challenging to comprehend, we applied the idea of explainable Artificial Intelligence (AI) to provide a model explanation. To increase confidence in the DNN model, we applied the explainable AI (XAI) Local Interpretable Model-agnostic Explanations (LIME ) method, and for better understanding, we also applied Shapley Additive Explanations (SHAP). |\n\n1. Introduction  \nIn recent years, IoT has been gaining popularity, and with the advancement of technologies, the internet, and big data, security has become essential for IoT networks. Researchers are seeking attention to the intrusion detection system for IoT networks for detecting malicious activities. Identifying any suspicious or abnormal activity generates a signal, thus preventing vulnerable devices. Since many heterogeneous devices for different applications are connected in IoT networks and generate big data within the network, thus the significant challenges are storage, computation of big data, and cyber security in IoT networks (Al-Fuqaha, Guizani, Mohammadi, Aledhari, & Ayyash, 2015; Da Xu, He, & Li, 2014).  \nThe Intrusion Detection System (IDS) has two types of detection methods. One method is Signature-based IDS which detects malicious activity based on known signatures stored in the database; another method is anomaly-based, which detects the abnormal behavior of the system.  \nSignature-based IDS are proven to be inefficient in today’s scenario for two main reasons. First, it needs the predetermined knowledge of  \nsignatures or attacks and is thus incapable of detecting new or zeroday attacks. Secondly, storing attacks in the database and computation for the devices in IoT networks with limited storage and computation capacity is inefficient.  \nAnomaly-based IDS detects abnormal behavior and is thus capable of detecting new or unknown attacks which are different from normal ones. The drawback is that it detects any change from the normal behavior and identifies it as abnormal behavior, and thus false positives are generated (Ahmad, Shahid Khan, Wai Shiang, Abdullah, & Ahmad, 2021; Sharma, Sharma, & Lal, 2019). With recent development in Machine Learning (ML)/Deep Learning (DL) techniques, these techniques are employed in Anomaly-based IDS to remove the drawbacks. Anomaly-based detection using ML/DL techniques can detect intrusions with higher accuracy and i","cbCaipLrJxOd2c9N","https://ap.wps.com/l/cbCaipLrJxOd2c9N","pdf",4462055,5,1,16,"English","en",105,"# Introduction\n## IDS detection approaches (signature-based vs anomaly-based)\n## Deep learning for intrusion detection in IoT\n## IoT architecture and security challenges\n# Proposed explainable deep learning approach\n## Model training with NSL-KDD and UNSW-NB15\n## DNN and CNN comparison\n## Explainability using LIME and SHAP","[{\"question\":\"What problem does the document focus on in IoT networks?\",\"answer\":\"It focuses on intrusion detection in IoT networks, where increased devices and data make systems more vulnerable to new attacks and malicious activities.\"},{\"question\":\"How does the proposed approach detect intrusions?\",\"answer\":\"It trains deep learning models to categorize different attack types using publicly accessible datasets NSL-KDD and UNSW-NB15, evaluating both DNN and CNN.\"},{\"question\":\"Why are explainable AI methods included, and which ones are used?\",\"answer\":\"Deep learning models are described as difficult to interpret, so explainable AI is applied to provide model explanations. The methods used are LIME (for DNN confidence) and SHAP (for better understanding).\"}]",1784237781,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"explainable-artificial-intelligence-for-intrusion-detection-in-iot-networks","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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/explainable-artificial-intelligence-for-intrusion-detection-in-iot-networks/86643/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"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","2026-07-29","2026-07-16",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},"What problem does the document focus on in IoT networks?","Question",{"text":76,"@type":77},"It focuses on intrusion detection in IoT networks, where increased devices and data make systems more vulnerable to new attacks and malicious activities.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed approach detect intrusions?",{"text":81,"@type":77},"It trains deep learning models to categorize different attack types using publicly accessible datasets NSL-KDD and UNSW-NB15, evaluating both DNN and CNN.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are explainable AI methods included, and which ones are used?",{"text":85,"@type":77},"Deep learning models are described as difficult to interpret, so explainable AI is applied to provide model explanations. 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