[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122811-en":3,"doc-seo-122811-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},122811,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Interpretable Anomaly Detection - A Hybrid Approach Using Rule-Based and Machine Learning Techniques - Conference Paper","Anomaly detection is essential for safeguarding security and reliability across domains such as cybersecurity, finance, and industrial operations. Conventional black-box techniques often achieve accuracy while failing to provide interpretability, leaving users unable to understand why anomalies are flagged. This research presents a hybrid method that combines a rule-based component for generating interpretable detection rules with machine learning for pattern recognition and classification. Evaluation on real-world datasets demonstrates effective anomaly identification while delivering transparent explanations, supported by comparative experiments against leading approaches. Results emphasize how interpretability improves trust and supports transparent decision-making.","Ouarbya, Lahcen and Rahul, Mohite. 2023. 'Interpretable Anomaly Detection: A Hybrid Approach Using Rule-Based and Machine Learning Techniques'. In: 2024 IEEE 9th International conference for Convergence in Technology (I2CT) . Vivanta Pune, Hinjawadi, Hinjawadi Road Hinjawadi Village, Hinjawadi, Pune, India 5-7 April 2024 . [Conference or Workshop Item](Forthcoming)  \n[https://research.gold.ac.uk/id/eprint/34694/](https://research.gold.ac.uk/id/eprint/34694/)  \nThe version presented here may differ from the published, performed or presented work. Please go to the persistent GRO record above for more information.  \nIf you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Goldsmiths, University of London via the following email address: [gro@gold.ac.uk](gro@gold.ac.uk).  \nThe item will be removed from the repository while any claim is being investigated. For  \nmore information, please contact the GRO team: [gro@gold.ac.uk](gro@gold.ac.uk)  \nInterpretable Anomaly Detection: A Hybrid Approach Using Rule-Based and Machine Learning  \nTechniques  \nRahul Mohite & Lahcen Ouarbya  \nDepartment of Computing, Goldsmiths University of London  \nAbstract—Anomaly detection is a critical aspect of ensuring the security and reliability of various systems in diverse domains, including cybersecurity, finance, and industrial processes. Traditional blackbox anomaly detection methods often lack interpretability, making it challenging for users to understand the reasoning behind the detection of anomalies. In this research, we propose a novel hybrid approach that combines rule-based and machine learning techniques to enhance the interpretability of anomaly detection systems. Our method integrates a rule-based system that generates interpretable anomaly detection rules with machine learning components that leverage complex pattern recognition and classification capabilities. We evaluate the proposed approach on a diverse set of real-world datasets, demonstrating its effectiveness in identifying anomalies while providing transparent explanations of the detection process. Through comprehensive experimentation and comparative analysis with existing state-of-the-art methods, we showcase the superior interpretability and performance of our hybrid approach. Our findings highlight the significance of interpretability in anomaly detection systems and underscore the potential of the proposed approach for enhancing transparency and trust in critical decision-making processes. This research contributes to the advancement of interpretable anomaly detection techniques and opens avenues for future research in the domain of transparent and reliable anomaly detection systems.  \n——  \nKeywords—Anomaly detection, Outlier analysis, Interpretability, Rule-based systems, Machine learning, Hybrid approach, Transparency, Trustworthy AI, Pattern recognition, Classification, Data analysis, Cybersecurity, Financial fraud detection, Industrial processes, Explainable AI.  \nI. INTRODUCTION  \nANOMALY DETECTION is a critical aspect of ensuring  \nthe security and reliability of various systems in diverse domains, such as cybersecurity, finance, and industrial processes. Traditional approaches to anomaly detection often rely on black-box models, which, while achieving high accuracy, lack transparency and interpretability, making it challenging for users to comprehend the reasoning behind the identification of anomalies [1] . Consequently, there is a growing need for the development of anomaly detection systems that not only demonstrate high precision but also provide interpretable insights into the detected anomalies, enhancing users’ understanding and trust in the decision-making process [2] .  \nIn response to this demand, this research paper proposes a novel hybrid approach that integrates rule-based and machine learning techniques to facilitate interpretable anomaly detection. By combining a rule-based system capable of ge","cbCairQ65r5AwPi6","https://ap.wps.com/l/cbCairQ65r5AwPi6","pdf",163155,1,10,"English","en",105,"# Introduction\n# Background","[{\"question\":\"Why is interpretability important in anomaly detection systems?\",\"answer\":\"Interpretability helps users understand the reasoning behind anomaly identification. Black-box models may be accurate but do not provide transparent explanations, reducing trust in decision-making.\"},{\"question\":\"What does the proposed hybrid approach combine?\",\"answer\":\"The approach integrates a rule-based system that generates human-readable anomaly detection rules with machine learning components for complex pattern recognition and classification.\"},{\"question\":\"How is the method evaluated and what are the outcomes?\",\"answer\":\"The approach is evaluated on diverse real-world datasets and compared with state-of-the-art methods. The results show strong anomaly detection effectiveness while providing understandable explanations that improve transparency and trust.\"}]","Interpretable Anomaly Detection - A Hybrid Approach Using Rule-Based and Machine Learning Techniques - Conference Paper | PDF",1785813031,25,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"interpretable-anomaly-detection-a-hybrid-approach-using-rule-based-and-machine-learning-techniques-conference-paper","",{"@graph":36,"@context":85},[37,54,68],{"@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/interpretable-anomaly-detection-a-hybrid-approach-using-rule-based-and-machine-learning-techniques-conference-paper/122811/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is interpretability important in anomaly detection systems?","Question",{"text":75,"@type":76},"Interpretability helps users understand the reasoning behind anomaly identification. Black-box models may be accurate but do not provide transparent explanations, reducing trust in decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed hybrid approach combine?",{"text":80,"@type":76},"The approach integrates a rule-based system that generates human-readable anomaly detection rules with machine learning components for complex pattern recognition and classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the method evaluated and what are the outcomes?",{"text":84,"@type":76},"The approach is evaluated on diverse real-world datasets and compared with state-of-the-art methods. 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