[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124085-en":3,"doc-seo-124085-105":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},124085,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Adversarial Machine Learning Approaches for Strengthening Cybersecurity in Intrusion Detection Systems","Adversarial machine learning is used to improve intrusion detection systems’ ability to counter sophisticated cyberattacks and reduce vulnerabilities to adversarial instances and evasion strategies. The work outlines practical challenges of deploying adversarial techniques, approaches for performance measurement, and ethical considerations. It discusses multi-level detection of artifacts, complex model building, and data gathering, emphasizing the trade-off between robustness and detection performance. It also motivates new performance metrics and stable, formally correct IDS behavior across network contexts.","Adversarial Machine Learning Approaches for Strengthening Cybersecurity in Intrusion Detection Systems  \nRahul Gupta  \nAmity University, Pune, INDIA.  \n[www.jrasb.com || Vol. 2 No. 5](www.jrasb.com || Vol. 2 No. 5) (2023): October Issue  \nReceived: 28-09-2023 Revised: 15-10-2023 Accepted: 20-10-2023  \nABSTRACT  \nIn this study, adversarial machine learning to enhance IDS’s capability to counterattack sophisticated cyberattacks employed in the investigation. This paper describes challenges in practice of adversarial techniques, performance measurement and ethical issues. In the research proposal, the authors describe the comprehensive and multi-level method of detecting artifacts, building complex models, and gathering data. Researchers stressed important conclusions regarding aggressiveness of privacypreserving methods, the need for developing new performance metrics, and the tension between robust model and detection performance. The research assists in developing IDS that are both efficient and formally correct in various contexts of a network.  \nKeywords-Intrusion, detection, systems, adversarial, Machine Learning, develop, robust.  \nI. INTRODUCTION  \nIntrusion detection systems (IDS) ability to stand against other elaborate cyber-attacks is something that has become essential to improve through the use of adversarial machine learning. Better solutions are sorely missing as classical IDS become vulnerable to adversarial instances and forms of evasion. It is the objective of this project to develop a dependable IDS that would be effective at detecting and mitigating strong cyber threats with the use of adversarial machine learning. The vision is to create IDS that are robust against adversarial attacks and maintain high detection rates despite adversarial changes in the environment using adversarial training, model hardening, and adaptive defenses’ integration. The work analyzes the challenges brought by malicious machine learning in IDS and provides futuristic solutions to improve the system’s stability.  \nII. LITERATURE REVIEW  \n2.1 Android Malware Classification using Adversarial Machine Learning for Hacking  \nAccording to the author, [Chen](Chen et. al. 2018)[ et. al.](Chen et. al. 2018)[ 2018](Chen et. al. 2018), This paper explains how adversarial attacks can threaten the effectiveness of the machine learning-based Android  \nmalware detection systems. Thus, to counter such rooteddeceived programs, the authors propose KuafuDet, a twophase iterative adversarial-based detection system with a similarity-based filter. They divide the type of attackers into three classes and prove how effective the toxin attacks are against existing defense mechanisms. Thus, in non-adversarial environments, KuafuDet achieves the result of 96%, while in adversarial environments, the result does not go below 15% . The technology is easily expandable, works, and is even better than the leading antivirus programs. It reaffirms the consideration of hostile incidents within the process of swinging mobile malware detection and introduces an inventive approach to enhance the protection from these attacks.  \nFigure 1: Intrusion Detection network  \n(Source: [https://www.mdpi.com](https://www.mdpi.com))  \n2.2 Achievements and Challenges in ML for Image Forensics  \nAccording to the author, Nowroozi [et. al.](et. al. 2021)[ 2021](et. al. 2021), the paper discusses the rising relevance of picture forensics to prevent the spread of doctored images, which cause harm to criminal and civil jurisdiction. It highlights the fact that different machine learning approaches are progressively applied in picture forensics for classification, identification, and verification of pictures’origin and integrity. However, the study also revealed that such machine learning-based defenses are very vulnerable to adversarial attacks. These restrictions may lead to rather unfair trials or, in other words, evidence that is inadmissible according to the legal norms. Thus, in image","cbCaiu9FkJEGaPee","https://ap.wps.com/l/cbCaiu9FkJEGaPee","pdf",474185,1,13,"English","en",105,"# Introduction\n# Literature Review\n## Android Malware Classification using Adversarial Machine Learning\n## Achievements and Challenges in ML for Image Forensics\n# Methods\n## Data collection and data processing\n## Designing of Machine Learning Models","[{\"question\":\"Why are adversarial machine learning methods important for intrusion detection systems?\",\"answer\":\"Classical IDS can be vulnerable to adversarial instances and evasion. Adversarial machine learning aims to build IDS that remain robust and keep high detection rates under adversarial environmental changes.\"},{\"question\":\"What challenges does the study highlight when applying adversarial techniques?\",\"answer\":\"The study addresses practical deployment challenges, methods for performance measurement, and ethical issues, along with difficulties in balancing robust models against detection performance.\"},{\"question\":\"How is the dataset prepared to support robust intrusion detection models?\",\"answer\":\"The approach uses diverse real network traffic with shared and abnormal examples, applies preprocessing such as feature extraction and normalization, handles class imbalance using undersampling/oversampling (e.g., SMOTE), and generates adversarial examples using methods like PGD and FGSM before splitting into training, validation, and test sets.\"}]","Adversarial Machine Learning Approaches for Strengthening Cybersecurity in Intrusion Detection Systems | PDF",1785820249,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"adversarial-machine-learning-approaches-for-strengthening-cybersecurity-in-intrusion-detection-systems","",{"@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/adversarial-machine-learning-approaches-for-strengthening-cybersecurity-in-intrusion-detection-systems/124085/",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 are adversarial machine learning methods important for intrusion detection systems?","Question",{"text":75,"@type":76},"Classical IDS can be vulnerable to adversarial instances and evasion. Adversarial machine learning aims to build IDS that remain robust and keep high detection rates under adversarial environmental changes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenges does the study highlight when applying adversarial techniques?",{"text":80,"@type":76},"The study addresses practical deployment challenges, methods for performance measurement, and ethical issues, along with difficulties in balancing robust models against detection performance.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the dataset prepared to support robust intrusion detection models?",{"text":84,"@type":76},"The approach uses diverse real network traffic with shared and abnormal examples, applies preprocessing such as feature extraction and normalization, handles class imbalance using undersampling/oversampling (e.g., SMOTE), and generates adversarial examples using methods like PGD and FGSM before splitting into training, validation, and test sets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]