[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117099-en":3,"doc-seo-117099-105":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},117099,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Malware Detection Techniques based on Machine Learning - Cybersecurity AI and ML","Artificial intelligence and machine learning are key tools for strengthening cybersecurity against modern cyber attacks, as traditional signature-based protection cannot keep pace with rapidly evolving threats. The document presents how AI/ML systems analyze large-scale network traffic to detect patterns and anomalies that may indicate malware activity. It highlights that automated anomaly detection can reduce response time, improve protection efficiency, and better identify previously unseen threats. Continuous research and proper deployment are emphasized to stay ahead of malicious actors.","| Malware Detection Techniques based on Machine\u003Cbr>Learning\u003Cbr>Dhruv Singh Rajput Gouri Sankar Mishra\u003Cbr>Department of Computer Science and Engineering Department of Computer Science and Engineering\u003Cbr>SSET, Sharda University SSET, Sharda University\u003Cbr>Greater NOIDA, U.P. India Greater NOIDA, U.P. India\u003Cbr>[dhruvsinghrajput2002@gmail.com](dhruvsinghrajput2002@gmail.com) [gourisankar.mishra@sharda.ac.in](gourisankar.mishra@sharda.ac.in)\u003Cbr>Ayush Pratap Singh Pradeep Kumar Mishra\u003Cbr>Department of Computer Science and Engineering Department of Computer Science and Application\u003Cbr>SSET, Sharda University SSET, Sharda University\u003Cbr>Greater NOIDA, U.P. India Greater NOIDA, U.P. India\u003Cbr>[ayushpsingh14@gmail.com](ayushpsingh14@gmail.com) [pradeepkumar.mishra@sharda.ac.in](pradeepkumar.mishra@sharda.ac.in)\u003Cbr>Abstract—Artificial intelligence and machine learning have become crucial tools in the fight against cyber attacks. With the constant evolution of technology, traditional methods of protecting networks are no longer enough. This is where AI and machine learning come into play, by analyzing vast amounts of data and detecting patterns or anomalies that might indicate a potential threat. This paper aims at understanding and analyzing the implementation of Artificial Intelligence (AI) and Machine Learning (ML) systems in enhancing cyber security. By detecting patterns and anomalies in network traffic, AI algorithms can quickly identify potential threats and reduce response time, far surpassing human capabilities. This not only saves valuable time and resources for organizations but also improves overall protection against cyber-attacks. As technology continues to advance, it is crucial that we leverage AI for cybersecurity to stay ahead in the fight against malicious actors. With proper utilization of AI and ML technologies, we can ensure a safer digital future for all users. .\u003Cbr>Keywords-Malware; anti-malware; machine learning; feature extraction; feature selection; random forest; SVM; neural networks |\n| --- |\n|  |\n\nI. INTRODUCTION  \nMalware, malicious software designed to harm users, poses a growing cyber threat in today's internet landscape. While traditional signature-based antivirus struggles to detect new and unseen threats, the ease of acquiring malware development tools and pre-made software lowers the barrier for potential attackers. Malware itself employs sophisticated techniques like polymorphism and automatic updates to evade detection. Machine learning offers a promising alternative, analyzing patterns to identify previously unseen malware, but faces challenges with certain types like polymorphic malware and requires high-quality training data. This highlights the need for continuous research and development in detection methods, security practices, and user awareness to combat the evolving threat of malware.  \nA serious cyberthreat known as malware has grown to be a commonplace on the internet, a ground-breaking instrument that has revolutionized communication and information access. These harmful applications, which can range from espionage tools to information-stealing programs, can cause serious harm to unwary users. Malware is software that is expressly created to penetrate user computers and inflict harm in many ways, as defined appropriately by Kaspersky Labs.  \nDue to its reliance on signature-based identification, traditional anti-virus software faces a serious threat from the fast development of malware. Using this method, files are compared to a sizable database of recognized virus signatures. Unfortunately, this method's efficacy is severely limited because it can only recognize malware that has already been encountered. Considering the startling rate at which new malware variants  \nappear—estimates indicate that hundreds of thousands are produced every day—this presents a sizable blind area.  \nThe declining barrier to expertise in malware generation exacerbates the limits of signature-based detection. Becau","cbCais8VVLustmxg","https://ap.wps.com/l/cbCais8VVLustmxg","pdf",196793,1,6,"English","en",105,"# Abstract\n# Introduction\n## Malware threat and limitations of signature-based antivirus\n## Evasion techniques used by modern malware\n## Machine learning approach to malware detection\n## Challenges: polymorphic malware and model accuracy","[{\"question\":\"Why do traditional signature-based antivirus methods struggle with new malware?\",\"answer\":\"They rely on matching files to known signatures, so they work poorly against unseen variants. Rapid malware evolution and new variants appearing frequently create a large blind area.\"},{\"question\":\"How does machine learning improve malware detection compared to signature-based techniques?\",\"answer\":\"Machine learning is trained on datasets containing benign and malicious files, enabling it to learn patterns that distinguish harmful software. This allows detection of malware that has not been encountered before.\"},{\"question\":\"What challenges can still affect AI/ML-based malware detection accuracy?\",\"answer\":\"Polymorphic malware can constantly change its signature, and model performance depends on training data quality. These factors can lead to false positives and false negatives.\"}]","Malware Detection Techniques based on Machine Learning - Cybersecurity AI and ML | PDF",1785673733,15,{"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},"malware-detection-techniques-based-on-machine-learning-cybersecurity-ai-and-ml","",{"@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/malware-detection-techniques-based-on-machine-learning-cybersecurity-ai-and-ml/117099/",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","2026-08-02",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 do traditional signature-based antivirus methods struggle with new malware?","Question",{"text":75,"@type":76},"They rely on matching files to known signatures, so they work poorly against unseen variants. Rapid malware evolution and new variants appearing frequently create a large blind area.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning improve malware detection compared to signature-based techniques?",{"text":80,"@type":76},"Machine learning is trained on datasets containing benign and malicious files, enabling it to learn patterns that distinguish harmful software. This allows detection of malware that has not been encountered before.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges can still affect AI/ML-based malware detection accuracy?",{"text":84,"@type":76},"Polymorphic malware can constantly change its signature, and model performance depends on training data quality. 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