[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119041-en":3,"doc-seo-119041-105":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},119041,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Ransomware Threat Mitigation Through Network Traffic Analysis and Machine Learning Techniques","Ransomware attacks have rapidly increased, enabling attackers to compromise networks and cause lasting harm to organizations and everyday users. Such incidents can lead to the loss or exposure of sensitive information, disruption of normal operations, and the creation of persistent security weaknesses. The paper proposes a ransomware recognition and identification method using machine learning and network-traffic pattern analysis. By collecting and studying traffic and applying trained models, the approach detects ransomware with high precision and accuracy.","RANSOMWARE THREAT MITIGATION THROUGH NETWORK TRAFFIC ANALYSIS AND MACHINE LEARNING TECHNIQUES  \nAli Mehrban 1 , Shirin Karimi Geransayeh2  \n1 School of Electrical and Electronic Engineering, Newcastle University, Newcastle, UK  \n2Department of IT Management, Ferdowsi University of Mashhad, Iran  \nABSTRACT  \nIn recent years, there has been a noticeable increase in cyberattacks using ransomware. Attackers use this malicious software to break into networks and harm computer systems. This has caused significant and lasting damage to various organizations, including government, private companies, and regular users. These attacks often lead to the loss or exposure of sensitive information, disruptions in normal operations, and persistent vulnerabilities. This paper focuses on a method for recognizing and identifying ransomwarein computer networks. The approach relies on using machine learning algorithms and analyzing the patterns of network traffic. By collecting and studying this traffic, and then applying machine learning models, we can accurately identify and detect ransomware. The results of implementing this method show that machine learning algorithms can effectively pinpoint ransomware based on network traffic, achieving high levels of precision and accuracy.  \nKEYWORDS  \nKeywords: Ransomware, Computer Networks, Cyber Attacks, Network Traffic, Machine Learning  \n1. INTRODUCTION  \nRansomware, a prevalent form of malware, has been proliferating in recent times, posing considerable threats to a wide range of victims, including various organizations and regular users. This menace is not constrained by geographic location or specific operating systems. According to reports from “Cyber Security Ventures”, the total damage caused by ransomware to organizations and individuals in 2019 amounted to approximately $11.5 billion. Shockingly, every 14 seconds, a user or organization falls victim to aransomware attack, a timeframe that has reduced to 11 seconds in 2021[1] . Within this remarkably short span, cyberattacks through ransomware have escalated, becoming increasingly perilous and invasive.  \nThese attacks have impacted diverse sectors, including finance, insurance, banking, real estate, healthcare, and government management. Symantec Security reports a 46% increase in the number of ransomware types in 2017 [2] . Ransomware restricts victims'access to their files by encrypting targeted files until the ransom is paid [3] . In 2017, the“No More Ransom” project was initiated as a collaboration between European law enforcement and information technology security companies, aiming to disrupt criminal activities associated with ransomware and assist businesses and individuals in mitigating  \nits impact [4] . Similar commercial software products have also been developed for network defense.  \nSecurity solutions like “Cybereason” utilize behavioral techniques to protect consumer networks [5], while “Darktrace” employs advanced unsupervised machine learning techniques for organizational network protection [6] . Several machine learning techniques and frameworks for ransomware detection and identification have been proposed and implemented. Malware analysis and detection have been subjects of research for years, involving both static and dynamic analysis approaches, with various analysis tools recommended [7], [8] . Additionally, various classification approaches for malware have been suggested [9], [10], but these approaches may not be suitable for defending against ransomware, as they generally focus on distinguishing malware from benign software.  \nTherefore, specialized detection mechanisms for ransomware are necessary, focusing on specific ransomware features to differentiate it from other malware types and benign software. This article proposes a method for detecting and identifying ransomware in computer networks using machine learning techniques.  \nThe steps for ransomware detection and identification through network traffic analysis","cbCaipOYo1NLEWvh","https://ap.wps.com/l/cbCaipOYo1NLEWvh","pdf",667875,1,12,"English","en",105,"# Introduction\n# Related Work\n## Machine learning and deep learning approaches\n# Proposed Method\n# Evaluation and Results\n# Conclusions","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses the growing threat of ransomware by focusing on recognizing and identifying ransomware within computer networks using network traffic analysis and machine learning techniques.\"},{\"question\":\"How does the proposed method detect ransomware?\",\"answer\":\"It collects and studies network traffic, extracts ransomware-specific features using the T-shark network protocol analyzer, and then applies machine learning models to identify ransomware.\"},{\"question\":\"Why are generic malware-detection methods insufficient for ransomware?\",\"answer\":\"Many existing classification approaches aim to distinguish malware from benign software, but ransomware requires specialized detection mechanisms based on features that differentiate it from other malware types.\"}]","Ransomware Threat Mitigation Through Network Traffic Analysis and Machine Learning Techniques | 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problem does the paper address?","Question",{"text":76,"@type":77},"The paper addresses the growing threat of ransomware by focusing on recognizing and identifying ransomware within computer networks using network traffic analysis and machine learning techniques.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method detect ransomware?",{"text":81,"@type":77},"It collects and studies network traffic, extracts ransomware-specific features using the T-shark network protocol analyzer, and then applies machine learning models to identify ransomware.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are generic malware-detection methods insufficient for ransomware?",{"text":85,"@type":77},"Many existing classification approaches aim to distinguish malware from benign software, but ransomware requires specialized detection mechanisms based on features that differentiate it from other malware 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