{"id":2013,"date":"2025-02-20T19:46:05","date_gmt":"2025-02-20T19:46:05","guid":{"rendered":"https:\/\/cybersecurityinfocus.com\/?p=2013"},"modified":"2025-02-20T19:46:05","modified_gmt":"2025-02-20T19:46:05","slug":"advanced-network-traffic-analysis-machine-learning-and-its-impact-on-nta","status":"publish","type":"post","link":"https:\/\/cybersecurityinfocus.com\/?p=2013","title":{"rendered":"Advanced Network Traffic Analysis: Machine Learning and Its Impact on NTA"},"content":{"rendered":"
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Machine Learning (ML) has revolutionized industries by empowering systems to learn from data, make predictions, automate decisions, and uncover insights\u2014all without the need for explicit programming. With ML, systems can:<\/span>\u00a0<\/span><\/p>\n

Learn from data.<\/span>Analyze data quickly<\/span>Make autonomous decisions<\/span>\u00a0<\/span>\t\t\t\t\t\t<\/p><\/div>\n<\/div>\n

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In network security and cybersecurity, ML and other <\/span>emerging <\/span>technologies are crucial for detecting malicious activities such as unauthorized access, data breaches, and other<\/span> complex<\/span> security threats.<\/span><\/p>\n<\/div>\n<\/div>\n

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Network Traffic Analysis (NTA)<\/div>\n<\/div>\n<\/div>\n
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Network Traffic Analysis involves analyzing network traffic data to identify and analyze communication patterns within a network to uncover potential security risks. It can even detect hidden threats through encrypted traffic analysis, ensuring all forms of malicious activity are discovered.\u00a0<\/span>\u00a0<\/span><\/p>\n

As networks expand and become complex, traditional NTA<\/a> tools may struggle to detect new or evolving threats. Integrating machine learning into advanced network traffic analysis helps address these challenges, improving detection and adaptability to rising security demands.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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The Impact of Network Traffic Analysis Using Machine Learning on Network Security<\/h2>\n<\/div>\n<\/div>\n
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Machine learning improves NTA by automating threat detection, boosting accuracy, and reducing false threat alerts through advanced network traffic classification techniques. This is achieved through key functions including pattern recognition<\/a>, intrusion detection, and continuous learning.\u00a0<\/span>\u00a0<\/span><\/p>\n

Let\u2019s explore the key functions of machine learning in more detail.<\/span>\u00a0<\/span><\/p>\n<\/div>\n<\/div>\n

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Core Functions of Machine Learning in Network Traffic Analysis<\/h3>\n<\/div>\n<\/div>\n
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\t\t\t\t\tKey FunctionDescription\t\t\t\t<\/p>\n

\t\t\t\t\tPattern RecognitionAnalyzes network data to identify patterns and unusual behaviors, helping detect potential security issues.PredictionsRecognizes trends in network traffic to predict future events and emerging threats.ClassificationClassifies data as \u2018normal\u2019 or \u2018anomalous\u2019, for detecting threats that traditional methods may miss.Faster Detection & Automated ResponsesSpeeds up threat identification and initiates automated responses to enhance network security and reduce manual work.Reduced False PositivesLearns to differentiate between legitimate and malicious actions, reducing false alarms.Continuous LearningContinuously updates its learnings according to evolving threats and improves its accuracy over time.\t\t\t\t<\/p><\/div>\n<\/div>\n

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Types of Machine Learning Used for NTA<\/h3>\n<\/div>\n<\/div>\n
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There are two main types of machine learning<\/a> used in network traffic analysis:<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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\t\t\t\t\tSupervised LearningUnsupervised Learning\t\t\t\t<\/p>\n

\t\t\t\t\tTrained on labeled data (with known outcomes).Doesn\u2019t require labeled data and finds hidden patterns.Used to detect specific attacks based on recognized patterns.Helps detect unknown attacks and anomalies.Example algorithms: <\/p>\n

Na\u00efve Bayes, Random Forest, Support Vector Machines (SVM).Example algorithms: <\/p>\n

K-Means clustering, DBSCAN.\t\t\t\t<\/p><\/div>\n<\/div>\n

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Both types have distinct advantages when used in network traffic <\/span>behavior<\/span> analysis.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Fidelis Network\u00ae: Machine Learning in Action<\/h2>\n<\/div>\n<\/div>\n
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To effectively use machine learning in your organization\u2019s network traffic analysis, <\/span>it\u2019s<\/span> important to choose a robust ML-integrated Network Detection and Response (NDR) tool.<\/span> And <\/span>Fidelis Network<\/a>\u00ae<\/span> is the right <\/span>option<\/span>!<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Prevention Capabilities of Fidelis NDR<\/div>\n<\/div>\n<\/div>\n
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\n\t\t\t\t\t\t\t Download the whitepaper if you\u2019re looking to improve your cybersecurity posture through advanced sensor technology.<\/span>\t\t\t\t\t\t<\/div>\n<\/div>\n
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\n\t\t\t\t\t\t\t\t\t\tWhat Fidelis Network Includes?<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Fidelis Network\u00ae is a full Network Detection and Response (NDR) solution that <\/span>provides<\/span> deep insights into network traffic for fast detection and response to security threats<\/span> with its<\/span> Deep Session Inspection<\/a> (DSI) and Cyber Terrain Mapping <\/span>specifications, and more.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Application of Machine Learning in NTA with Fidelis Network\u00ae<\/h2>\n<\/div>\n<\/div>\n
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Fidelis Network\u00ae uses both supervised and unsupervised machine learning according to the requirements, analyzing real time and historical data to identify potential threats.\u00a0<\/span>It uses ML methods to spot patterns and unusual behavior in network traffic, such as strange external communication or abnormal internal movements. This approach helps detect threats like data theft, lateral movement, and malware early, providing security teams with quick, actionable alerts to respond effectively to potential issues.<\/span>\u00a0<\/span><\/p>\n

Fidelis addresses two key challenges in network traffic analysis using ML:<\/span><\/p>\n<\/div>\n<\/div>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tFidelis uses ML to create highly accurate baseline models of typical network behavior, incorporating deep learning to flag deviations as suspicious, improving network management and threat detection accuracy.<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\tFidelis applies advanced anomaly detection<\/a> techniques across different contexts to reduce false positives, ensuring that network traffic data handling is efficient and focused on true threats, with only significant threats being flagged for security teams to focus on.<\/span><\/p><\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Contexts Considered by Fidelis Network\u00ae in Network Traffic Analysis<\/h2>\n<\/div>\n<\/div>\n
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Fidelis Network\u00ae incorporates ML into its NTA system, using advanced anomaly detection models across multiple contexts.\u00a0<\/span>\u00a0<\/span><\/p>\n

These contexts include:\u00a0<\/span>\u00a0<\/span><\/p>\n

External Context (North-South Traffic)<\/span>Internal Context (East-West Traffic)<\/span>Application Protocols Context<\/span>Data Movement Context<\/span>Events Detected Using Rules and Signatures Context<\/span>\t\t\t\t\t\t<\/p><\/div>\n<\/div>\n

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Let\u2019s<\/span> go through the<\/span> context<\/span>s<\/span> for more details:<\/span><\/span><\/em><\/p>\n<\/div>\n<\/div>\n

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1. External Context (North-South Traffic) <\/h3>\n<\/div>\n<\/div>\n
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In the external context, ML analyzes traffic between the internal network and external locations (north-south communication). This context focuses on detecting suspicious behavior in traffic moving between internal systems and the broader internet.<\/span>\u00a0<\/span><\/p>\n

An example of a threat detected:<\/span>\u00a0<\/span><\/p>\n

ML detects anomalies where traffic is directed to previously unseen or unusual locations. This could potentially signal data exfiltration or other malicious activity.<\/span>\u00a0<\/span><\/p>\n

Fidelis NDR uses unsupervised ML to detect abnormal external traffic patterns and correlates these findings with relevant techniques in the MITRE ATT&CK framework<\/a>, such as data exfiltration and Drive-by Compromise tactics.<\/span><\/p>\n<\/div>\n<\/div>\n

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2. Internal Context (East-West Traffic)<\/h3>\n<\/div>\n<\/div>\n
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In the internal context, ML focuses on traffic within the organization\u2019s network. It tracks patterns of communication between internal assets, monitors remote access behaviors, and assesses data movement within systems.\u00a0<\/span>\u00a0<\/span><\/p>\n

An example of suspicious activities flagged by ML is:<\/span>\u00a0<\/span><\/p>\n

Password Spraying\/Brute Force Attacks \u2013 ML identifies spikes in failed login attempts, which could indicate attackers trying various passwords to gain unauthorized access.<\/span>\u00a0<\/span><\/p>\n

These abnormal behaviors are detected by Fidelis<\/a> using supervised machine learning algorithms that analyze connection patterns, login behaviors, and data flows. This early detection helps uncover potential threats before they escalate.<\/span><\/p>\n<\/div>\n<\/div>\n

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3. Application Protocols Context<\/h3>\n<\/div>\n<\/div>\n
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In this context, ML analyzes traffic patterns at the application layer, detecting deviations in the usage of protocols such as HTTP, DNS, FTP, and others. Both types of machine learning are employed by Fidelis in the context of application protocols.<\/span>\u00a0<\/span><\/p>\n

By monitoring this layer, Fidelis helps identify abnormal traffic patterns that could indicate malicious activities, such as:<\/span>\u00a0<\/span><\/p>\n

Detection of unusual application protocols being used or known protocols being accessed over uncommon ports.<\/span>\u00a0<\/span>Detects instances where legitimate protocols are misused, such as malware hiding its communications inside commonly used protocols.<\/span>\t\t\t\t\t\t<\/p><\/div>\n<\/div>\n

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\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tSuggested Reading: Detect Threats by Modeling Application Protocol Behaviors<\/a><\/span><\/p><\/div>\n<\/div>\n

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This context is crucial for <\/span>identifying<\/span> covert data exfiltration or malware communication attempts disguised within <\/span>seemingly normal<\/span> network <\/span>behavior<\/span> and traffic.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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4. Data Movement Context<\/h3>\n<\/div>\n<\/div>\n
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Th<\/span>is <\/span>context focuses on tracking how data moves across the network between assets, particularly <\/span>identifying<\/span> any anomalies in data transfers or file movements. This is a critical context for <\/span>identifying<\/a><\/span> data exfiltration<\/a> or lateral movements of sensitive information.<\/span> Supervised learning is used to model normal data transfer patterns between internal assets and <\/span>identify<\/span> anomalies, such as abnormal data collection activities.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tSuggested Reading: Comprehensive Data Security: Protecting Data at Rest, In Motion, and In Use<\/a><\/span><\/p><\/div>\n<\/div>\n

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5. Events Detected Using Rules and Signatures Context<\/h3>\n<\/div>\n<\/div>\n
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This context uses predefined rules and signatures to identify known threat patterns. These techniques are fundamental for detecting known attacks and malware based on their unique signatures or behaviors. Supervised learning is used to enhance traditional rule- and signature-based detection<\/a> methods.<\/span>\u00a0<\/span><\/p>\n

Overall, Fidelis Network<\/a>\u00ae uses machine learning across these five critical contexts to develop a multi-dimensional approach to network traffic analysis.<\/span>\u00a0<\/span><\/p>\n

The combination of supervised and unsupervised ML, advanced anomaly detection, and contextual analysis allows Fidelis to uncover even the most sophisticated attacks\u2014detecting everything from zero-day exploits to advanced threats. This ensures that security teams receive actionable insights and alerts, helping them respond to potential threats swiftly and accurately.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Conclusion<\/h2>\n<\/div>\n<\/div>\n
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Combining Machine Learning with Network Traffic Analysis offers a robust, intelligent approach to network security, detecting threats from minor to advanced quickly and <\/span>automatically before they can compromise the network<\/span>. <\/span>Adopting a robust ML-integrated NDR tool like Fidelis Network\u00ae is the ideal solution to protect your network, respond swiftly, and prevent future incidents.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Frequently Ask Questions<\/h2>\n<\/div>\n<\/div>\n
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What is Network Traffic Analysis (NTA) and how does it help network security?<\/h3>\n<\/div>\n
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Network Traffic Analysis (NTA) involves monitoring network data to <\/span>identify<\/span> unusual communication patterns and detect hidden security threats, even in encrypted traffic, to ensure network security.<\/span><\/span><\/p>\n<\/div><\/div>\n

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How does Machine Learning improve Network Traffic Analysis? <\/h3>\n<\/div>\n
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Machine Learning enhances NTA by automating threat detection, reducing false alarms, and <\/span>analyzing<\/span> traffic patterns through data classification, pattern recognition, and threat prediction. Over time, it learns to spot new and evolving threats, enabling networks to respond quickly and effectively to security risks.<\/span><\/p>\n<\/div><\/div>\n

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What are the benefits of using both supervised and unsupervised machine learning for Network Traffic Analysis?<\/h3>\n<\/div>\n
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Combining supervised and unsupervised machine learning provides a comprehensive approach to threat detection. Supervised learning helps identify known attacks, while unsupervised learning detects unknown threats and anomalies.<\/span><\/p>\n<\/div><\/div>\n

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How does Fidelis Network\u00ae use Machine Learning for Network Traffic Analysis?<\/h3>\n<\/div>\n
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Fidelis Network\u00ae uses both supervised and unsupervised machine learning to <\/span>analyze<\/span> real-time and historical network traffic. It <\/span>identifies<\/span> patterns, detects anomalies, and sends actionable alerts for potential threats, enhancing the security of both internal and external network traffic.<\/span><\/p>\n<\/div><\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

The post Advanced Network Traffic Analysis: Machine Learning and Its Impact on NTA<\/a> appeared first on Fidelis Security<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

Machine Learning (ML) has revolutionized industries by empowering systems to learn from data, make predictions, automate decisions, and uncover insights\u2014all without the need for explicit programming. With ML, systems can:\u00a0 Learn from data.Analyze data quicklyMake autonomous decisions\u00a0 In network security and cybersecurity, ML and other emerging technologies are crucial for detecting malicious activities such as […]<\/p>\n","protected":false},"author":0,"featured_media":2014,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-2013","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/posts\/2013"}],"collection":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2013"}],"version-history":[{"count":0,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/posts\/2013\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/media\/2014"}],"wp:attachment":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2013"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2013"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2013"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}