Machine Learning
Detect Threats Other Solutions Miss
Detecting attacks is essential to protecting business reputations, revenue, and opportunities. Malicious actors often hide from traditional security solutions within network traffic, but finding these attacks is an enormous hurdle for IT security teams.
MENDEL uses advanced teachable unsupervised machine learning as part of its network traffic analysis-based approach to these attacks.
・Faster detection of unknown threats and anomalies
・Reduction of time-to-response for security incidents
・More sensitive and reliable behavioral detection
・Behavioral detection adapts to changing environments
・Less time-demanding administration
MENDEL’s advanced machine learning algorithms apply several unique analytic techniques to each flow, allowing it to identify threats which seek to deceive a basic network performance model; including the ability to detect presence of anomalous devices, communication volume, communication peers. MENDEL is also unique in that it distinguishes between human and machine communication to identify dangerous advanced threats which are hidden within the network, unseen by other security solutions. MENDEL also identifies personally identifying information within network communications, as well as unauthorized access to personal data, making it ideal for those implementing GDPR-focused security postures.
Many analytic security tools rely on pre-set rules or baselines to detect threats. MENDEL independently models the network’s unique traffic patterns from the moment it’s installed. This model adapts as traffic and threats in the network evolve, to effectively pinpoint malicious and anomalous behavior. Based on MENDEL's Advanced Security Network Metrics data, the model is able to identify subtle changes in network traffic caused by malicious actors. This allows it to detect threats more effectively than security tools that use pre-set rules or baselines.
Machine learning is effective in detecting threats, and becomes more effective over time. But it can generate a high number of false positives as it learns, creating challenges for network security teams. Truly effective machine learning can be guided by an analyst. MENDEL's machine learning is advanced on its own, but may also be improved through analyst input, making it a truly effective and useful for the security analyst.
・Faster detection of unknown
threats and anomalies
・Reduction of time-to-response
for security incidents
・More sensitive and reliable
behavioral detection
・Behavioral detection adapts to
changing environments
・Less time-demanding
administration
MENDEL’s advanced machine learning algorithms apply several unique analytic techniques to each flow, allowing it to identify threats which seek to deceive a basic network performance model; including the ability to detect presence of anomalous devices, communication volume, communication peers. MENDEL is also unique in that it distinguishes between human and machine communication to identify dangerous advanced threats which are hidden within the network, unseen by other security solutions. MENDEL also identifies personally identifying information within network communications, as well as unauthorized access to personal data, making it ideal for those implementing GDPR-focused security postures.
Many analytic security tools rely on pre-set rules or baselines to detect threats. MENDEL independently models the network’s unique traffic patterns from the moment it’s installed. This model adapts as traffic and threats in the network evolve, to effectively pinpoint malicious and anomalous behavior. Based on MENDEL's Advanced Security Network Metrics data, the model is able to identify subtle changes in network traffic caused by malicious actors. This allows it to detect threats more effectively than security tools that use pre-set rules or baselines.
Machine learning is effective in detecting threats, and becomes more effective over time. But it can generate a high number of false positives as it learns, creating challenges for network security teams. Truly effective machine learning can be guided by an analyst. MENDEL's machine learning is advanced on its own, but may also be improved through analyst input, making it a truly effective and useful for the security analyst.
・Faster detection of unknown
threats and anomalies
・Reduction of time-to-response
for security incidents
・More sensitive and reliable
behavioral detection
・Behavioral detection adapts to
changing environments
・Less time-demanding
administration
MENDEL’s advanced machine learning algorithms apply several unique analytic techniques to each flow, allowing it to identify threats which seek to deceive a basic network performance model; including the ability to detect presence of anomalous devices, communication volume, communication peers. MENDEL is also unique in that it distinguishes between human and machine communication to identify dangerous advanced threats which are hidden within the network, unseen by other security solutions. MENDEL also identifies personally identifying information within network communications, as well as unauthorized access to personal data, making it ideal for those implementing GDPR-focused security postures.
Many analytic security tools rely on pre-set rules or baselines to detect threats. MENDEL independently models the network’s unique traffic patterns from the moment it’s installed. This model adapts as traffic and threats in the network evolve, to effectively pinpoint malicious and anomalous behavior. Based on MENDEL's Advanced Security Network Metrics data, the model is able to identify subtle changes in network traffic caused by malicious actors. This allows it to detect threats more effectively than security tools that use pre-set rules or baselines.
Machine learning is effective in detecting threats, and becomes more effective over time. But it can generate a high number of false positives as it learns, creating challenges for network security teams. Truly effective machine learning can be guided by an analyst. MENDEL's machine learning is advanced on its own, but may also be improved through analyst input, making it a truly effective and useful for the security analyst.
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