Which security analytics platforms provide machine learning capabilities for anomaly detection?

Machine learning is a game-changer for security analytics. By crunching massive data sets, ML algorithms learn the difference between normal activity and potential threats. This continuous learning allows them to adapt to new security risks. Security platforms that leverage ML offer real-time insights and even predict future attacks, enabling proactive defense. Common ML techniques include supervised learning with tagged data and unsupervised learning finding patterns in untagged data for anomaly detection and it can classify threats, predict security incidents, and even automate response actions. Challenges in ML models can generate false alarms, and their decision-making process can be complex to understand.

ML - learn prior behavior on key fields or combinations - is an underpinning of multiple tools (Securonix, Exabeam, Rapid7 Insight IDR, and possible in Google SECOPS/RA and Sentinel with KQL). Early simple versions were simply summary tables like ArcSight trends, and LogRythm LogMart. I find most descriptions of ML over complicated versus what is used by a SOC team. Has the user/machine ever done this? in that quantity? Do other similar users/machines do this?

Why did the security analytics platform bring a data scientist to the party? For some anomaly detection dance moves! I've been diving into security analytics platforms lately, and it's fascinating how some leverage machine learning for anomaly detection.

Azure Stream Analytics offers machine learning-based anomaly detection functions, which are now generally available. These functions can detect various types of anomalies in real-time data streams, such as spikes, dips, and trends AI Anomaly Detector by Microsoft Azure provides a robust anomaly detection system that can be used to monitor and detect unusual patterns or rare events in data, helping to quickly identify potential issues

SIEM Platforms: McAfee SIEM, Splunk, Palo Alto Cortex XDR - centralize logs & use ML to identify deviations from normal user/device behavior. Endpoint Detection & Response (EDR): Crowdstrike Falcon, SentinelOne - focus on endpoint data & leverage ML to detect malware & suspicious activities. User and Entity Behavior Analytics (UEBA): MacAfee UEBA, IBM QRadar User Behavior Analytics - analyze user & entity activity across systems for anomalies suggesting breaches.

So far, most anomalous detection is going to involve user activity, whether that be a sign in from an unusual location or within an unusual time frame, an unexpected activity taken on by a user such as connection to an asset they have never reached before, or even the execution of 'fly by night' software that doesn't require administrator permissions. However, you will also encounter ML systems provide information on unusual tasks by relatively normal software such as egress of large amounts of data through a browser, which might identify potential espionage, or an unexpected registry modification on an otherwise average device. All of these are extremely helpful in identifying potential vulnerabilities or infiltrations actions.

Sophos Optix and Network Detection and Response (NDR) products leverage machine learning capabilities for advanced anomaly detection, providing robust security analytics. Sophos Optix utilizes machine learning to continuously monitor cloud environments, establishing a baseline of normal activity and detecting deviations that may signify security threats. Its ability to identify unusual patterns in user behavior and network traffic helps uncover potential breaches and misconfigurations in real-time. Similarly, Sophos NDR applies machine learning to network traffic analysis, identifying anomalies that could indicate malicious activities such as lateral movement, data exfiltration, or unauthorized access.

Among other platforms, Splunk Enterprise Security offers advanced analytics capabilities, like machine learning-based anomaly detection. It can analyze large volumes of security data in real time to detect unusual patterns or behaviors that may indicate potential threats.

Splunk: Splunk offers an integrated security platform with ML-powered anomaly detection capabilities. Its User Behavioral Analytics (UBA) module allows you to define baselines for user behavior and leverage machine learning to identify deviations that might indicate suspicious activity. Elastic Security: Built on the open-source Elasticsearch platform, It analyzes security data from various sources and identifies unusual patterns that could signify potential threats. McAfee Enterprise Security Manager: This platform incorporates machine learning for anomaly detection within its SIEM capabilities by analyzing security events, user activity, and network traffic to detect anomalies that might signal security incidents.

Machine learning enhances anomaly detection by identifying unusual patterns in large datasets, adapting to new threats, and improving accuracy over time, making it essential for effective cybersecurity and fraud prevention. Splunk and IBM QRadar are popular platforms that provide machine learning capabilities for anomaly detection.