Cisco Sourcefire SNORT and Splunk User Behavior Analytics compete in the cybersecurity domain. Splunk holds the upper hand due to its advanced analytics capabilities offering superior insights.
Features: Cisco Sourcefire SNORT provides real-time network traffic monitoring, a comprehensive intrusion detection system, and an adaptable open-source rule-based alert system. Splunk User Behavior Analytics offers advanced threat detection, comprehensive log analysis, and detailed insights into user actions. It is desirable for situations requiring extended feature sets and sophisticated analytics.
Room for Improvement: Cisco Sourcefire SNORT could improve in areas such as providing more intuitive reporting tools, enhancing user-interface design, and integrating more automated threat intelligence updates. Splunk User Behavior Analytics might focus on reducing deployment complexity, offering more out-of-the-box integrations, and optimizing cost-effectiveness for small to medium-sized enterprises.
Ease of Deployment and Customer Service: Cisco Sourcefire SNORT is known for its easy deployment model and strong community support, allowing quick implementation. Splunk User Behavior Analytics, while requiring more detailed setup due to its complexity, benefits from thorough documentation and professional support, appealing to organizations seeking extensive guidance.
Pricing and ROI: Cisco Sourcefire SNORT has a low initial setup cost, offering ROI through proactive threat mitigation. Splunk User Behavior Analytics requires a higher initial investment but justifies this with significant ROI via enhanced security insights and improved operational efficiency, attracting businesses prioritizing long-term returns and deep analytics.
Snort is an open-source, rule-based, intrusion detection and prevention system. It combines the benefits of signature-, protocol-, and anomaly-based inspection methods to deliver flexible protection from malware attacks. Snort gained notoriety for being able to accurately detect threats at high speeds.
Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.
We monitor all Intrusion Detection and Prevention Software (IDPS) reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.