Anomaly Detection
Apex Technology Services will pinpoint vulnerabilities in your infrastructure, process flow and internal security to ensure you are securing your data behind layers of security.
Anomaly detection and behavioral analytics are used to fight Advanced Persistent Threats (APTs) used in cyberespionage from nation-states, hackers, employees, competitors, and others with malicious intent. Current security solutions are not enough.
Expert hackers know how to hide their activities within the everyday noise of networks. They are becoming more sophisticated and have dramatically changed the threat landscape.
To be more proactive against cyberattacks, organizations must detect high-risk activities during the reconnaissance phase. Waiting for external communication through the perimeter is too late and can have disastrous consequences. Detecting and stopping attackers inside the network, requires gathering network data and applying advanced behavioral analytics to recognize normal patterns vs anomalous patterns likely to be the activities of an attacker.
Using our sophisticated and proprietary Apex Analytics Security Platform, organizations gain visibility into anomalous behavior on their networks. No other solution is able to duplicate our results and moreover, it monitors, tracks and classifies risk, allowing the enterprise to identify APTs and automate forensic analysis.
Apex Analytics Security Platform
- Multi-dimensional, multi-model, streaming algorithms
- Not based on any rules, signatures, patterns or heuristics
- High computation efficiency, real-time threat/risk detection
- Unsupervised machine-learning, minimal configuration settings
Continuous Security Threat Assessment
- Segmentation: Limit & monitor the attack surface
- Security Hygiene: Continuous employee-risk analysis
- Advanced Threats: Continuous anomaly detection
Cyber Security & Industrial Internet of Things
- Scalable to very large networks of IP devices
- Not based on rules, signatures or heuristics
- Unsupervised machine learning based on non-linear behavioral clustering using self-organizing maps
Implementation
- Installed in a day, on passive network tap/span port
- Typically off-core switch or network segment
- Small footprint, Virtual VM, platform independent
- Software as a Service model
