NEW! Cyberseer Paper:
Applying Machine Learning to Cyber Security
Defending your company’s assets is complex; it only takes one slip up for a security incident to take place. The current lack of available talent and increasing workload has resulted in Security Analysts being overwhelmed. Attackers on the other hand, have upped their game to employ multi-stage, sophisticated techniques.
Conducting large scale analysis to find the proverbial needle in a haystack needn’t be a challenge. Machine learning can emulate the same manual process of a Security Analyst by using powerful algorithms to create models that learn patterns of behaviour, offering automated analysis of unusual or risky activities. This is critical to regaining some control over the ocean of data that is generated and minimising the “Dwell Time” a threat goes undetected within the target environment.
A machine learning approach:
- Automates numerous tasks
- Gains collaborative visibility into the core of your network
- Targets real issues in real time before damage is caused
- Identifies new emerging threats that can evade human generated signatures
- Flags up unusual behaviour worthy of further investigation
- Manages third party information security risks at suppliers and partners
- Protects against insider threat.
Avoiding 100% of attacks is unachievable, but the attack dwell time can be minimised. Machine learning can improve the security posture of your organisation and help detect both advanced threat and malicious insider activities.
Review this paper to learn:
- What Machine Learning approaches are available?
- Which approach is the right fit for your organisation?
- How it can help your overstretched resources