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ATP Advanced Threat Protection

Stop sophisticated threats to prevent breaches and data loss.

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Integration Across the Digital Attack Surface.

The number, variety and sophistication of cyberattacks is increasing on a daily basis. Ransomware, DDoS and brute force attacks form just a snapshot of the types of threats faced by consumers and organisations alike. While the vast majority of known threats can be defended against using traditional security technologies, the real challenge is protecting against previously unseen attacks.

Safeguarding your financial, intellectual and personal data requires an integrated, intelligence-based security fabric to provide end-to-end protection across the organisation. Ensuring that you have a holistic security strategy - combining people, process and advanced technology - will dramatically reduce your exposure to current and future attacks.

Infradata has helped many organisations to protect themselves from advanced threats, specifically relating to DDoS, Behavioural Analytics and Sandboxing.

Sandboxing

Sophisticated cyberattacks often use unknown malware to evade traditional gateway and endpoint protection. These advanced persistent threats (APTs) are custom-developed using evasive coding techniques and their aim is to remain undetected for as long as possible.

One next-generation solution available to help mitigate APTs is sandboxing. A sandbox is a security mechanism which is used to isolate unknown or suspicious programs and applications in a safe environment. Once contained in the sandbox, dubious programs can be executed in order to monitor their purpose without spreading throughout the network.

A sandbox provides a dedicated environment to analyse, understand and take action against threats which have evaded conventional security measures. They should form part of every organisation’s IT security arsenal.

User and Entity Behaviour Analytics (UEBA)

User and Entity Behaviour Analytics (UEBA) uses machine learning and behavioural analytics to spot changes in behaviour which may indicate an insider attack that has evaded perimeter defenses. Together, these AI-based techniques can cross-correlate interactions between users, systems and data to detect insider threats, cyberthreats, fraud and non-compliance.

Events that can look harmless in isolation often map into high-risk threats when analysed in context over time. The most progressive UEBA solutions correlate and analyse events from multiple sources such as user, device, asset, application, and network segment to predict, detect and contain slow-and-low attacks that are invisible to legacy solutions.

Patented unsupervised and supervised machine learning and statistical algorithms profile normal activity and detect anomalies. Some of the key signature-less techniques include mix-max clustering, peer analysis, event rarity analysis, predictive learning, fuzzy correlation, robotic pattern detection, DGA detection and sequential learning.

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