DDoS Protection

Machine Learning Architecture

Uses machine learning architecture to study the behavior of data packets.

Ease integration with third-party DDoS mitigation services to protect your organization from large-scale DDoS attacks without limiting your deployment options.

Traditional DDoS Attack Prevention is typically comprised of Firewalls and hardware-based Intrusion Detection Systems (IDS) which are not equipped to handle the multi-vector and sneaky DDoS attacks of today.

Distributed Denial of Service (DDoS)

Machine Learning Detection

Machine learning architecture to study the behavior of data packets and blocks anomalous activities, protecting your site or application

Autonomous Mitigation

Provides you with autonomous mitigation, which means there is no need for a member of your IT team to intervene during an attack.

Continuous Attack Evaluation

Continuous evaluation of the attack surface and detect threat changes to network traffic to mitigate threats, protecting your site or application.

Advanced NTP Protection

Performs 100% inspection of every Network Time Protocol (NTP) query and response at a rate as high as 6 million QPS.

How Does Automation Help Mitigate DDoS Attacks?

Distributed Denial of Service (DDoS) attacks pose a serious threat to the security of IoT. Attackers can easily exploit the vulnerabilities of IoT devices and control them as part of botnets to launch DDoS attacks

Application Layer Attacks

These focus on overwhelming a specific application or service, often by exploiting vulnerabilities in the application. Examples include HTTP floods and DNS amplification attacks.

Protocol-Based Attacks

These exploit vulnerabilities in network protocols. An example is a SYN/ACK attack, which exploits the three-way handshake in TCP connections.

Internet of Things (IoT)

Discovers current cloud application or infrastructure security settings and suggests modifications to improve security based on industry standards such as the Center for Internet Security (CIS) benchmarks.

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