TL;DR:
- Netscout’s Arbor Edge Defense (AED) platform integrates machine learning (ML) techniques for advanced DDoS protection.
- The AED platform, deployed at the network edge, identifies risks and defends against sophisticated DDoS attacks.
- The latest version integrates Netscout’s ATLAS Intelligence Feed, which updates IP addresses associated with bots and amplifiers involved in DDoS attacks.
- ML models trained with ASERT’s expertise automatically detect and stop attacks, enhancing response capabilities.
- AED uses ML for real-time deep packet inspection and building a model of normal network traffic to establish a baseline.
- ML-based analysis enables dynamic attack mitigation, adapting defenses as attack traffic changes.
- Recent DDoS threat reports reveal a shift in attack tactics, with threat actors conducting extensive reconnaissance and employing multivector attacks.
- AED’s adaptive DDoS defense measures have shown up to 90% effectiveness in identifying and stopping attacks.
- Organizations can configure AED to automatically deploy new defenses based on ML-generated recommendations.
Main AI News:
As the threat landscape of distributed denial of service (DDoS) attacks becomes increasingly complex, organizations face a pressing need for advanced systems to safeguard their networks. Netscout, a leading provider of cybersecurity solutions, is addressing this challenge head-on with the latest update to its Arbor Edge Defense (AED) platform, which leverages the power of machine learning (ML). Deployed at the network edge, in front of firewalls, the AED platform combines ML intelligence with Netscout’s threat expertise to proactively identify risks and defend against sophisticated DDoS attacks.
The key highlight of the latest version of AED is its integration of Netscout’s ATLAS Intelligence Feed. This feed gathers intelligence from a staggering 400 terabits per second of international transit traffic, received in real-time from 93 countries, 600 industry verticals, and over 31,000 autonomous systems. With this vast data pool, ATLAS continuously updates the IP addresses associated with bots and amplifiers actively engaged in DDoS attacks worldwide. Netscout’s dedicated security research and DDoS attack mitigation team, ASERT, has utilized this data to train the integrated ML models in AED, enabling them to automatically detect and halt attacks with the same level of precision and speed as if they were managed personally.
Machine learning plays a vital role in enhancing DDoS protection capabilities. Netscout has been incorporating ML into its platforms in various ways, and AED is no exception. For instance, AED employs automated deep packet inspection in real-time using Netscout’s ATLAS Threat Intelligence, which looks for known characteristics of DDoS attacks, such as specific signatures of attack tools and services. ML is also utilized to establish a model of normal network traffic on a per-network segment and per-protocol basis, from which a baseline is derived. By employing statistical analysis and proprietary algorithms, AED can automatically detect DDoS attacks and swiftly apply countermeasures to mitigate their impact.
With the latest release, Netscout has expanded its utilization of ML capabilities within AED. As attacks are mitigated in real-time, AED conducts additional deep packet inspection to gain further insights. Leveraging the real-world experience of ASERT, the system recommends additional countermeasure configurations based on this analysis, allowing customers to fine-tune their attack mitigation strategies dynamically as the attack traffic evolves. This adaptive approach to DDoS protection ensures organizations stay one step ahead of the ever-changing threat landscape.
Netscout’s recent DDoS threat reports have highlighted a shift in the tactics employed by threat actors over the past two years. Extensive reconnaissance of target organizations prior to attacks has become a common practice, with threat actors meticulously probing defenses and assessing the effectiveness of preliminary attacks. If one approach proves ineffective, they swiftly switch methodologies or threat vectors until they achieve their objectives. This probing phase may be spread out over hours or even days, making it increasingly challenging for organizations to defend themselves.
According to the Netscout DDoS Threat Intelligence Report, multivector attacks accounted for over 40% of all DDoS attacks in 2022, with more than a quarter-million of these attacks employing more than 10 vectors. Dubbed dynamic DDoS attacks, these tactics involve frequent and real-time changes in attack strategies, making it arduous for organizations to mount effective defenses. Notably, many of these dynamic attacks avoid ISP-level DDoS defenses by targeting the application layer with lower bandwidth levels.
In the past, AED relied on predetermined lists and settings determined by an organization’s IT or networking teams to block incoming traffic. However, with the latest enhancements, these groups can now configure the system to automatically deploy new defenses in real-time based on ML-generated recommendations. Preliminary tests have demonstrated that AED’s adaptive DDoS defense measures can successfully identify and mitigate up to 90% of DDoS attacks. Nevertheless, Netscout remains vigilant, as attackers persistently innovate new methodologies to circumvent adaptive defenses, an area that the company closely monitors.
Conclusion:
Netscout’s integration of machine learning in its AED platform represents a significant advancement in the field of DDoS protection. By leveraging ML intelligence and adaptive defense measures, Netscout empowers organizations to proactively identify and mitigate DDoS attacks with a remarkable 90% effectiveness rate. This development not only enhances network security but also demonstrates the growing importance of ML-driven solutions in combating the evolving threat landscape. As the market for DDoS protection solutions continues to evolve, businesses that adopt advanced ML-based technologies like Netscout’s AED are better positioned to safeguard their critical assets and stay ahead of attackers.
