NETSCOUT, a leading provider of cybersecurity solutions, has announced that it is utilizing machine learning algorithms to combat distributed denial-of-service (DDoS) attacks. These attacks have become increasingly sophisticated as cybercriminals adapt their tactics to exploit vulnerabilities in defenses. NETSCOUT’s Arbor Edge Defense (AED) platform is an advanced system installed alongside firewalls and other cybersecurity infrastructure. It continuously monitors network traffic for signs of DDoS attacks.
Traditionally, cybercriminals would launch DDoS attacks and hope for success. However, modern attackers now closely monitor the effectiveness of their attacks and adjust their techniques accordingly. They conduct extensive reconnaissance to identify weaknesses in target defenses before launching attacks. To counter this, NETSCOUT has introduced machine learning algorithms that analyze network packets, both inbound and outbound, in real-time. These algorithms detect shifts in network behavior and provide mitigation recommendations to cybersecurity teams. This allows teams to either address vulnerabilities or strengthen other elements of their cybersecurity defenses.
NETSCOUT boasts an extensive Atlas cybersecurity sensor network that spans over 500 internet service providers (ISPs) and analyzes 400 Tbps of network traffic from 93 countries. Through its ASERT analytics application, it tracks 50% of all internet traffic and DDoS attack activity in real-time, providing intelligence feeds to update AED instances.
DDoS attacks, which can disrupt internet services for entire countries or specific organizations, have seen a significant increase in recent years. Activists often utilize these attacks to further their cause. Furthermore, cybercriminals are now offering their services to anyone interested, making DDoS attacks more accessible and frequently used as a diversion to distract cybersecurity teams from detecting more targeted attacks.
Defending against DDoS attacks has become increasingly resource-intensive for organizations, as they divert limited resources from defending against other attack vectors. This has created a challenging situation, as cybercriminals often have access to greater resources compared to enterprise IT organizations that must constantly prioritize cybersecurity defense tactics.
While there is hope that ISPs and telecommunications carriers will be able to intervene and prevent DDoS attacks in the future, current solutions rely on machine learning algorithms and artificial intelligence (AI) to level the playing field. These technologies aim to enhance the capabilities of cybersecurity teams in detecting and mitigating DDoS attacks.
In conclusion, NETSCOUT’s implementation of machine learning algorithms in its AED platform demonstrates a commitment to combatting the increasing threat of DDoS attacks. By analyzing network traffic and providing real-time insights, cybersecurity teams can better defend against these attacks. Although the battle against cybercriminals continues, advancements in AI and machine learning offer hope in overcoming this persistent challenge.
