Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

ExtraHop Data Shows 150 Percent Increase in Suspicious Network Activity During Peak of SUNBURST Attack

  • Post-disclosure investigations revealed sophisticated attack patterns designed to subvert traditional security controls and detection methods

ExtraHop, the leader in cloud-native network detection and response, released a security report offering an in-depth look at the methods cybercriminals used to evade detection during the months before the SolarWinds SUNBURST exploit was discovered. The report also reveals significant increases in suspicious network activity that went largely ignored due to the privileged and trusted status of SolarWinds within the IT environment. As part of the report, ExtraHop also released an expanded list of over 1,700 SUNBURST indicators of compromise (IOCs) as observed across affected environments protected by Reveal(x), critical information that can help organizations determine if and to what extent they’ve been compromised.

“But because other detection methods weren’t alerting on the activity, it largely went ignored. In this case, the attack was strategically designed to evade those detections, and we can expect more similar attacks to follow. It’s an important reminder that the network doesn’t lie.”

During its own investigation, and through its work with customers to help detect and remediate the SUNBURST exploit, ExtraHop threat researchers found that between late March 2020 and early October 2020, detections of probable malicious activity increased by approximately 150 percent. These detections, which included lateral movement, privilege escalation, and command and control beaconing, evaded the more traditional detection methods like endpoint detection and response (EDR) and antivirus. Activity patterns outlined in the report indicate that the SUNBURST attackers were successful in flying under the radar of these detection methods either by disabling them, or by redirecting their approach before they could be detected.

Recommended AI News: Cornerstone to Bring Learning into the Flow of Work, Powered by Microsoft Viva

“Unfortunately, what we found when investigating SUNBURST is that the activity was actually detected on the network,” said Jeff Costlow, Deputy CISO, ExtraHop. “But because other detection methods weren’t alerting on the activity, it largely went ignored. In this case, the attack was strategically designed to evade those detections, and we can expect more similar attacks to follow. It’s an important reminder that the network doesn’t lie.”

Recommended AI News: ETF Action Announces Major Enhancements To Industry-Leading FinTech Platform

In addition to shedding new light on how the SUNBURST attackers were able to dwell within the network unchecked for so long, the report delves into several case studies on how ExtraHop customers investigated and remediated the exploit within their own environments. The case studies include details on how customers were able to use historical metrics to determine the duration of the compromise, as well as which systems and data may have been impacted.

Recommended AI News: Nokia to Help Deutsche Telekom’s Optical Transport Network Modernization

Related Posts
1 of 29,066

Leave A Reply

Your email address will not be published.