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TRANSMISSION 001 • 2017

Mirai Botnet & IoT DDoS Defense

Distributed Denial of Service (DDoS) attacks and IoT Security — Mirai Botnet vs Cloudflare Orbit mitigation.

Co-authored with Eric Wang • University of Technology Sydney (UTS) • BSc IT (Networking & Security)
ABSTRACT

In late 2016, the Mirai botnet emerged as one of the most significant threats to internet infrastructure, leveraging compromised Internet of Things (IoT) devices to launch massive distributed denial-of-service (DDoS) attacks. This research, conducted at the University of Technology Sydney (UTS), analyzes the Mirai malware's architecture, its exploitation of weak default credentials on IP cameras, DVRs, and routers, and the resulting outages against high-profile targets including Krebs on Security and Dyn DNS.

The study evaluates traditional mitigation strategies and introduces Cloudflare Orbit as an innovative virtual patching mechanism. Orbit allows vulnerable IoT devices to be protected at the edge without requiring firmware updates or heavy local compute — effectively shielding devices that manufacturers have abandoned.

KEY FINDINGS
  • • Mirai primarily targets devices with default or weak telnet credentials
  • • Botnet propagation is extremely rapid once a device is compromised
  • • Many devices cannot be patched due to end-of-life status
  • • Edge-based virtual patching (Orbit) provides immediate protection
  • • Research co-authored with Eric Wang as part of UTS BSc IT (Networking & Security)
RECOMMENDATIONS
  • • Change default credentials on all IoT devices
  • • Segment IoT devices on isolated networks
  • • Deploy edge security layers (e.g. Cloudflare Orbit)
  • • Monitor for anomalous outbound traffic from consumer devices
  • • Align with professional certifications in cybersecurity and cloud
“Security is just an illusion.”
— ROBERT JOODAT
Research Archive

The original paper and supporting materials from the 2017 UTS study. This work contributed to early discussions around IoT security and edge-based DDoS mitigation strategies.

PUBLISHED AS PART OF RJ-NEXUS RESEARCH ARCHIVE • UTS 2017