QUANTUM-ADAPTIVE NEURAL DEFENSE: A SELF-LEARNING DDOS MITIGATION FRAMEWORK USING ENTROPY-BASED TRAFFIC FINGERPRINTING AND REAL-TIME BEHAVIORAL ANALYSIS
Keywords:
DDoS mitigation, distributed denial of service, quantum-inspired computing, adaptive neural networks, entropy analysis, traffic fingerprinting, real-time machine learning, network security, attack detection, anomaly detection.Abstract
DDoS attacks are getting smarter and use more than one method, which makes it hard for regular defenses to work. Modern mitigation systems have three big problems: they give a lot of false positives when there are real traffic bursts, they can't learn new attack patterns, and they make the user experience worse by adding a lot of latency. This paper introduces an innovative architecture known as QAND (Quantum-Adaptive Neural Defense), which utilizes adaptive neural filtering, quantum-inspired randomization, and entropy-based traffic fingerprinting to tackle these difficulties.
There have been three big updates to QAND. To find granular attack detection, a multi-dimensional entropy fingerprinting approach looks at six traffic parameters at simultaneously. The source IP distribution, the destination port patterns, the packet size variation, the inter-arrival length, the TCP flag combinations, and the payload randomization are all examples of these parameters. Second, a quantum-inspired randomness generator that updates every 100 microseconds can produce 2^128 different defense setups. This stops opponents from employing reconnaissance to figure out how the system operates. Third, an adaptive neural filter can change in real time without having to go through offline retraining cycles. It does this by using gradient descent for continuous online learning, which is limited by a memory buffer.
We put QAND to the test in the real world with 40Gbps of traffic and attacks including DNS amplification, SYN floods, HTTP floods, UDP floods, and Slowloris attacks. The trials showed that 97.3% of the traffic was correctly recognized under long-term 30Gbps attacks, with a false positive rate of 0.8%. The average latency was about 2.1 milliseconds. When there is a lot of traffic, QAND is 3.1–5.8% more accurate, has 1.3–3.6 times fewer false positives, and has 2.8–21.4 times less latency than other academic systems and commercial solutions like Cloudflare, F5 Advanced WAF, and Imperva. The CPU is consuming 68% of its power at 40Gbps, while the RAM is using 4.9GB, which means that it performs well in production.
