Security intrusion detection using quantum machine learning techniques

Conventional machine learning approaches applied for the security intrusion detection degrades in case of big data input ( $$10^6$$ and more samples a dataset). Model training computing by traditional executed on at common environment may produce accurate outputs but take long time, or poor accuracy quick training, both disparate to malicious activity. The paper observes quantum (QML) methods overcoming barriers abilities hardware purpose high performance detection. Quantum support vector (QSVM) convolution neural network (QCNN) as concurrent are discussed evaluated comparing conventional detectors running computer. QML-based utilizes our own dataset that implements grouping packets into streams eatable QML. We have developed software solution encodes traffic ready computing. Experimental results show ability processing inputs with (98%) providing twice faster speed algorithms utilized same task.