The increasing volume of traffic (especially from IoT devices) is posing a challenge to the current anomaly detection systems. Existing systems are forced to take the support of the control plane for a more thorough and accurate detection of malicious traffic (anomalies). This introduces latency in making decisions regarding fast incoming traffic and therefore, existing systems are unable to scale to such growing rates of traffic. In this paper, we propose CyberSentinel, a high throughput and accurate anomaly detection system deployed entirely in the programmable switch data plane; making it the first work to accurately detect anomalies at line speed. To detect unseen network attacks, CyberSentinel uses a novel knowledge distillation scheme that incorporates "learned" knowledge of deep unsupervised ML models (\textit{e.g.}, autoencoders) to develop an iForest model that is then installed in the data plane in the form of whitelist rules. We implement a prototype of CyberSentinel on a testbed with an Intel Tofino switch and evaluate it on various real-world use cases. CyberSentinel yields similar detection performance compared to the state-of-the-art control plane solutions but with an increase in packet-processing throughput by $66.47\%$ on a $40$ Gbps link, and a reduction in average per-packet latency by $50\%$.