Efficient DDOS Detection in Internet of Medical Things using CNN-ACL Approach
Keywords:
Healthcare, IoMT, DDOS detection, network security, machine learning, CNN-ACLAbstract
Internet of Things (IoT) has made great progress in the field of health care system and has the possibility of revolutionizing technical, sociological, and commercial hopes for a healthier future. The term "IoMT" (Internet of Medical Things) refers to the use of networking technology to connect medical devices and software programs related to health-care data to the Internet. These advancements allow the healthcare industry to continue providing patients with a higher level of attention and care. Despite the many advantages they provide, these gadgets also provide new attack surfaces, raising various security and privacy issues. Attacks against medical instruments that is connected to the Internet have the potential to affect people severely and in a life-threatening way. To guarantee accuracy and patient data confidentiality, this thesis focuses on offering a highly secure method of transmitting medical data via IoMT. This paper proposes the average convolution layer (CNN-ACL), a unique CNN architecture type for learning the content features of anomalous activity and then identifying the individual anomaly. A powerful system for detecting network DDOS that incorporates a cutting-edge convolutional layer capable of teaching low-level anomalous qualities is designed with the aid of the CNN-ACL, which is highly recommended. Experimental results demonstrate that this method's network DDOS detection effectiveness is superior to machine learning methods based on the KDDCUP99 and CICIDS2017 datasets. Python is used to stimulate the study, and the average accuracy may be increased to 91%, with 0.87 precision and 0.86 recall.
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