Cybersecurity Network Prevention from DDOS Attack in Healthcare System
Ravi Tomar1, Yogesh Awasthi2
1Ravi Tomar, Department of Engineering & Technology, Shobhit Institute of Engineering & Technology, Meerut, India.
2Yogesh Awasthi, Department of Engineering & Technology, Shobhit Institute of Engineering & Technology, Meerut, India.
Manuscript received on August 18, 2020. | Revised Manuscript received on August 27, 2020. | Manuscript published on September 10, 2020. | PP: 329-333 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K78110991120 | DOI: 10.35940/ijitee.K7811.0991120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today’s world of network security, wireless communication attacks such as Distributed Denial of Services (DDoS) attacks are one of the most severe cybercriminal attacks. For the information technology and computer systems, a cyber security rule is required to compel different group as well as businesses to secure their systems and information from cyber-attacks. The occurrence of attacks in the healthcare system is responsible for affecting financial as well as prestige losses the patient. To cyber defense networks from this type of attack, it is essential to design an autonomous detection system by considering some essential countermeasures. Our aim is to detect Distributed Denial of Service (DDoS) attack, which is one of the most commonly present cyber-attacks. This research presented an automatic cybersecurity system against DDoS attacks in healthcare applications. This paper focused on deep learning technology along with the concept of a nature-inspired optimization algorithm to detect the affected node. The designed network is simulated in MATLAB tool and provides better results in terms of Packet Delivery Rate, delay and detection rate with Cuckoo Search (CS) and Artificial Neural Network (ANN) as prevention algorithm. In this paper, author has discussed the importance of the information of the patient data in the healthcare. The detail architecture of the health care information system has also been demonstrated and various security requirement are also been discussed. To analyse the performance of this proposed work, the computed metrices are Throughput %, PDR, Detection Rate and Delay.
Keywords: Cyber network, Healthcare system, Distributed Denial of Services, Cuckoo Search, and Artificial Neural Network.
Scope of the Article: Artificial Neural Network