Visible to the public Intrusion Detection and Prevention System Using Cuckoo Search Algorithm with ANN in Cloud Computing

TitleIntrusion Detection and Prevention System Using Cuckoo Search Algorithm with ANN in Cloud Computing
Publication TypeConference Paper
Year of Publication2020
AuthorsGupta, Anushikha, Kalra, Mala
Conference Name2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)
Date PublishedNov. 2020
ISBN Number978-1-7281-7132-6
KeywordsANN (Artificial Neural Network), Artificial neural networks, cloud computing, composability, cuckoo search algorithm, DDoS Attack Prevention, delays, energy consumption, Handheld computers, Human Behavior, IDS (Intrusion Detection System), Intrusion detection, Metrics, PDR (Packet Delivery Ratio), pubcrawl, resilience, Resiliency, Servers
AbstractThe Security is a vital aspect of cloud service as it comprises of data that belong to multiple users. Cloud service providers are responsible for maintaining data integrity, confidentiality and availability. They must ensure that their infrastructure and data are protected from intruders. In this research work Intrusion Detection System is designed to detect malicious server by using Cuckoo Search (CS) along with Artificial Intelligence. CS is used for feature optimization with the help of fitness function, the server's nature is categorized into two types: normal and attackers. On the basis of extracted features, ANN classify the attackers which affect the networks in cloud environment. The main aim is to distinguish attacker servers that are affected by DoS/DDoS, Black and Gray hole attacks from the genuine servers. Thus, instead of passing data to attacker server, the server passes the data to the genuine servers and hence, the system is protected. To validate the performance of the system, QoS parameters such as PDR (Packet delivery rate), energy consumption rate and total delay before and after prevention algorithm are measured. When compared with existing work, the PDR and the delay have been enhanced by 3.0 %and 21.5 %.
Citation Keygupta_intrusion_2020