Improved security in cloud using sandpiper and extended equilibrium deep transfer learning based intrusion detection

摘要

Cloud computing (CC) offers various types of services for the users and it is also termed on-demand computing. Because of its increasing popularity, it is vulnerable to a variety of intruders who could compromise the integrity and privacy of data stored in the cloud. Due to its distributed nature, security is the most challenging one in the cloud solution. Privacy and security are the major problems in its victory of the on-demand service, but it is simply vulnerable to intruders for any kind of attack. To solve this problem, IDSs (intrusion detection systems) play a major task in identifying the threats on cloud infrastructure. This paper develops an efficient cloud IDS using the sandpiper-based feature selection and extended equilibrium deep transfer learning (EEDTL) classification to improve the overall security of a cloud-based computing environment. The number of features is reduced from the given intrusion dataset based on the sandpiper optimization algorithm (SOA) while maintaining the minimal loss of information. Finally, the EEDTL model is used for the classification of different attacks based on their selected optimal features. For fine-tuning the attributes in convolution layers, transfer learning uses a pre-trained network called AlexNet. Also, the extended equilibrium optimizer (EEO) is used to update the network weights. The proposed cloud IDS effectively classify whether the network traffic behavior is normal or attack. The proposed system is executed in python using the UNSW-NB15 dataset, and NSL-KDD dataset. The various evaluation metrics are used to show the efficiency of the proposed method and compared to the existing works. The simulation results show that the proposed method can able to detect intrusions with a high detection rate and a low false alarm rate (FAR) than other approaches.