Visible to the public Biblio

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2020-10-29
Sajyth, RB, Sujatha, G.  2018.  Design of Data Confidential and Reliable Bee Clustering Routing Protocol in MANET. 2018 International Conference on Computer Communication and Informatics (ICCCI). :1—7.
Mobile ad hoc network (MANET) requires extraneous energy effectualness and legion intelligence for which a best clustered based approach is pertained called the “Bee-Ad Hoc-C”. In MANET the mechanism of multi-hop routing is imperative but may leads to a challenging issue like lack of data privacy during communication. ECC (Elliptical Curve Cryptography) is integrated with the Bee clustering approach to provide an energy efficient and secure data delivery system. Even though it ensures data confidentiality, data reliability is still disputable such as data dropping attack, Black hole attack (Attacker router drops the data without forwarding to destination). In such cases the technique of overhearing is utilized by the neighbor routers and the packet forwarding statistics are measured based on the ratio between the received and forwarded packets. The presence of attack is detected if the packet forwarding ratio is poor in the network which paves a way to the alternate path identification for a reliable data transmission. The proposed work is an integration of SC-AODV along with ECC in Bee clustering approach with an extra added overhearing technique which n on the whole ensures data confidentiality, data reliability and energy efficiency.
2015-04-30
Nikolai, J., Yong Wang.  2014.  Hypervisor-based cloud intrusion detection system. Computing, Networking and Communications (ICNC), 2014 International Conference on. :989-993.

Shared resources are an essential part of cloud computing. Virtualization and multi-tenancy provide a number of advantages for increasing resource utilization and for providing on demand elasticity. However, these cloud features also raise many security concerns related to cloud computing resources. In this paper, we propose an architecture and approach for leveraging the virtualization technology at the core of cloud computing to perform intrusion detection security using hypervisor performance metrics. Through the use of virtual machine performance metrics gathered from hypervisors, such as packets transmitted/received, block device read/write requests, and CPU utilization, we demonstrate and verify that suspicious activities can be profiled without detailed knowledge of the operating system running within the virtual machines. The proposed hypervisor-based cloud intrusion detection system does not require additional software installed in virtual machines and has many advantages compared to host-based and network based intrusion detection systems which can complement these traditional approaches to intrusion detection.