  The availability of the high-capacity network, massive storage, hardware
virtualization, utility computing, service-oriented architecture leads to high
accessibility of cloud computing. The extensive usage of cloud resources causes
oodles of security controversies. Black-hole & Gray-hole attacks are the
notable cloud network defenseless attacks while they launched easily but
difficult to detect. This research work focuses on proposing an efficient
integrated detection method for individual and collusion attacks in cloud
computing. In the individual attack detection phase, the forwarding ratio
metric is used for differentiating the malicious node and normal nodes. In the
collusion attack detection phase, the malicious nodes are manipulated the
encounter records for escaping the detection process. To overcome this user,
fake encounters are examined along with appearance frequency, and the number of
messages exploits abnormal patterns. The simulation results shown in this
proposed system detect with better accuracy.
