论文标题
云计算中勾结的黑洞和灰孔攻击的检测
Detection of Colluded Black-hole and Grey-hole attacks in Cloud Computing
论文作者
论文摘要
高容量网络的可用性，大量存储，硬件虚拟化，实用程序计算，面向服务的体系结构可导致云计算的高可访问性。云资源的广泛用法引起了安全争议的困扰。黑洞和灰孔攻击是众所周知的云网络无防御攻击的攻击，而它们易于启动但难以检测。这项研究工作着重于为云计算中的个体和勾结攻击提出一种有效的集成检测方法。在单个攻击检测阶段，转发比率度量用于区分恶意节点和正常节点。在勾结攻击检测阶段，恶意节点被操纵以逃避检测过程的遇到记录。为了克服该用户，检查了假遭遇以及外观频率以及消息的数量利用异常模式。该提出的系统中显示的仿真结果以更好的精度检测。
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.
