code for DDoS attack detection in cloud
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I need source code for DDoS attack detection in cloud
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: Cloud is becoming a dominant computing platform. Of course, one question that arises is whether we can overcome notorious DDoS attacks in a cloud environment. Researchers have shown that the essential issue of DDoS attack and defense is the resource competition between defenders and attackers. A cloud typically has deep resources, and it has full control and a dynamic allocation capability of its resources. Therefore, the cloud offers us the potential to overcome DDoS attacks. However, individual cloud-hosted servers remain vulnerable to DDoS attacks if they continue to run in the traditional way. In this paper, we propose a dynamic resource allocation strategy to counter DDoS attacks against individual cloud clients. When a DDoS attack occurs, we use inactive cloud resources to clone enough intrusion prevention servers for the victim in order to quickly filter the attack packets and ensure the quality of service for benign users simultaneously. We establish a mathematical model to approximate the needs of our investment in resources based on queuing theory.


Today, WBAN assisted cloud monitoring for patient health has attracted the attention of researchers. In addition to other open issues in the WBAN environment, such as energy efficiency, quality of service and standardization, security and privacy are the key issues that require special attention. Among these security issues, data availability is the most annoying security problem. The Distributed Denial of Service (DDoS) attack is one of the most powerful attacks on the availability of patient health data and services from healthcare professionals. The DDoS attack severely affects the capacity and performance of a WBAN network if it is not handled in a timely and appropriate manner.

To detect a DDoS attack on cloud-assisted WBAN, there is a need for a defensive approach that encompasses network semantics and traffic flow in networks. When a victim node is flooded with lots of packets that exceeds its processing capacity, the excess must be eliminated. The packet-based discard strategy helps distinguish legitimate traffic from flood traffic and is used to prevent the impact of attack traffic on legitimate users. Observing the network traffic flow shows that there is no regular pattern structure in the network and therefore statistical pattern identification techniques are needed. Integrating the existing attack detection and defense mechanism into a resource constrained WBAN network increases the computation and communication costs.

Network resources are not enough to mitigate the huge amount of traffic generated by the DDoS attack. Therefore, there is a need for an approach that is lightweight and capable of handling real-time transmission data. For this research, data mining techniques have been studied and explored. Among data mining techniques, VFDT has been shown to be the most frequent because of the simplicity and interpretability of its rules and therefore considered more suitable for low power sensor networks. The underlying reasons for VFDT selection are as follows: (1) it is light; That is, it does not require a dataset to be stored in memory, so it is suitable for restricting WBAN resources; (2) can progressively build the decision tree from scratch which helps in detecting the DDoS attack at any stage; (3) each time a new segment of sensor data arrives, a test and training process is performed on the sensor, keeping the stored data updated; (4) does not require reading the complete dataset and yet adjusts the decision tree according to the new incoming and assembled statistical attributes, consuming less memory space; (5) is suitable for a large amount of non-stationary and data transmission data obtained from WBAN sensors; (6) provides a transparent learning process. These features make VFDT a suitable candidate to implement a stand-alone decision-maker for the detection of DDoS attacks in cloud-assisted WBAN.
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