- #BEST DDOSING PROGRAM 2016 UPDATE#
- #BEST DDOSING PROGRAM 2016 FULL#
- #BEST DDOSING PROGRAM 2016 SOFTWARE#
#BEST DDOSING PROGRAM 2016 SOFTWARE#
While ensuring network services and reducing deployment costs, the software defined network enhances the quality of user experience and facilitates the promotion of the whole network deployment. The SDN has the capability of perceived control of the global visualization view, flexible and schedulable rapid deployment capability, and service open intelligent scheduling capability.
#BEST DDOSING PROGRAM 2016 UPDATE#
It supports quick response and update of traffic policies and rules.
#BEST DDOSING PROGRAM 2016 FULL#
Under the innovative architecture environment of SDN, deep packet analysis is available through the full network view. The latter is mainly to establish traffic model and analysis of abnormal flow changes, to determine whether the traffic is abnormal or not, so as to detect whether the server was attacked. The main implementation methods are characteristics match, model reasoning, state transition, and expert systems. By comparing and analyzing the data information of the current network data packet and characteristics database, we can judge whether it is attacked by DDoS or not. The former mainly collects all kinds of characteristics information related to the attack and establishes a characteristics database of DDoS attack. In the traditional network architecture, the main methods of DDoS attack detection technology can be divided into attack detection based on traffic characteristics and attack detection based on traffic anomaly. The difficulties of DDoS attack detection are as follows: (1) the attack traffic characteristics not being easy to identify (2) the lack of collaboration between the coherent network nodes (3) the change of the attack tool being strengthened, with the threshold of its use decreasing (4) the widely used address fraud making it difficult to trace the source of the attack (5) the duration time of attack being short and response time being limited. DDoS attacks show the increasing scale of attack the attack mode is more intelligent. Network attackers attack network bandwidth, system resources, and application resources, to achieve the effect of denial of service attacks. SDN is an emerging network innovation architecture that separates the network data plane and the control plane, which has the characteristics of network programmable, centralized management control, and interface opening. It is a key research topic in the security field to detect DDoS attacks accurately and quickly. DDoS attacks are one of the serious network security threats facing the Internet. The emergence of DDoS attacks can lead to abnormalities in the related network services, causing huge economic losses and even causing other catastrophic consequences.
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With the continuous development of network technology, the ceaseless expansion of network business needs, and rapid growth of the Internet economy in the Internet age, the services of network with important business and industry information have been spread to the production and life of current society.
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Our work is of good value for the detection of DDoS attack in SDN. The experiments show that average accuracy rate of our method is with a small amount of flow collecting. In this paper, the SDN environment by mininet and floodlight (Ning et al., 2014) simulation platform is constructed, 6-tuple characteristic values of the switch flow table is extracted, and then DDoS attack model is built by combining the SVM classification algorithms. However, the existing methods such as neural network algorithm are not practical enough to be applied. The occurrence of software defined network (SDN) (Zhang et al., 2018) brings up some novel methods to this topic in which some deep learning algorithm is adopted to model the attack behavior based on collecting from the SDN controller. The detection of DDoS attacks is an important topic in the field of network security.