The Experts below are selected from a list of 519 Experts worldwide ranked by ideXlab platform

Yue Zong - One of the best experts on this subject based on the ideXlab platform.

  • an efficient sdn based ddos attack detection and rapid response platform in vehicular networks
    IEEE Access, 2018
    Co-Authors: Lei Guo, Ye Liu, Jian Zheng, Yue Zong
    Abstract:

    With the prosperity of wireless networks, vehicular networks (VNs) have been extensively studied in recent years. It is deployed to ensure road safety, enhance the driving experience, and reduce traffic congestion. However, VNs are vulnerable to various attacks, especially Distributed Denial of Service (DDoS) that attackers control a large number of compromise nodes inside the networks to occupy the network resources of legitimate users and impact the communication among vehicles and between vehicles and infrastructure. In this paper, we design a platform to efficiently detect and rapidly respond to the DDoS attack in VNs based on software-defined networking (SDN). The proposed platform not only contains the trigger mechanism based on the message of OpenFlow protocol (i.e., PACKET_IN message) for a response not timely but also involves a Flow feature extraction strategy based on the multi-dimensional information. Moreover, we construct an effective global network Flow Table feature values based on OpenFlow Flow Table feature and the entropy feature of Flow Table Entry. We determine all Flow Table Entry by the trained SVM. By analyzing the simulation results, we verify that the detection scheme effectively reduces the time for starting attack detection and classification recognition and has a lower false alarm rate.

Lei Guo - One of the best experts on this subject based on the ideXlab platform.

  • an efficient sdn based ddos attack detection and rapid response platform in vehicular networks
    IEEE Access, 2018
    Co-Authors: Lei Guo, Ye Liu, Jian Zheng, Yue Zong
    Abstract:

    With the prosperity of wireless networks, vehicular networks (VNs) have been extensively studied in recent years. It is deployed to ensure road safety, enhance the driving experience, and reduce traffic congestion. However, VNs are vulnerable to various attacks, especially Distributed Denial of Service (DDoS) that attackers control a large number of compromise nodes inside the networks to occupy the network resources of legitimate users and impact the communication among vehicles and between vehicles and infrastructure. In this paper, we design a platform to efficiently detect and rapidly respond to the DDoS attack in VNs based on software-defined networking (SDN). The proposed platform not only contains the trigger mechanism based on the message of OpenFlow protocol (i.e., PACKET_IN message) for a response not timely but also involves a Flow feature extraction strategy based on the multi-dimensional information. Moreover, we construct an effective global network Flow Table feature values based on OpenFlow Flow Table feature and the entropy feature of Flow Table Entry. We determine all Flow Table Entry by the trained SVM. By analyzing the simulation results, we verify that the detection scheme effectively reduces the time for starting attack detection and classification recognition and has a lower false alarm rate.

Ye Liu - One of the best experts on this subject based on the ideXlab platform.

  • an efficient sdn based ddos attack detection and rapid response platform in vehicular networks
    IEEE Access, 2018
    Co-Authors: Lei Guo, Ye Liu, Jian Zheng, Yue Zong
    Abstract:

    With the prosperity of wireless networks, vehicular networks (VNs) have been extensively studied in recent years. It is deployed to ensure road safety, enhance the driving experience, and reduce traffic congestion. However, VNs are vulnerable to various attacks, especially Distributed Denial of Service (DDoS) that attackers control a large number of compromise nodes inside the networks to occupy the network resources of legitimate users and impact the communication among vehicles and between vehicles and infrastructure. In this paper, we design a platform to efficiently detect and rapidly respond to the DDoS attack in VNs based on software-defined networking (SDN). The proposed platform not only contains the trigger mechanism based on the message of OpenFlow protocol (i.e., PACKET_IN message) for a response not timely but also involves a Flow feature extraction strategy based on the multi-dimensional information. Moreover, we construct an effective global network Flow Table feature values based on OpenFlow Flow Table feature and the entropy feature of Flow Table Entry. We determine all Flow Table Entry by the trained SVM. By analyzing the simulation results, we verify that the detection scheme effectively reduces the time for starting attack detection and classification recognition and has a lower false alarm rate.

Jian Zheng - One of the best experts on this subject based on the ideXlab platform.

  • an efficient sdn based ddos attack detection and rapid response platform in vehicular networks
    IEEE Access, 2018
    Co-Authors: Lei Guo, Ye Liu, Jian Zheng, Yue Zong
    Abstract:

    With the prosperity of wireless networks, vehicular networks (VNs) have been extensively studied in recent years. It is deployed to ensure road safety, enhance the driving experience, and reduce traffic congestion. However, VNs are vulnerable to various attacks, especially Distributed Denial of Service (DDoS) that attackers control a large number of compromise nodes inside the networks to occupy the network resources of legitimate users and impact the communication among vehicles and between vehicles and infrastructure. In this paper, we design a platform to efficiently detect and rapidly respond to the DDoS attack in VNs based on software-defined networking (SDN). The proposed platform not only contains the trigger mechanism based on the message of OpenFlow protocol (i.e., PACKET_IN message) for a response not timely but also involves a Flow feature extraction strategy based on the multi-dimensional information. Moreover, we construct an effective global network Flow Table feature values based on OpenFlow Flow Table feature and the entropy feature of Flow Table Entry. We determine all Flow Table Entry by the trained SVM. By analyzing the simulation results, we verify that the detection scheme effectively reduces the time for starting attack detection and classification recognition and has a lower false alarm rate.

Mishra, Ajay Kumar - One of the best experts on this subject based on the ideXlab platform.

  • SDN Based Network Management for Enhancing Network Security
    2020
    Co-Authors: Mishra, Ajay Kumar
    Abstract:

    Traditional network management techniques consists many vulnerabilities and are less viable to maintain the privacy of user's data. In this direction, Centralised management system has been emerged as efficient way to manage the network. Software Defined Network (SDN) is the dynamic and easily programmable networking system where the software manages the data Flow and maintains the continuity of the networks. SDN separates the data-plane and control-plane in order to manage the network efficiently and enhances the network security. The proposed Smart-Net system where each router maintains a Flow Table Entry, if the Entry existed in Flow Table the packet will be forwarded to SDN controller. SDN controller checks for the unusual behaviour in the system and takes the preventive actions in order to stop security attacks such as Sniffing attack, re-directional attack, Denial of Service attack. This paper also encompasses a comparative analysis between Traditional networks and SDN based networks with predefined topology