Biblio

Software Defined Networking (SDN) provides opportunities for flexible and dynamic traffic engineering. However, in current SDN systems, routing strategies are based on traditional mechanisms which lack in real-time modification and less efficient resource utilization. To overcome these limitations, deep learning is used in this paper to improve the routing computation in SDN. This paper proposes Convolutional Deep Reinforcement Learning (CoDRL) model which is based on deep reinforcement learning agent for routing optimization in SDN to minimize the mean network delay and packet loss rate. The CoDRL model consists of Deep Deterministic Policy Gradients (DDPG) deep agent coupled with Convolution layer. The proposed model tends to automatically adapts the dynamic packet routing using network data obtained through the SDN controller, and provides the routing configuration that attempts to reduce network congestion and minimize the mean network delay. Hence, the proposed deep agent exhibits good convergence towards providing routing configurations that improves the network performance.

Mobile ad hoc network (MANET) is a kind of mobile multi-hop network which can transmit data through intermediate nodes, it has been widely used and become important since the growing of the market of Internet of Things (IoT). However, the transmissions on MANET are vulnerable, it usually suffered with many internal or external attacks, and the research on security topics of MANET are becoming more and more hot recently. Blackhole Attack is one of the most famous attacks to MANET. In this paper, we focus on the Blackhole Attack in AODV protocol, and use NS-3 network simulator to study the impact of Blackhole Attack on network performance parameters, such as the Throughput, End-to-End Delay and Packet Loss Rate. We further analyze the changes in network performance by adjusting the number of blackhole nodes and total nodes, and the movement speed of mobile nodes. The experimental results not only reflect the behaviors of the Blackhole Attack and its damage to the network, but also provide the characteristics of Blackhole Attacks clearly. This is helpful to the research of Blackhole Attack feature extraction and MANET security measurement.

Nowadays, Vehicular ad hoc Network as a special class of Mobile ad hoc Network(MANET), provides plenty of services. However, it also brings the privacy protection issues, and there are conflicts between the privacy protection and the services. In this paper, we will propose a privacy protection algorithm based on group signature including two parts, group signature based anonymous verification and batch verification. The anonymous verification is based on the network model we proposed, which can reduce the trust authority burden by dividing the roadside units into different levels, and the batch verification can reduce the time of message verification in one group. We also prove our algorithm can satisfy the demand of privacy protection. Finally, the simulation shows that the algorithm we proposed is better than the BBS on the length of the signature, time delay and packet loss rate.

With the developing understanding of Information Security and digital assets, IT technology has put on tremendous importance of network admission control (NAC). In NAC architecture, admission decisions and resource reservations are taken at edge devices, rather than resources or individual routers within the network. The NAC architecture enables resilient resource reservation, maintaining reservations even after failures and intra-domain rerouting. Admission Control Networks destiny is based on IP networks through its Security and Quality of Service (QoS) demands for real time multimedia application via advance resource reservation techniques. To achieve Security & QoS demands, in real time performance networks, admission control algorithm decides whether the new traffic flow can be admitted to the network or not. Secure allocation of Peer for multimedia traffic flows with required performance is a great challenge in resource reservation schemes. In this paper, we have proposed our model for VoIP networks in order to achieve security services along with QoS, where admission control decisions are taken place at edge routers. We have analyzed and argued that the measurement based admission control should be done at edge routers which employs on-demand probing parallel from both edge routers to secure the source and destination nodes respectively. In order to achieve Security and QoS for a new call, we choose various probe packet sizes for voice and video calls respectively. Similarly a technique is adopted to attain a security allocation approach for selecting an admission control threshold by proposing our admission control algorithm. All results are tested on NS2 based simulation to evalualate the network performance of edge router based upon network admission control in VoIP traffic.