A Secure DHCP Protocol to Mitigate LAN Attacks

Network security has become more of a concern with the rapid growth and expansion of the Internet. While there are several ways to provide security in the application, transport, or network layers of a network, the data link layer (Layer 2) security has not yet been adequately addressed. Data link layer protocols used in local area networks (LANs) are not designed with security features. Dynamic host configuration protocol (DHCP) is one of the most used network protocols for host configuration that works in data link layer. DHCP is vulnerable to a number of attacks, such as the DHCP rouge server attack, DHCP starvation attack, and malicious DHCP client attack. This work introduces a new scheme called Secure DHCP (S-DHCP) to secure DHCP protocol. The proposed solution consists of two techniques. The first is the authentication and key management technique that is used for entities authentication and management of security key. It is based on using Diffie-Hellman key exchange algorithm supported by the difficulty of Elliptic Curve Discrete Logarithm Problem (ECDLP) and a strong cryptographic one-way hash function. The second technique is the message authentication technique, which uses the digital signature to authenticate the DHCP messages exchanged between the clients and server.

Deep 3D mesh watermarking with self-adaptive robustness

Robust 3D mesh watermarking is a traditional research topic in computer graphics,which provides an efficient solution to the copyright protection for 3D meshes.Traditionally,researchers need manually design watermarking algorithms to achieve suffcient robustness for the actual application scenarios.In this paper,we propose the first deep learning-based 3D mesh watermarking network,which can provide a more general framework for this problem.In detail,we propose an end-to-end network,consisting of a watermark embedding sub-network,a watermark extracting sub-network and attack layers.We employ the topology-agnostic graph convolutional network(GCN)as the basic convolution operation,therefore our network is not limited by registered meshes(which share a fixed topology).For the specific application scenario,we can integrate the corresponding attack layers to guarantee adaptive robustness against possible attacks.To ensure the visual quality of watermarked 3D meshes,we design the curvature consistency loss function to constrain the local geometry smoothness of watermarked meshes.Experimental results show that the proposed method can achieve more universal robustness while guaranteeing comparable visual quality.