Biblio

With the development of network technology, identifying specific traffic has become important in network monitoring and security. However, designing feature sets that can accurately describe network traffic is still an urgent problem. Most of existing researches cannot realize effectively the identification of targets, and don't perform well in the complex and dynamic network environment. Aiming at these problems, we propose a novel method in this paper, which learns correlation features of network traffic based on the hierarchical structure. Firstly, the method learns the spatial-temporal features using convolutional neural networks (CNNs) and the bidirectional long short-term memory networks (Bi-LSTMs), then builds network topology to capture dependency characteristics between sessions and learns the context-related features through the graph attention networks (GATs). Finally, the network traffic session is classified using a fully connected network. The experimental results show that our method can effectively improve the detection ability and achieve a better classification performance overall.

While Ethereum smart contracts enabled a wide range of blockchain applications, they are extremely vulnerable to different forms of security attacks. Due to the fact that transactions to smart contracts commonly involve cryptocurrency transfer, any successful attacks can lead to money loss or even financial disorder. In this paper, we focus on the overflow attacks in Ethereum, mainly because they widely rooted in many smart contracts and comparatively easy to exploit. We have developed EasyFlow, an overflow detector at Ethereum Virtual Machine level. The key insight behind EasyFlow is a taint analysis based tracking technique to analyze the propagation of involved taints. Specifically, EasyFlow can not only divide smart contracts into safe contracts, manifested overflows, well-protected overflows and potential overflows, but also automatically generate transactions to trigger potential overflows. In our preliminary evaluation, EasyFlow managed to find potentially vulnerable Ethereum contracts with little runtime overhead. A demo video of EasyFlow is at https://youtu.be/QbUJkQI0L6o.

In recent years, the issues of RFID security and privacy are a concern. To prevent the tag is cloned, physically unclonable function (PUF) has been proposed. In each PUF-enabled tag, the responses of PUF depend on the structural disorder that cannot be cloned or reproduced. Therefore, many responses need to store in the database in the initial phase of many authentication protocols. In the supply chain, the owners of the PUF-enabled Tags change frequently, many authentication and delegation protocols are proposed. In this paper, a new lightweight authentication and delegation protocol for RFID tags (LADP) is proposed. The new protocol does not require pre-stored many PUF's responses in the database. When the authentication messages are exchanged, the next response of PUF is passed to the reader secretly. In the transfer process of ownership, the new owner will not get the information of the interaction of the original owner. It can protect the privacy of the original owner. Meanwhile, the original owner cannot continue to access or track the tag. It can protect the privacy of the new owner. In terms of efficiency, the new protocol replaces the pseudorandom number generator with the randomness of PUF that suitable for use in the low-cost tags. The cost of computation and communication are reduced and superior to other protocols.