Quality-of-Service (QoS) guarantees are crucial for meeting the diverse performance requirements of applications in packet networks. The Proportional Delay Differentiation (PDD) model offers relative service differentiation based on the delay requirements of different traffic classes. However, implementing PDD on current hardware switches faces challenges due to the lack of inherent queuing behavior description in switch ASICs. This paper introduces FlexPDD, a dynamic and adaptive packet prioritization mechanism designed to implement the PDD model on programmable switches. FlexPDD leverages the flexibility of programmable switch to adjust the mapping between packet classes and output queues dynamically, ensuring precise control over delay differentiation. Our implementation of FlexPDD on a Barefoot Tofino switch and an NS3 simulator demonstrates its feasibility and effectiveness. The results indicate that FlexPDD successfully maintains approximate delay differentiation among service classes proportional to their delay weights, highlighting its potential as a practical solution for achieving advanced service differentiation in modern network infrastructures.

The two main types of blockchains that are currently widely deployed are public blockchains and permissioned blockchains. The research that has been conducted for blockchain vulnerability detection is mainly oriented to public blockchains. Less consideration is given to the unique requirements of the permissioned blockchains, which cannot be directly migrated to the application scenarios of the permissioned blockchains. The permissioned blockchain is deployed between verified organizations, and its smart contracts may contain sensitive information such as the transaction flow of the contracts, transaction algorithms, etc. The sensitive information can be considered as the private information of the smart contracts themselves, which should be kept confidential to users outside the blockchain. In this paper, a privacy-preserving smart contract vulnerability detection framework is proposed. The framework leverages blockchain and confidential computing technologies to enable vulnerability detection in permissioned blockchain smart contracts while protecting the privacy of smart contracts. The framework is also able to protect the interests of vulnerability detection model owners. We experimentally validate the detection performance of our framework in a confidential computing environment.

With the popularity of computers and mobile devices and the development of the Internet, browsers (applications used to retrieve and display information resources on the World Wide Web) are often included by default and have become an indispensable software. Therefore, research on browser security issues is essential for protecting information assets. Among many browsers in the industry, Chrome, as a cross-platform web browser developed by Google, occupies a large market share in desktop browsers, and its security risks are further amplified as its kernel is used by many other browsers. Therefore, the research on the security issues of Chrome browser is critical for browser security.This paper focuses on the vulnerability detection of the process communication interface in Chrome browser, and designs and implements a fuzzing framework, auto-mojo-fuzz (AMF). The fuzzing process mainly designs a sample optimization technique to ensure the effectiveness of input samples and improve the efficiency of fuzzing. After implementing the AMF solution, we evaluate the generated test samples to demonstrate the effectiveness of the sample optimization technique. We also prove the possibility of discovering more vulnerabilities with AMF, and tests it with the latest version of Chrome browser, finding five unique crashes, four of which are verified as security vulnerabilities, effectively proving the automatic and efficient ability of this framework to discover vulnerabilities in the process communication interfaces in browsers.

With the rapid development of deep learning technology, DNN models are increasingly used in safety-critical systems such as autonomous driving and robotics. Safety-critical systems have very stringent requirements for reliability and security, and failure of the system may have very serious consequences. It has been shown that small perturbations of key DNN parameters can lead to incorrect inference results in DNN models. The existing DNN model dependability assurance methods mainly focus on finding the key parameters in the DNN model and protecting the key parameters by hardware to ensure the dependability during the model execution. These methods mainly optimize on the basis of existing models and do not consider the dependability of the model in the DNN model design process. Neural Architecture Search (NAS) technology can use data to automatically optimize neural network architectures. Differentiated Architecture Search (DARTS) is an efficient NAS technology. The existing NAS technology mainly obtains the model with the best inference accuracy by optimizing the neural network architecture. Inspired by the DARTS, we propose an end-to-end automated design dependability model method that can automatically optimize the neural network architecture under the constraints of DNN model dependability to obtain models that satisfy both inference accuracy and inference time requirements, and demonstrate the effectiveness of this method through experiments.

The topology discovery service in Software-Defined Networking (SDN) provides the controller with a global view of the substrate network topology, allowing for central management of the entire network. Unfortunately, emerging topology attacks can poison the network topology and result in unforeseeable disasters. Although researchers have made great efforts to mitigate this problem, security hazards still exist. In this paper, we propose Invisible Assailant Attack (IAA), the first combination topology attack capable of injecting and maintaining fake links even when 12 existing defense strategies are deployed simultaneously. IAA consists of 14 attack phases that apply multiple attack strategies. Attackers skillfully disguise the attack traffic in each phase so that it looks like normal network traffic, and perform these phases in a well-planned sequence, thereby bypassing existing defenses step by step. To mitigate this attack, we propose a Route Path Verification (RPV) mechanism that orchestrates multiple defense strategies to identify fake links. According to the experiments, RPV can successfully detect IAA with low overhead: its detection completes within 1 ms while its per-flow storage consumption is only a few KB.