Distributed Denial of Service (DDoS) attacks continue to pose a substantial threat to Internet services. The Moving Target Defense (MTD) presents a promising solution for DDoS defense. The existing researches focused on detecting DDoS attacks using characteristics of packets and traffics, which is not adaptive to proxy-based MTD system. This paper introduces MPDD, a model for detecting DDoS attacks via parameters of multiple proxy servers, leveraging the inherent benefits of the proxy-based MTD architecture. By dynamically monitoring network traffic across each proxy server, MPDD is a service-agnostic and adaptive system that is aware of potential DDoS attacks. When integrated with an actual MTD system, we validate the effectiveness and feasibility of our detection methods through rigorous experimentation.
With the development and application of the Internet of Things (IoT) and Artificial Intelligence (AI) and other high technologies that greatly expand the scope of time, space and people’s cognition. Humanity is entering an era of intelligent interconnection of all things with the ternary fusion of people, machines and things. The intelligent interconnection of everything is based on the Internet of Things, and OpenHarmony is an operating system that facilitates the intelligent connection of all things. Although various development tools and environments of Hongmeng system have been initially established, OpenHarmony ecosystem is still in the stage of development and improvement, and the supported third-party libraries are still relatively limited, Hongmeng ecosystem still has a long way to go. It is very important for the OpenHarmony ecosystem to have more functions or components to be ported to complete the OpenHarmony project. We ported the third-party package paho mqtt to the OpenHarmony standard system. The successful porting of MQTT in the OpenHarmony standard system complements the OpenHarmony IoT project with additional functions or components. Furthermore, based on the ported MQTT third-party library in the OpenHarmony standard system, we developed an IoT system application for smart flower pots. This application not only verifies the porting results of MQTT, but also proposes a complete bottom-up IoT system development proposal based on OpenHarmony, from third-party library porting, peripheral driver development, device-side development to cloud platform-side development and application-side development, which promotes the construction and prosperity of the OpenHarmony ecosystem.
Federated learning effectively solves the problems of data privacy leakage and communication overhead in centralized machine learning by transferring the model training process from a central server to local devices. Since most of the real world has multimodal data scenarios, the scalability of federated learning systems that can only deal with unimodal local data is limited. Recently, researchers have attempted to apply federated learning to multimodal tasks and have achieved good performance. However, existing multimodal federated learning methods still face some challenges, such as model drift, slow model convergence, insufficient labeled data at the client, and high communication costs. To this end, this paper proposes a new multimodal federated learning scheme, which adopts an embedded knowledge transfer mechanism and a semi-supervised learning method, and improves the client selection strategy and server aggregation mechanism. By quantifying the client update degree and introducing a weighted aggregation mechanism, more targeted model optimization is realized. The experimental results show that our method significantly improves the model performance and achieves satisfactory results in downstream tasks, providing new research ideas and a practical basis for developing multimodal federated learning.
To enhance the security, complexity, and applicability of cloud node programs, effectively hinder attackers from reverse engineering, and reduce the ability of detailed analysis and vulnerability discovery of applications, in this paper, we propose a code obfuscation protection strategy based on the LLVM compiler framework oriented to the run state of the program. The approach first implements obfuscation processing on the cloud node program at the code IR layer, including control flow obfuscation, data flow obfuscation, and code virtualization obfuscation, to generate a protected executable program. After deploying the program to the cloud server, process obfuscation and memory obfuscation are performed to implement strong protection of data when the program is running. This procedure effectively guarantees the security of applications in cloud computing environments by rendering the structure and logic of the internal operation of the program inaccessible to program attackers.
The Resource Public Key Infrastructure (RPKI) stands as the sole standardized solution to date for mitigating route prefix hijacking, playing a critically important role in maintaining network security order and addressing security vulnerabilities in route exchanges. With the global deployment of RPKI, ensuring the stable operation of the RPKI system becomes crucial. In order to solve the problem of affecting the stable operation of the RPKI system due to the disconnection of relying parties from the RPKI repository, this paper proposes an emergency synchronization scheme for relying party cache route origin authorization (ROA) information based on the Simplified Local Internet Number Resource Management (SLURM). The proposed solution automatically monitors the connection status between relying parties and the resource repository, initiating emergency synchronization of ROA data in case of a disconnection. Finally, the effectiveness of this solution is validated through further experimental testing.
