Characterizing, managing and monitoring the networks for the ATLAS data acquisition system

Particle physics studies the constituents of matter and the interactions between them. Many of the elementary particles do not exist under normal circumstances in nature. However, they can be created and detected during energetic collisions of other particles, as is done in particle accelerators. The Large Hadron Collider (LHC) being built at CERN will be the world's largest circular particle accelerator, colliding protons at energies of 14 TeV. Only a very small fraction of the interactions will give raise to interesting phenomena. The collisions produced inside the accelerator are studied using particle detectors. ATLAS is one of the detectors built around the LHC accelerator ring. During its operation, it will generate a data stream of 64 Terabytes/s. A Trigger and Data Acquisition System (TDAQ) is connected to ATLAS -- its function is to acquire digitized data from the detector and apply trigger algorithms to identify the interesting events. Achieving this requires the power of over 2000 computers plus an interconnecting network capable of sustaining a throughput of over 150 Gbit/s with minimal loss and delay. The implementation of this network required a detailed study of the available switching technologies to a high degree of precision in order to choose the appropriate components. We developed an FPGA-based platform (the GETB) for testing network devices. The GETB system proved to be flexible enough to be used as the ba sis of three different network-related projects. An analysis of the traffic pattern that is generated by the ATLAS data-taking applications was also possible thanks to the GETB. Then, while the network was being assembled, parts of the ATLAS detector started commissioning -- this task relied on a functional network. Thus it was imperative to be able to continuously identify existing and usable infrastructure and manage its operations. In addition, monitoring was required to detect any overload conditions with an indication where the excess demand was being generated. We developed tools to ease the maintenance of the network and to automatically produce inventory reports. We created a system that discovers the network topology and this permitted us to verify the installation and to track its progress. A real-time traffic visualization system has been built, allowing us to see at a glance which network segments are heavily utilized. Later, as the network achieves production status, it will be necessary to extend the monitoring to identify individual applications' use of the available bandwidth. We studied a traffic monitoring technology that will allow us to have a better understanding on how the network is used. This technology, based on packet sampling, gives the possibility of having a complete view of the network: not only its total capacity utilization, but also how this capacity is divided among users and software applicati ons. This thesis describes the establishment of a set of tools designed to characterize, monitor and manage complex, large-scale, high-performance networks. We describe in detail how these tools were designed, calibrated, deployed and exploited. The work that led to the development of this thesis spans over more than four years and closely follows the development phases of the ATLAS network: its design, its installation and finally, its current and future operation

A Survey on the Evolution of Stream Processing Systems

Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. This survey provides a comprehensive overview of fundamental aspects of stream processing systems and their evolution in the functional areas of out-of-order data management, state management, fault tolerance, high availability, load management, elasticity, and reconfiguration. We review noteworthy past research findings, outline the similarities and differences between early ('00-'10) and modern ('11-'18) streaming systems, and discuss recent trends and open problems.Comment: 34 pages, 15 figures, 5 table

Research on text watermark algorithm for generative large language models

In recent years, the widespread use of large language models has raised issues of traceability and copyright. This requires the use of methods to distinguish whether a piece of text was written by a human or generated by a large language model. Large language model text watermarking techniques aim to distinguish whether the source of a piece of text is a human or a large language model. However, these approaches usually require a trade-off between detectability, text quality, and robustness against attacks. Existing methods use fixed watermark strength parameters, which cannot be flexibly adjusted for different contextual environments. In addition, embedding specific watermarking information in the text to realize multi-bit text watermarking is still a difficult task. Therefore, for the problem that watermarking cannot be flexibly adjusted according to the context, this thesis develops a controlled watermarking technique, and experiments show that the proposed method achieves a better trade-off between detectability, text quality, and robustness against attacks, and outperforms the original method. For the scenarios where multi-bit information needs to be embedded in the text, on the basis of the controlled watermarking technique, the direction of multi-bit watermarking is further explored, and a controlled intermittent embedding technique for multi-bit watermarking is developed, which can reliably insert additional information. Experimental results show that the method achieves reliable accuracy

Cyber Security

This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification

Cyber Security

This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification

Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures

The electricity industry is now at the verge of a new era. An era that promises, through the evolution of the existing electrical grids to Smart Grids, more efficient and effective power management, better reliability, reduced production costs and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the Smart Grid and the Smart Home, which we present as an integral part of the Smart Grid. Based on several scenarios we aim to present some of the most representative threats to the Smart Home / Smart Grid environment. The threats detected are categorized according to specific security goals set for the Smart Home/Smart Grid environment and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes the paper