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NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tiered Mitigation Strategy 25

NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tie...

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26th International Conference on Distributed computing and Networking, ICDCN 2025

作者： Jha, Prianshu Singh, Gurpreet Kumar, Amit Agrawal, Dewesh Patel, Yashwant Singh Forough, Javad Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computing Science Umea University Umeå Sweden

ISBN: (纸本)9798400710629

Web servers are the backbone of modern Internet infrastructure, serving as the primary medium for online information distribution. Despite their critical role, web servers are susceptible to cyber-attacks. While current firewall mechanisms provide some level of protection against cyber threats, the evolving nature of these attacks and emerging vulnerabilities continue to pose significant risks. One of the most prevalent yet lethal attacks known today is DDoS (Distributed Denial of Service) attacks. These growing risks emphasize the urgent need for dynamic and robust threat detection and mitigation systems. This paper presents a comparative analysis of ensemble learning models (e.g., Random Forest, XGBoost, and LightGBM) and neural network-based models (e.g., Graph Neural Networks (GNN), Long Short-Term Memory networks (LSTM) with attention layers, and Gated Recurrent Units (GRU)) for DDoS attack detection and classification. Based on this analysis, we propose a real-time DDoS attack detection system integrated with a mitigation mechanism. The proposed system utilizes a two-tiered mitigation strategy assisted by UFW (Uncomplicated Firewall) and Apache server configuration files to block the incoming and outgoing traffic associated with suspicious IP addresses. The system's overall complexity, integrating both detection and response processes, ensures its efficiency in real-time environments while handling large volumes of traffic. Furthermore, the proposed approach achieves 15% improvement in detection accuracy and 20% reduction in false positives compared to traditional techniques, making it an effective and scalable solution for modern web server security. © 2025 Copyright held by the owner/author(s).

关键词： Decision trees

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Revisiting Automatic Pipelining: Gate-level Forwarding and Speculation 24

Revisiting Automatic Pipelining: Gate-level Forwarding and S...

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61st acm/IEEE Design Automation Conference, DAC 2024

作者： Cheng, Shuyao Li, Chongxiao Du, Zidong Zhang, Rui Hu, Xing Li, Xiaqing Xu, Guanglin Wen, Yuanbo Guo, Qi State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Cambricon Technologies China Shanghai Innovation Center for Processor Technologies China

ISBN: (纸本)9798400706011

Pipelining is a widely applied micro-architectural performance optimization and requires non-trivial designs for better execution throughput. The key to pipeline throughput optimization is to resolve data hazards caused by read-after-write (RAW) dependencies, which are traditionally tackled by forwarding and speculation to avoid pipeline stalls. However, existing approaches are conducted based on high-level dataflow analysis, with potential loss of optimization opportunities for lack of analysis of the netlist *** propose an efficient method to resolve RAW dependencies with low-level netlist analysis by gate-level forwarding and speculation. With a greedy search method to detect and resolve short-delay gate-level signal paths for forwarding and an approximate circuit synthesis method with formal verification for gate-level speculation, the method efficiently utilizes the gate-level information to further improve pipeline throughput. Even if the gate-level design space is vast, by assembling the logic gates in the short-delay signal paths in fewer adjacent pipeline stages, the forwarding paths and the speculator bypass invalid pipeline stages and thus conceal the latency of these paths. We conduct experiments on the widely-used ISCAS/EPFL benchmark circuits and a large-scale RISC-V CPU, and our approach can find better designs than human experts. © 2024 Copyright held by the owner/author(s).

关键词： Delay circuits

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DPS: Adaptive Power Management for Overprovisioned systems 23

DPS: Adaptive Power Management for Overprovisioned Systems

引用

2023 International Conference for High Performance computing, Networking, Storage and analysis, SC 2023

作者： Ding, Jianru Hoffmann, Henry University of Chicago Chicago United States

ISBN: (纸本)9798400701092

Maximizing performance under a power budget is essential for HPC systems and has inspired the development of many power management frameworks. These can be broadly characterized into two groups: model-based and stateless. Model-based frameworks use machine learning to achieve good performance under a power budget but are highly dependent on the quality of the learned model and the data used to train it. Stateless frameworks are more robust and require no training, but are generally lower performance. In this paper, we propose a new framework that does not require a model, but does track a small amount of state in the form of recent power dynamics. We implement this idea and test it on a public cloud running both Spark and HPC jobs. We find when total power demand is low, our framework achieves equivalent performance to prior work, but when power demand is high it achieves mean 8% performance improvement (with no reliance on a learned model). © 2023 Owner/Author(s).

关键词： Power management

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(Re)Politicizing DigitalWell-Being: Beyond User Engagements 22

(Re)Politicizing DigitalWell-Being: Beyond User Engagements

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CHI Conference on Human Factors in computing systems (CHI)

作者： Docherty, Niall Biega, Asia J. Microsoft Res Cambridge MA 02142 USA Max Planck Inst Secur & Privacy Bochum Germany

ISBN: (纸本)9781450391573

The psychological costs of the attention economy are often considered through the binary of harmful design and healthy use, with digital well-being chiefly characterised as a matter of personal responsibility. This article adopts an interdisciplinary approach to highlight the empirical, ideological, and political limits of embedding this individualised perspective in computational discourses and designs of digital well-being measurement. We will reveal wellbeing to be a culturally specific and environmentally conditioned concept and will problematize user engagement as a universal proxy for well-being. Instead, the contributing factors of user well-being will be located in environing social, cultural, and political conditions far beyond the control of individual users alone. In doing so, we hope to reinvigorate the issue of digital well-being measurement as a nexus point of political concern, through which multiple disciplines can study experiences of digital ill as symptomatic of wider social inequalities and (capitalist) relations of power.

关键词： well-being user engagement measurement time well-spent power

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What-if analysis for Business Professionals: Current Practices and Future Opportunities 25

What-if Analysis for Business Professionals: Current Practic...

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2025 CHI Conference on Human Factors in computing systems, CHI 2025

作者： Gathani, Sneha Liu, Zhicheng Haas, Peter J. Demiralp, Çağatay University of Maryland College Park MD United States University of Massachusetts Amherst Amherst MA United States Aws Ai Labs New York United States Mit Csail Cambridge MA United States

ISBN: (纸本)9798400713941

What-if analysis (WIA) is essential for data-driven decision-making, allowing users to assess how changes in variables impact outcomes and explore alternative scenarios. Existing WIA research primarily supports the workflows of data scientists and analysts, and largely overlooks business professionals who engage in WIA through non-technical means. To bridge this gap, we conduct a two-part user study with 22 business professionals across marketing, sales, product, and operations roles. The first study examines their existing WIA practices, tools, and challenges. Findings reveal that business professionals perform many WIA techniques independently using rudimentary tools due to various constraints. We then implement representative WIA techniques in a visual analytics prototype and use it as a probe to conduct a follow-up study evaluating business professionals' practical use of the techniques. Results show that these techniques improve decision-making efficiency and confidence while underscoring the need for better support in data preparation, risk assessment, and domain knowledge integration. Finally, we offer design recommendations to enhance future business analytics systems. © 2025 Copyright held by the owner/author(s).

关键词： Business Intelligence Interview Study Predictive and Prescriptive Analytics What-if analysis

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A Systematic Review and Meta-analysis of Text Entry Studies

A Systematic Review and Meta-analysis of Text Entry Studies

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2025 CHI Conference on Human Factors in computing systems, CHI EA 2025

作者： Iarygina, Olga Mottelson, Aske IT University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9798400713958

Text entry is a notably standardized research field in human-computer interaction, with established benchmarks and methodologies enabling rigorous comparisons across studies. Nevertheless, meta research in text-entry is scarce. This meta-analysis summarizes findings and effects from text entry experiments published from 1990 to 2024. Our records show that most text-entry experiments feature a baseline and an experimental UI, and that they mostly show that the experimental UI is superior (g = 1.68), with regards to text entry speed. We found that earlier text entry research focused mostly on the development of novel techniques, whereas recent text entry research, to a larger extent, adapts existing input methods to new devices, such as smartwatches or virtual reality headsets. We also find that text entry research often lacks statistical power (M = 0.66, SD = 0.37), relies on small sample sizes (Mdn = 12), and is mostly conducted with within-subjects designs (84%). Subjective evaluations of text entry systems beyond objective performance are rare. Our analysis found evidence for systematic publication bias in text entry research, as indicated by funnel plot asymmetry and a significant weight-function model adjustment. This underlines the research field’s competitive culture of publishing research only if entry speed is beaten in comparison to some baseline. © 2025 Copyright held by the owner/author(s).

关键词： keyboards meta-analysis text entry

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Towards Zero Trust Security in SDN: A Multi-Layered Defense Strategy 25

Towards Zero Trust Security in SDN: A Multi-Layered Defense ...

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26th International Conference on Distributed computing and Networking, ICDCN 2025

作者： Barach, Jay IT Operations and Recruitment Systems Staffing Group. Inc King of PrussiaPA United States

ISBN: (纸本)9798400710629

Software-defined networking (SDN) encounters security challenges akin to those found in conventional networks. The decoupling of the SDN control plane from the data plane introduces a heightened risk to the controller, rendering it susceptible to cyberattacks. Traditional security models, such as perimeter-based defenses, fail to mitigate lateral movement attacks, often exploited by malicious insiders or vulnerabilities in hardware and software. Zero Trust Architecture (ZTA) has emerged as a modern security paradigm designed to enhance enterprise network defenses. In this work, we present an advanced zero-trust security framework, ZSDN-Guard, tailored for SDN environments. The proposed framework leverages deep learning techniques and ZTA principles to safeguard all network assets and connections. ZSDN-Guard incorporates a traffic anomaly detection module, CALSeq2Seq1, which utilizes deep learning for real-time analysis of user network activities. This system continuously monitors and restricts unauthorized access to network resources, enabling dynamic, context-aware authorization. The MiniIZTA simulation platform, built upon Mininet, was developed to assess the efficacy of the proposed ZSDN-Guard framework. Experimental evaluations indicate that ZSDN-Guard maintains approximately 80.5% network throughput under attack conditions. Furthermore, the framework achieves an anomaly detection accuracy of 99.56% using the SDN dataset, validating its robustness and effectiveness in enhancing network security. © 2025 Copyright held by the owner/author(s).

关键词： Deep learning

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The Sharply Decreasing Disruptiveness of HCI 25

The Sharply Decreasing Disruptiveness of HCI

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2025 CHI Conference on Human Factors in computing systems, CHI 2025

作者： Chen, Zhilong Li, Yong Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9798400713941

How creative is HCI research? Although creativity has been a notable theme in HCI, the landscape of the creativity of HCI research itself remains unclear. In this paper, we address this by measuring the disruptiveness of HCI research, one important dimension distinguishing the level of creativity, through a large-scale data-driven bibliometric analysis. By quantitatively tracing its evolution over the past 40 years, we find that the disruptiveness of HCI is decreasing sharply, even at a faster speed than the global average across all fields. We characterize the patterns shown by the themes, knowledge use, and authorship of disruptive papers in HCI, and identify how they associate with disruptiveness, e.g., the positive relationship between author freshness and disruptiveness. Based on our results, we discuss practical implications to improve and secure disruptiveness and creativity in HCI. © 2025 Copyright held by the owner/author(s).

关键词： bibliometric analysis Creativity disruptiveness human-computer interaction

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IoT System Vulnerability analysis and Network Hardening with Shortest Attack Trace in aWeighted Attack Graph 23

IoT System Vulnerability Analysis and Network Hardening with...

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8th acm/IEEE International Conference on Internet of Things Design and Implementation (IoTDI)

作者： Wan, Yinxin Lin, Xuanli Sabur, Abdulhakim Chang, Alena Xu, Kuai Xue, Guoliang Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA Arizona State Univ Sch Math & Nat Sci Glendale AZ USA

ISBN: (纸本)9798400700378

In recent years, Internet of Things (IoT) devices have been extensively deployed in edge networks, including smart homes and offices. Despite the exciting opportunities afforded by the advancements in the IoT, it also introduces new attack vectors and vulnerabilities in the system. Existing studies have shown that the attack graph is an effective model for performing system-level analysis of IoT security. In this paper, we study IoT system vulnerability analysis and network hardening. We first extend the concept of attack graph to weighted attack graph and design a novel algorithm for computing a shortest attack trace in a weighted attack graph. We then formulate the network hardening problem. We prove that this problem is NP-hard, and then design an exact algorithm and a heuristic algorithm to solve it. Extensive experiments on 9 synthetic IoT systems and 2 real-world smart home IoT testbeds demonstrate that our shortest attack trace algorithm is robust and fast, and our heuristic network hardening algorithm is efficient in producing near optimal results compared to the exact algorithm.

关键词： shortest attack trace weighted attack graph network hardening IoT security

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PEARL: Physical Environment based Augmented Reality Lenses for In-Situ Human Movement analysis 23

PEARL: Physical Environment based Augmented Reality Lenses f...

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CHI conference on Human Factors in computing systems (CHI)

作者： Luo, Weizhou Yu, Zhongyuan Rzayev, Rufat Satkowski, Marc Gumhold, Stefan McGinity, Matthew Dachselt, Raimund Tech Univ Dresden Interact Media Lab Dresden Germany Tech Univ Dresden Immers Experience Lab Dresden Germany Tech Univ Dresden Comp Graph & Visualizat Dresden Germany Ctr Scalable Data Analyt & Artifcial Intelligence Dresden Germany Tech Univ Dresden Ctr Tactile Internet Human In The Loop CeTI Dresden Germany Tech Univ Dresden Cluster Excellence Phys Life Dresden Germany

ISBN: (纸本)9781450394215

This paper presents Pearl, a mixed-reality approach for the analysis of human movement data in situ. As the physical environment shapes human motion and behavior, the analysis of such motion can beneft from the direct inclusion of the environment in the analytical process. We present methods for exploring movement data in relation to surrounding regions of interest, such as objects, furniture, and architectural elements. We introduce concepts for selecting and filtering data through direct interaction with the environment, and a suite of visualizations for revealing aggregated and emergent spatial and temporal relations. More sophisticated analysis is supported through complex queries comprising multiple regions of interest. To illustrate the potential of PEARL, we developed an Augmented Reality-based prototype and conducted expert review sessions and scenario walkthroughs in a simulated exhibition. Our contribution lays the foundation for leveraging the physical environment in the in-situ analysis of movement data.

关键词： In-situ visualization augmented/mixed reality Immersive Analytics movement data analysis affordance physical referents

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