NeuPow

Nasser, Yehya; Sau, Carlo; Prévotet, Jean-Christophe; Fanni, Tiziana; Palumbo, Francesca; Hélard, Maryline; Raffo, Luigi

doi:10.1145/3388141

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…An influential example of Node.js's impact on performance is LinkedIn's transition of their mobile backend servers from Ruby on Rails to Node.js in 2012. This shift resulted in a significant improvement in server performance during testing [13].…”

Section: Methodsmentioning

confidence: 99%

Evaluating and Improving the Robustness of Web Crawlers Against IP Blocking and Captchas

Onyenagubom

2024

IJLTEMAS

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In this work, we demonstrate the capability of a JavaScript-based web crawler to overcome anti-crawling measures such as CAPTCHAs and IP blocking. we delved into the ethical and legal dimensions of web crawling and provide recommendations for future research endeavors in this domain. A web crawler, being an automated software program, can navigate through websites and extract information. While it serves purposes like website analysis and indexing, it can also be misused for extracting personal data, scraping content, and overloading servers. Website administrators often employ anti-crawling techniques, such as CAPTCHAs, Robot.txt, and IP blocking, to thwart malicious web crawlers from accessing their content. These techniques aim to curtail the ability of a web crawler to scrape, access, or overload website resources, without impeding legitimate users from accessing the necessary content. The objective of this study is to demonstrate and enhance the resilience of legitimate web crawlers against anti-crawling techniques like CAPTCHAs and IP blocking, challenging the notion that these measures are universally effective against all types of web crawlers.

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Section: Methodsmentioning

confidence: 99%

Evaluating and Improving the Robustness of Web Crawlers Against IP Blocking and Captchas

Onyenagubom

2024

IJLTEMAS

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“…On-board power measurement refers to measuring the power consumption of the device's motherboard. Power consumption has become a critical metric in various electronic devices for modern and critical applications [37]. It is possible to divide the power consumption into two main components:…”

Section: B On-board Power Measurementsmentioning

confidence: 99%

Toward Hardware-Assisted Malware Detection Utilizing Explainable Machine Learning: A Survey

Nasser,

Nassar

2023

IEEE Access

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Hardware joined the battle against malware by introducing secure boot architectures, malware-aware processors, and trusted platform modules. Hardware performance indicators, power profiles, and side channel information can be leveraged at run-time via machine learning for continuous monitoring and protection. The explainability of these machine learning algorithms may play a crucial role in interpreting their results and avoiding false positives. In this paper, we present an eagle eye on the state of the art of these components: we examine secure architectures and malware-aware processors such as those implemented in the RISC-V Instruction Set Architecture and Reduced Instruction Set Computer (RISC). We categorize hardware-assisted solutions increased by machine learning for classification. We survey recently proposed software-assisted and hardware-assisted explainability algorithms in our context. In the discussion, we suggest that (1) safe architectures that guarantee secure device boot are a must, (2) Sidechannel approaches are challenging to integrate into embedded systems, yet they show promise in terms of efficiency, (3) malware-aware processors provide valuable features for malware detection software, and (4) Without explainability, malware detection software is error-prone and can be easily bypassed.

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“…Instead of considering the switching activities at the circuit level, the authors in [20] propose NeuPow, a neural networkbased approach that works at the component level. It uses Artificial Neural Networks (ANNs) to model the power and signal propagation behavior of the considered components.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Application for Early Power Analysis Accuracy Improvement: A Case Study for Nets Switching Power

Chentouf,

Dior Nafy BA,

Attaoui

et al. 2023

JICS

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In the quest for precise power estimation during the early phases of design, the absence of a Standard Parasitic Exchange File (SPEF) with interconnect R/C values poses a significant hurdle. To address this challenge, we introduce a Machine Learning (ML) approach designed to predict net power metrics at the Gate Level without relying on SPEF. Net features are extracted from Electronic Design Automation (EDA) tools, facilitating the training of models for the prediction of Net Switching Power. Notably, the Random Forest model emerges as the most effective, achieving high accuracy by reducing power error from around 20% to a mere 0.1%.Furthermore, our innovative approach enhances efficiency by bypassing the traditional SPEF generation process. This results in a significant 5x reduction in runtime compared to the conventional flow, with a notable decrease from 163.6 minutes to just 33.7 minutes. This substantial acceleration is achieved by skipping the time-intensive synthesis and physical design steps required for SPEF.In summary, our ML-based methodology not only achieves swift and accurate power estimation in the early stages of design but also liberates the process from the constraints of SPEF dependency. This marks a transformative shift in the landscape of power estimation methodologies.

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NeuPow

Cited by 10 publications

References 22 publications

Evaluating and Improving the Robustness of Web Crawlers Against IP Blocking and Captchas

Evaluating and Improving the Robustness of Web Crawlers Against IP Blocking and Captchas

Toward Hardware-Assisted Malware Detection Utilizing Explainable Machine Learning: A Survey

Machine Learning Application for Early Power Analysis Accuracy Improvement: A Case Study for Nets Switching Power

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