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DAPPER: A Performance-Attack-Resilient Tracker for RowHammer Defense 31

DAPPER: A Performance-Attack-Resilient Tracker for RowHammer...

引用

31st IEEE international symposium on High Performance computer Architecture, HPCA 2025

作者： Woo, Jeonghyun Nair, Prashant J. The University of British Columbia Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331506476

RowHammer vulnerabilities pose a significant threat to modern DRAM-based systems, where rapid activation of DRAM rows can induce bit-flips in neighboring rows. To mitigate this, state-of-the-art host-side RowHammer mitigations typically rely on shared counters or tracking structures. While these optimizations benefit benign applications, they are vulnerable to Performance Attacks (Perf-Attacks), where adversaries exploit shared structures to reduce DRAM bandwidth for co-running benign applications by increasing DRAM accesses for RowHammer counters or triggering repetitive refreshes required for the early reset of structures, significantly degrading performance. In this paper, we propose secure hashing mechanisms to thwart adversarial attempts to capture the mapping of shared structures. We propose DAPPER, a novel low-cost tracker resilient to Perf-Attacks even at ultra-low RowHammer thresholds. We first present a secure hashing template in the form of DAPPER-S. We then develop Dapper-H, an enhanced version of DapperS, incorporating double-hashing, novel reset strategies, and mitigative refresh techniques. Our security analysis demonstrates the effectiveness of DAPPER-H against both RowHammer and Perf-Attacks. Experiments with 57 workloads from SPEC2006, SPEC2017, TPC, Hadoop, MediaBench, and YCSB show that, even at an ultra-low RowHammer threshold of 500, DapperH incurs only a 0.9% slowdown in the presence of Perf-Attacks while using only 96 KB of SRAM per 32GB of DRAM memory. © 2025 IEEE.

关键词： Static random access storage

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CCD Array Spectrometer-based FBG Bragg Wavelength Detection Using ANN Algorithm 11th

CCD Array Spectrometer-Based FBG Bragg Wavelength Detection ...

引用

11th international symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Kumar, Dipten Basumallick, Nandini Bhattacharjee, Debotosh Jadavpur University Kolkata700032 India CSIR-CGCRI Kolkata700032 India

ISBN: (纸本)9789819797615

An artificial neural network (ANN)-based algorithm for detecting Bragg wavelengths is proposed in this paper for interrogating fiber Bragg grating (FBG) sensors using a CCD array spectrometer. The proposed algorithm addresses the actual non-ideal behaviors of components, including optical intensity variation over time. It also accommodates the fact that FBG sensor nominal Bragg wavelengths often change during field deployment due to variations in pre-strain or ambient conditions. Finally, the proposed ANN model is compared with traditional methods of determining FBG Bragg wavelengths, such as curve fitting and centroid finding. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fiber Bragg gratings

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Advancing Multilingual Speaker Identification and Verification for Indo-Aryan and Dravidian Languages 1

Advancing Multilingual Speaker Identification and Verificati...

引用

1st workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Sritharan, Braveenan Thayasivam, Uthayasanker Dept. of Computer Science & Engineering University of Moratuwa Sri Lanka

ISBN: (纸本)9798891762145

Multilingual speaker identification and verification is a challenging task, especially for languages with diverse acoustic and linguistic features such as Indo-Aryan and Dravidian languages. Previous models have struggled to generalize across multilingual environments, leading to significant performance degradation when applied to multiple languages. In this paper, we propose an advanced approach to multilingual speaker identification and verification, specifically designed for Indo-Aryan and Dravidian languages. Empirical results on the Kathbath dataset show that our approach significantly improves speaker identification accuracy, reducing the performance gap between monolingual and multilingual systems from 15% to just 1%. Additionally, our model reduces the equal error rate for speaker verification from 15% to 5% in noisy conditions. Our method demonstrates strong generalization capabilities across diverse languages, offering a scalable solution for multilingual voice-based biometric systems. © 2025 Association for Computational Linguistics.

关键词： Speech recognition

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Flood Detection in UAV Images of Urban Area Using Machine Learning and Deep Learning Techniques 11th

Flood Detection in UAV Images of Urban Area Using Machine L...

引用

11th international symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Ghosh, Tirthankar Paul, Arati Chaki, Nabendu Department of Computer Science and Engineering Techno International New Town Block—DG 1/1 Action Area 1 New Town Rajarhat West Bengal Kolkata700156 India RRSC-East NRSC Indian Space Research Organisation BG-2 AA-1B New Town West Bengal Kolkata700156 India Department of Computer Science and Engineering University of Calcutta JD Block Sector 3 West Bengal Kolkata700106 India

ISBN: (纸本)9789819797615

The present work uses unmanned aerial vehicle (UAV) images for flood detection in urban areas. Two Machine Learning (ML) techniques, namely k-nearest neighbours (KNN) and random forest, are initially employed to classify flood-affected areas based on extracted features from the UAV images. Subsequently, two Deep Learning (DL) methods, UNet and DeepLabv3+, are implemented for semantic segmentation of the images to obtain precise flood maps. The evaluation metrics include accuracy, precision, recall, and F1-score. This work demonstrates the effectiveness of different ML and DL techniques for flood detection using UAV images in urban areas. The comparison reveals that UNet offers superior accuracy compared to KNN, random forest, and DeepLabv3+. The higher accuracy of UNet is attributed to its ability to capture both local and global contextual information, enabling precise delineation of flood extents in urban areas. The findings also emphasize the significance of deep learning approaches in solving similar problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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iCNN-LSTM: An Incremental CNN-LSTM based Ransomware Detection System

iCNN-LSTM: An Incremental CNN-LSTM Based Ransomware Detectio...

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PhD symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international Conference on Web Information systems engineering, WISE 2024

作者： Ispahany, Jamil Islam, MD Rafiqul Khan, M. Arif Islam, MD Zahidul Charles Sturt University Bathurst2795 Australia Charles Sturt University Albury/Wodonga2640 Australia Kingston2600 Australia

ISBN: (纸本)9789819614820

In response to the increasing ransomware threat, this study presents a novel detection system that combines parallel Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. By leveraging Sysmon logs, the system enables real-time analysis on Windows-based endpoints. Our approach overcomes the limitations of traditional models by employing batch-based incremental learning, allowing the system to continuously adapt to new ransomware variants without requiring full retraining. The proposed model achieved an impressive average F2 score of 99.57%, with low false positive and false negative rates of 0.16% and 4.89%, respectively, within a highly imbalanced dataset, demonstrating exceptional accuracy in detecting malicious behaviour. The dynamic detection capabilities of Sysmon enhance the model’s effectiveness by providing a continuous stream of security events, reducing the vulnerabilities associated with static detection methods. Additionally, the parallel processing of LSTM and CNN modules, along with attention mechanisms, enables our system to achieve the highest F2 score and the lowest false negative rate compared to other popular deep learning algorithms for ransomware detection, making it highly suitable for real-world applications. These results underscore the potential of our iCNN-LSTM framework as a robust solution for real-time ransomware detection, ensuring adaptability and resilience against evolving cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Surface Electromyography Acquisition System based on IIR Comb Filter 24

Surface Electromyography Acquisition System Based on IIR Com...

引用

2024 international symposium on Integrated Circuit Design and Integrated systems, ICDIS 2024

作者： Wu, Xiaoqiang Shen, Hanxin Huang, Jichun Wu, Zhipeng School of Optoelectronic and Communication Engineering Xiamen University of Technology Xiamen China Xiamen Guli Electronics Technology Co. Ltd Xiamen China

ISBN: (纸本)9798400718229

To address the issue of the complexity and inconvenience in adjusting the analog notch filter within the surface electromyography(sEMG) signal acquisition circuit, a surface electromyography signal acquisition system founded on the IIR comb filter is devised. Firstly, this paper investigates the frequency characteristics of industrial frequency interference and puts forward an IIR comb filter for the elimination of the 50 Hz industrial frequency interference and its harmonic noises. The filter is subjected to simulation and testing. In this system, the surface electromyography signal gathered by the electrode undergoes preprocessing by the front-end amplification circuit and band-pass filter circuit and is subsequently converted into a digital signal through the ADC module. The signal is then digitally filtered by the FPGA to remove the industrial frequency interference. The processed data is transmitted to the host computer via Ethernet for waveform display and spectral analysis. After the actual machine test, this system is capable of achieving a suppression and attenuation of -30 dB for the 50 Hz industrial frequency interference and its harmonic noises while retaining the useful myoelectric signals. The proposed scheme holds certain reference value for the digitalization and miniaturization of myoelectric signal acquisition. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Comb filters

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Enhancement of Malware Detection systems Using Mal-cGAN 8th

Enhancement of Malware Detection Systems Using Mal-cGAN

引用

8th international symposium on Intelligent Informatics, ISI 2023

作者： Timmanagoudar, Harshit Preethi, P. Department of Computer Science and Engineering PES University Karnataka Bangalore560085 India

ISBN: (纸本)9789819721467

Malware detection systems that incorporate machine learning algorithms have received considerable attention because of their impressive ability to identify and combat emerging and sophisticated malware threats effectively. These systems rely on advanced techniques to analyse vast amounts of data and recognise patterns that distinguish between harmless and malicious software. The development of Generative Adversarial Networks [1] has enabled innovative techniques to enhance the capability of the malware detection systems, Mal-GAN [2]. In this research paper, an innovative approach called Mal-cGAN is introduced, aiming to enhance the performance of malware detection systems that solely work on image representations of software by synthesising image representations of malware. Drawing inspiration from the architecture of Conditional Generative Adversarial Networks (cGANs) [3], the method employs a "U-Net" [4] based generator that facilitates seamless communication between the Malware Detection System and Substitute detector with the Substitute Detector performing the role of a discriminator from typical GANs. This effective collaboration enables the generator to produce practical and highly realistic malware image representations, which in turn can bypass the detection systems and eventually used to enhance the performance of the Malware Detection System. Thorough evaluations of the model demonstrate that the Mal-cGAN generator successfully synthesizes accurate and informative image representations of malware. Notably, training the Malware Detection System on these synthesized samples leads to a substantial increase in accuracy, improving from 83.47% to 93.61%. The results depicting synthesised image representations of malware and the classification metrics highlight the effectiveness of the proposed approach and its potential to revolutionise the field of malware detection using machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pt

关键词： Generative adversarial networks

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A computer-Aided Diagnosis System for the Detection of Parkinson’s Disease 8th

A Computer-Aided Diagnosis System for the Detection of Pa...

引用

8th international symposium on Intelligent Informatics, ISI 2023

作者： Abhijith, K.P. Sarath, R. Santhosh, Partha Mohan, Jesna Abraham, Bejoy Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Kerala Trivandrum India Department of Computer Science and Engineering College of Engineering Muttathara Kerala Trivandrum India

ISBN: (纸本)9789819721467

Parkinson’s disease (PD) is a neurological condition that worsens over time and causes accidental or uncontrolled movements, stiffness, and problems with balance and coordination. Usually, symptoms are minor, to begin with, and worsen with time. As the condition develops, individuals may have trouble speaking and moving about. They could also have mental and psychological issues, such as fatigue, sadness, sleeplessness, and cognitive impairment. The development of useful, technologically supported methods for monitoring the development of PD symptoms in everyday life has the potential to change disease assessment and hasten diagnosis. The creation of simple, technology-based techniques for tracking PD symptoms over time in daily life has the potential to revolutionize disease evaluation and speed up diagnosis. The proposed work aims to detect Parkinson’s disease from a patient’s vocal features at a very early stage. This can be achieved by training machine learning models to detect the vocal characteristics distinct in patients with PD from the data of patients who are already diagnosed with Parkinson’s Disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Transfer Learning based Framework of VGG16 to Detect Breast Cancer using Histopathological Images 6

Transfer Learning based Framework of VGG16 to Detect Breast ...

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6th Doctoral symposium on Intelligence Enabled Research, DoSIER 2024

作者： Divya, Zion G. Tripathy, B.K. School of Computer Science and Engineering VIT TN Vellore632014 India School of Computer Science Engineering and Information Systems VIT TN Vellore632014 India

Breast Cancer Detection introduces a prominent confrontation for researchers and clinical experts as it is one of the major public health issues and is weighed as a leading root for cancer correlated deaths among women worldwide. Early diagnosis helps the chances of survival rate which is a crucial part, but standard breast cancer classical techniques rely on operative techniques, open procedures and time-consuming analyses. So, the standard classical techniques pave a way in demanding accurate solutions that are provided with efficient algorithms. This paper addresses these challenges by utilizing automated cancer detection that presents an ensemble deep learning approach by implementing transfer learning for breast cancer detection using histopathological images for classifying Benign and Malignant tissues. Transfer learning uses VGG16 to train the model on large dataset, by reusing the knowledge that has obtained from previous task which is considered as an input to another task thereby improving the performance. In this paper VGG16 architecture is considered as a pretrained model to train on ImageNet. Ensemble strategy is applied as a next step by taking the average of predicted probabilities, out of which the VGG16 model offers overall best accuracy of 98.83%. © 2025 Copyright for this paper by its authors.

关键词： CNN Deep Learning Transfer Learning VGG16

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SWFEM: Sparse Weighted Fine-Tuned Ensemble Model for Leaf Disease Detection 11th

SWFEM: Sparse Weighted Fine-Tuned Ensemble Model for Leaf D...

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11th international symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Paul, Rakesh Ghosh, Susobhan Ghosh, Mridul Obaidullah, Sk Md Roy, Kaushik Department of Computer Science Debra Thana S. K. S Mahavidyalaya Debra India Department of Computer Science Dinabandhu Mahavidyalaya Bongaon India Department of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah India Department of Computer Science and Engineering Aliah University Kolkata India Department of Computer Science West Bengal State University Barasat India

ISBN: (纸本)9789819797615

Globally, the greatest concerns of farmers are plant diseases. A considerable loss of yield has an immediate impact on the economy. Machine learning models exhibited capabilities to detect plant diseases. To enhance the generalization, flexibility, and adaptability of machine learning models, an ensemble model was introduced to combine many models and produce an appropriate and optimal predictive solution. To achieve better accuracy in deep learning models, it is necessary to have a significant amount of training time and memory resources. This is frequently utilized in engineering fields. Nowadays, the computational burden of training multiple independent networks increases with the growing number of deep learning models. To address these computational challenges, we introduced pruning and tuning-based fast, cost-effective sparse ensemble networks. The popularity of sparse architecture has increased because of its low memory requirements and faster inference rates. To identify an appropriate subset of ensemble members to build a smaller ensemble model, pruning is used to refrain from retraining the model. To achieve this, we first trained a base network. After that, we clone the parent network and drastically reduce each child’s features to generate an ensemble of members with various topologies. We soon trained each successor network for a smaller number of epochs. An empirical investigation demonstrated how well our method works on the pruned ensemble model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agriculture Deep learning Leaf disease Sparse fine-tuned ensemble

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