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International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Isha Thakur Kajal Panda Sanjeev Kumar Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India

ISBN: (纸本)9781665454025

In the Internet ecosystem, URLs (Uniform Resource Locators) are widely used to propagate malicious infections through spam, spear-phishing, drive-by-download exploitation, malware embedding, etc. The blacklisting, pattern analysis, and signature-matching approaches are widely used to detect such threats. However, these techniques are very effective in detecting known types of malicious URLs but unable to detect new types of attacks launched by the malicious URLs. Moreover, traditional machine learning-based malicious URL detection is highly dependent upon manual feature engineering, which is a costly approach. , This research study proposes deep learning-based malicious URL detection with less dependent on manual feature engineering to overcome the above-mentioned challenges. The Natural Language Processing (NLP) technique, i.e., Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, along with the N-gram parameter, is used for extracting the features. Afterward, two feature selection techniques as Embedded methods are used - Logistic Regression with L1 (Lasso) penalty and Random Forest Selection. Neural network models such as Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Convolution Neural Networks (CNN) are constructed to detect malicious URLs. The experiment results demonstrate that the DNN model gives the best performance results as 96.95% accuracy, 99% precision, 100% recall, and 99% F1-score with logistic regression along with the L1 (Lasso) penalty as a feature selection method. Finally, this study compares state-of-art implemented deep learning methods for malicious URL detection.

关键词： Uniform resource locators Deep learning Biological system modeling Neural networks Manuals Feature extraction Natural language processing

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LTV-Backed E-Signatures Using Post-Quantum Cryptography

LTV-Backed E-Signatures Using Post-Quantum Cryptography

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Quantum Technologies, Communications, computing, Hardware and Embedded Systems Security (iQ-CCHESS), International Conference on

作者： Pavan Kurariya Srikanth Sailada Ankita Bhargava Jahnavi Bodhankar Ajai Kumar Santosh K Pandey Kavita Bhatia Centre for Development of Advanced Computing (CDAC) Pune India Ministry of Electronics and Information Technology Government of India New Delhi India

Due to concerns over the evolving quantum computing scenario and its potential threat to existing cryptosystems, including Public-Key Infrastructure (PKI) used in electronic signatures, there is a need to enhance the eSign components with quantum-resistant algorithms. One such algorithm is CrystalsDilithium, based on lattice cryptography, which can perform PKI operations in the eSign system. In addition to being resistant to quantum attacks, Long-Term Validation (LTV) is also utilized to ensure the validity of a document for an extended period. This is achieved by referring to PAdES/CAdES profiles and a time stamping service, which provides an accurate and trustworthy record of when a particular electronic document, file, or transaction was created, modified, or sent. Given the significance of eSign as a crucial digital service worldwide, migrating to post-quantum components will strengthen the system’s security. Therefore, the proposed research provides implementation, analysis and experimentation to ensure the eSign system components are robust against quantum computing threats.

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Comparative Study of Noises Over Quantum Key Distribution Protocol 7th

Comparative Study of Noises Over Quantum Key Distribution ...

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7th International Conference on Data Management, Analytics and Innovation, ICDMAI 2023

作者： Bhattacharyya, Sawan Das, Ajanta Banerjee, Anindita Chakrabarti, Amlan Department of Computer Science Ramakrishna Mission Vivekananda Centenary College West Bengal Kolkata India Amity Institute of Information Technology Amity University Kolkata Kolkata India Corporate Research and Development Centre for Development of Advanced Computing Pune India A. K. Choudhury School of Information Technology University of Calcutta West Bengal Kolkata India

ISBN: (纸本)9789819914135

The security of modern cryptographic systems mostly depends on mathematical hardness to solve a particular problem manually. The most popular and widely used classical cryptosystem, RSA (Rivest, Shamir, Alderman) is based on the mathematical hardness of prime factorization of the product of two large prime by any of the present classical processors. But the advancement in technology particularly in quantum information provides a threat to the present communication protocols. It motivates the researchers to move to new technologies that are fundamentally more secure through the principle of quantum mechanics. Such protocols rely upon quantum bits or qubits for data transmissions securely. The application of qubits instead of bits in cryptography using quantum key distribution protocols makes encryption robust. But the quantum communication protocols are prone to noise due to environmental factors, fragile and traverse’ a long fiber optical path. Hence, the states of qubits are easily tampered with by external factors. Noise alters the information;eventually, the message may not be meaningful to receivers. Except for bit flip and phase flip noises, depolarization, amplitude damping, decoherence, thermal relaxation, and phase damping are obstacles to achieving long-distance communication. The objective of this paper is to present a comparative study of quantum communication protocols in a noisy environment with different classes of noise. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quantum entanglement

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NLP2SQL Using Semi-supervised Learning 10th

NLP2SQL Using Semi-supervised Learning

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10th International advanced computing Conference, IACC 2020

作者： Vathsala, H. Koolagudi, Shashidhar G. Centre for Development of Advanced Computing Bengaluru Karnataka India Department of Computer Science and Engineering NITK Mangalore Karnataka India

ISBN: (纸本)9789811604003

Human Computer interaction has been moving towards Natural language in the modern age. SQL (Structured Query Language) is the chief database query language used today. There are many flavors of SQL but all of them have the same basic underlying structure. This paper attempts to use the Natural Language inputs to query the databases, which is achieved by translating the natural language (which in our case is English) input into the SQL (specific to MySQL database) query language. Here we use a semi-supervised learning with Memory augmented policy optimization approach to solve this problem. This method uses the context of the natural language questions through database schema, and hence its not just generation of SQL code. We have used the WikiSQL dataset for all our experiments. The proposed method gives a 2.3% higher accuracy than the state of the art semi-supervised method on an average. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Query processing

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Multi-ensemble machine learning framework for omics data integration: A case study using breast cancer samples

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Informatics in Medicine Unlocked 2024年 47卷

作者： Tembhare, Kunal Sharma, Tina Kasibhatla, Sunitha M. Achalere, Archana Joshi, Rajendra HPC-Medical and Bioinformatics Applications Group Centre for Development of Advanced Computing Innovation Park Panchavati Pashan Maharashtra Pune 411008 India

Integration of voluminous omics data aids to unravel biological complexities associated with different disease phenotypes. Machine learning (ML) approaches provide insightful techniques for systematic multi-omics data integration. In this study, survival prediction of breast cancer patients was undertaken using omics data of 302 female patients from The Cancer Genome Atlas (TCGA). The data included gene expression, miRNA expression, DNA methylation and copy number variation. Three computational multi-ensemble ML pipelines were tested using Support Vector Machine (SVM), Random Forest (RF) and Partial Least Squares-Discriminant Analysis (PLS-DA) algorithms. To overcome the limitations associated with univariate feature selection criteria, the ML pipelines were built along with latent factors obtained by multivariate dimension reduction method. This facilitated investigation of background genetic networks and identification of potential hub genes. Analysis of the results obtained revealed that SVM with PLS-DA method (integrated with gene expression, DNA methylation, and miRNA expression modalities) was the best-performing model with an Area Under Curve (AUC) of 89% and an accuracy of 83% for survival prediction. This study not only corroborated previously reported breast cancer-specific prognostic biomarkers but also predicted additional potential biomarkers. The work demonstrates the effective use of a multi-ensemble ML model with efficient feature selection methods as a robust protocol for cancer genotype to phenotype correlation. © 2024

关键词： Biomarker prediction Breast cancer survival Multi-omics integration Supervised machine learning

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Deep Learning-Based Object Detection Algorithms on Image and Video

Deep Learning-Based Object Detection Algorithms on Image and...

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Intelligent Technologies (CONIT), International Conference on

作者： Prasanta Das Angshuman Chakraborty Ravi Sankar Om Krishan Singh Hena Ray Alokesh Ghosh Dept. of AEE Centre for Development of Advanced Computing Kolkata India MeitY Ministry of Electronics and Information Technology New Delhi India

The research led to a change in direction or approach toward automated object detection on images and video. Some challenges in the detection of video make them complex. The mobility of objects and the complexity of low-light conditions make object detection a highly important field of research. Many researchers have proposed several approaches for detecting the object in images and video. This paper approaches the deep-learning-based YOLO (You Only Look Once) algorithms for object detection on image and video sequences. We proposed YOLOv7 and YOLOv8 algorithms with an apple dataset for this experiment which has achieved Mean Average Precision is 94.2% and 91.2% respectively. Where the recall and precision are 98%, 93.8% for YOLOV7, and 98%, 93.9% for YOLOv8. In such cases, fine-tune the model's hyperparameters or train models on a more balanced dataset to improve its performance. All the positive predictions were made by the classifiers and identified the actual positive instances in the dataset. The algorithms process 120 frames per second with GPU, while YOLOv8 performed well and had more accurate results on images and video within 1.3ms speed per image size at 640x640. In addition to comparing the experimental models, this work focuses on object detection on images and videos taken in various complicated situations.

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Configurable Wide Voltage Range Isolated Converter Configuration for Electric Vehicle Drive Train Evaluation and EVSE Validation with Back-to-Back Test Functionality

Configurable Wide Voltage Range Isolated Converter Configura...

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IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020)

作者： Murali Manohar S R Arun Raj Kumar K P Sreedevi M L Jinuraj K G Amal S Renji V Chacko Power Electronics Group Centre for Development of Advanced Computing Thiruvananthapuram A Scientific Society of the Ministry of Communication and Information Technology Govt. of India

Due to the rapid advancements in Electric Vehicle (EV) and EV subsystem development, the requirement of test platforms for Electric vehicle level testing; Drive train system development and evaluation, Electric Vehicle Supply Equipment (EVSE) validation etc. are on the rise. EV manufacturers, OEMs of various drive subsystems, and the automotive certification agencies test the electric vehicle subsystem in terms of performance and standards for certification. Extensive and rigorous use of energy storage systems is needed in EV subsystem testing, which is time consuming and expensive. A high frequency, high power DC-DC converters with isolation and back-to-back test functionality can replicate the behaviour of energy storage systems for EV level testing. Such a configurable wide voltage range isolated converter configuration with back-to-back test functionality is discussed in this paper. Series resonant dual active bridge DC-DC converter is one of the most researched topologies in this regard due to their high power density and efficiency. But over a wide voltage range operation there is alarming rise in the inductor currents. In order to achieve wide output voltage range, a multiphase DC-DC converter is cascaded with the SR - DAB converter. In this design, two SR-DABs are connected in input-parallel output-parallel configuration and at the output of this a two-phase interleaved bidirectional DC-DC converter is cascaded. Due to the modular structure of the converter configuration, higher power and current levels can be achieved by adding more parallel modules of SR-DAB and bidirectional DC-DC converter, using the same configuration. The converter configuration, design, and simulation results are presented in this paper.

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Relevance of Grid computing for India in the era of Cloud computing 9

Relevance of Grid Computing for India in the era of Cloud Co...

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9th IEEE International Conference on Cloud computing in Emerging Markets, CCEM 2020

作者： Arackal, Vineeth Simon Mangala, N. Centre for Development of Advanced Computing Bengaluru India

ISBN: (纸本)9781728195766

Grid computing is often implemented by relying on the concept of sharing of available resources, in order to improve economies of scale and utilization. However, after the advent of Cloud computing, the adoption of Grid computing has been rather slow, and people wonder if it is still relevant, and whether Grid computing truly matters. In a developing country like India where computer, smart phone and internet penetration is still low compared to richer nations, Grid computing can make a huge difference in the adoption of scientific computing, by making niche resources available to the researchers. Grid computing provides a framework to bridge the gap between what the researchers want and what they currently have. Grid aggregates available resources through efficient security mechanisms, meta-scheduling capabilities, and file transfer methodologies among others, and offers an unprecedented infrastructure for solving grand challenge problems. Grid computing is here to stay, though it may not become as common as Cloud computing, since it targets a comparatively niche set of users. It is also felt that the proponents of Grid computing need to come up with new ideas and paradigms to improve the relevance of the grid in the changed scenario of computing. © 2020 IEEE.

关键词： Grid computing

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Autonomic Runtime Adaptation Framework for Power Management in Large-Scale High-Performance computing Systems 17

Autonomic Runtime Adaptation Framework for Power Management ...

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17th IEEE India Council International Conference, INDICON 2020

作者： Kumar Saurav, Sumit Bindhumadhva Bapu, S Centre for Development of Advanced Computing Bangalore India

ISBN: (纸本)9781728169163

Power consumption is one of the most critical issues in the viability and sustainability of exascale computing systems. It is challenging to achieve a quintillion (1018) computations per second within the sustainable power budget of 20 MW. We need to improve energy efficiency at all HPC ecosystem levels to achieve exascale performance, such as subsystem level, node level, system level, scheduling level, software level, HPC site level, and power supply level. The power consumption and performance are the conflicting optimization concerns and that we need to manage intelligently. Hence, we need an efficient and comprehensive power management framework. In this paper, we have presented an agent-based autonomic runtime adaptation framework (ARAF). We investigated the Q-learning algorithm for devising optimal operating point (V, F) based control policy. The experimental results show that the described control policy provides 5-12% of energy-saving with minimal performance degradation. The proposed architecture offers a comprehensive and efficient power management framework for next-generation HPC systems. © 2020 IEEE.

关键词： Reinforcement learning

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Study and analysis of Unique Health Identifiers and applicability of Aadhaar as a Unique Health Identifier 17

Study and analysis of Unique Health Identifiers and applicab...

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17th IEEE India Council International Conference, INDICON 2020

作者： Chaturvedi, Abhijat Cheema, A S Srivastava, P K Rai, Astha Srivastava, Siddharth Centre for Development of Advanced Computing Noida India

ISBN: (纸本)9781728169163

This paper studies the technical characteristics of a Unique Health Identifier (UHID) in Indian and global context while also dwells upon the usage of Aadhaar has a Unique Health Identifier in India. Specifically, we highlight the major properties of a Unique Identifier (UID) transitioning into a Unique Health Identifier and study the advantages and disadvantages of using them and limit our scope to the technical aspects of these identifiers. Further, we study and discuss National Identification Numbers/UIDs of various countries along with an in-depth policy level and feature level study of UHID in India and applicability of Aadhaar for the same. © 2020 IEEE.

关键词： Medical informatics

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