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Holistic Cyber Threat Hunting Using Network Traffic Intrusion Detection Analysis for Ransomware Attacks

Holistic Cyber Threat Hunting Using Network Traffic Intrusio...

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International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Sangher, Kanti Singh Noor, Arti Sharma, V.K. School of IT Centre for Development of Advanced Computing Noida201307 India

ISBN: (纸本)9789819950904

In recent times, cybercriminals have penetrated diverse areas or sectors of the human business enterprise to initiate ransomware attacks against information technology infrastructure. They demand for money called ransom from organizations and individuals to save valuable data. There are varieties of ransomware attacks floating worldwide using intelligent algorithms and with the usage of different setup vulnerabilities. In our research work, we are exploring the latest trends in terms of sector-wise infiltration, captured the most popular among available and also the distribution of the number of attacks using the location information available at the country level. To achieve the correlation between the sectors and locations along with the parametric analysis, we have utilized artificial intelligence techniques. Accuracy of the prediction of attack based on the sector level analysis we have implemented Random Forest and XGBoost algorithm. This research work focuses primarily on two aspects, first is to explore the different aspects of ransomware attacks using intelligent machine learning algorithms. The method used insights to severity of spread of ransomware attacks, second research outcome is to forensically evidence finding of the attack traces using traffic analysis. The challenge is to learn from the previous weaknesses available in the infrastructure and at the same time to prepare the organization and countries' own prevention methods based on the lessons learnt, our exploratory analysis using the latest set of data implementing with AI will give a positive dimension in this area. Also, the proactive approach for managing the data safely is based on the finding of digital forensic analysis of infected ransomware traffic. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Avian Influenza Prediction Using Machine Learning 1

引用

10th International Conference on Innovations in Computer Science and Engineering, ICICSE 2022

作者： Shori, Maana Saroha, Kriti Centre for Development of Advanced Computing Noida India

ISBN: (数字)9789811974557

ISBN: (纸本)9789811974540

The avian influenza virus can be the cause of economic devastation due the impact on poultry, making it the cause for a potential pandemic. By predicting the disease, the consequences can be mitigated;so, the research explores various simple and ensemble classifiers, their performances are compared, and the one that is most efficient at predicting the avian influenza in Asian Countries is found. Facebook prophet is used to make prediction about the number of cases arising in future. The research also studies the impact of meteorological factors on the prediction of the disease. The results obtained from the research can be used as a tool to understand the causes for the aggravation of the avian influenza in future studies and thus, help mitigate its effects. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Towards Scalability and Performance: Framework for Heterogeneous Cluster Integration in Deep Learning Accelerators 4

Towards Scalability and Performance: Framework for Heterogen...

引用

4th IEEE International Conference on VLSI Systems, Architecture, Technology and Applications, VLSI SATA 2024

作者： Bhagirath, K. Pant, Dheeraj Tiwari, Abhishek Khaneja, Vivek Centre For Development Of Advanced Computing Embedded Systems Division Noida India

ISBN: (纸本)9798350362268

This paper presents a streamlined approach to address the challenges of integrating heterogeneous clusters in deep learning(DL) accelerators. As the demand for scalable and high-performance computing in DL continues to grow, the proposed framework offers an easy implementation solution, ensuring efficiency without compromise. The method details core components and design principles, guiding the inclusion of heterogeneous clusters step by step. Extensive experiments show the framework's adaptability, handling diverse workloads and cluster architectures. Our exploration integrates various dataflows, dynamically selected by NN during runtime for optimal performance. Results indicate improved scalability and robustness compared to existing methods. This research provides a valuable resource for practitioners seeking efficient and accessible solutions for harnessing the power of heterogeneous clusters in DL acceleration, with implications for advancements in AI and computational efficiency. © 2024 IEEE.

关键词： Computational efficiency

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Emulation of Recommendation System Using QSVT Framework of QiML on CPU 4th

Emulation of Recommendation System Using QSVT Framework of...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Patil, Akshay Tiwari, Abhishek Khaneja, Vivek Singh, Nagendra Embedded Systems R&D Centre for Development of Advanced Computing Noida India

ISBN: (纸本)9789819757022

Classical computing (CC) takes place in the binary form of 0 and 1, whereas Quantum computing (QC) takes place adhering to fundamental principles of quantum mechanics, i.e., superposition, entanglement, and tunneling. Humanity is in the race to show that QC is better than CC in terms of time, accuracy, and memory requirements. But since Fault-Tolerant QC hardware with solving problems computationally exhaustive for today’s CC are out of reach, we in this paper emulate Use case of the Recommendation System on Quantum Singular Value Transform Framework (QSVT) of Quantum-Inspired Machine Learning (QiML) on CPU using the Qiskit Library, pyqsp library and have shown a quantum advantage in terms of accuracy, in terms of time and memory requirement classical implementation is a winner as computations in the Quantum Emulations are larger owing to unitary matrix conversions of input matrix and considerable depths, but these depths can be reduced to approximate the target polynomial function in which case the Quantum Implementation is comparable to/better than its counterpart at cost of *** entanglement principle of QM is used in the QSVT Circuit for entangling the QSVT Qubit with unitary Qubits by using the CNOT gates. Also, the entanglement principle is inherently present in the Input User Preference Rating Unitary Matrix as well, if the exact tensor decomposition at a break-off qubit length of the input user preference unitary matrix is not *** input quantum state of our QSVT circuit can be initialized to the superposition of input user states to access all the items corresponding to all the users in one run of the circuit. Hence by using the fundamental principles of quantum mechanics, we have shown quantum advantage over classical implementation in terms of accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Qubits

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Federated Machine Learning Architecture for Heterogeneous Quantum Devices 1

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3rd International Conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

作者： Singh, Nagendra Tiwari, Abhishek Khaneja, Vivek Embedded Systems Division Centre for Development of Advanced Computing Noida India

ISBN: (数字)9789819939633

ISBN: (纸本)9789819939626

Quantum computing shows immense potential for solving traditional, near to impossible, complex problems. It also shows how the performance and speed can be improved for the solution of existing classical problems by using quantum mechanics. However, the computation power of current computers, servers, and supercomputers is well enough to speed up the existing problem solutions. But as data generated from various resources increases rapidly, in current scenarios more than 2.5 quintillion bytes of data is generated daily and is expected to be 200+ zettabytes on the cloud by 2025. Handling and storing such a massive amount of data is a significant issue for current computing and storage devices. Quantum computing has the capability to resolve this issue with quantum rules. In this paper, the task of pattern finding and recognizing in data (i.e., machine learning) is explored with the help of quantum computing and introducing a collaborative learning architecture. It enables collaborative computation from heterogeneous quantum computing devices for machine learning tasks. Also, it can be adapted to any quantum machine learning approach and speed up the learning phase with better data privacy. The proposed scheme is implemented with the MNIST dataset to compare with current existing research work. Results show that the proposed architecture matches existing research work’s performance with the additional features of privacy and heterogeneity of quantum devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Towards Quantum-inspired Evolutionary Algorithms for Denoising PCG Signals 7

Towards Quantum-inspired Evolutionary Algorithms for Denoisi...

引用

7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity University Amity School of Engineering and Technology Noida India Amity University Amity Innovation and Design Centre Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9798350368673

This document aims to reaffirm the superiority of quantum-inspired evolutionary algorithms(QiEA) over conventional algorithms. The study proposes a novel QiEA with sorted mutation and greedy local restart (SMGLR) algorithm for finding the optimal coefficients of a filter using a multi-objective optimization function. PCG signals sourced from the Washington Hearts Sounds and Murmurs database and the Michigan Heart Sounds database were filtered using this enhanced filter. Comparative analysis was conducted against the proposed algorithm and established techniques such as Wavelet-based denoising and Variable Mode decomposition. Results: The results revealed that the filter designed using QiEA-SMGLR exhibited superior performance in terms of improved Signal-to-Noise Ratio (SNR) in both comparisons. A comprehensive statistical analysis yielded a correlation coefficient of close to 0.99 indicating strong signal integrity. The QiEA-SMGLR algorithm outperforms Wavelet-based and VMD methods by improving the output SNR by 20% to 100%. This variance can be attributed to the intensity of noise introduced. The result reaffirms the hypothesis that the QiEA-SMGLR algorithm is superior in improving signal clarity and precision over traditionally popular techniques. The improvement in the analysis of Phonocardiogram (PCG) signals could help improve patient diagnostics, amidst the fight against global cardiac problems. © 2024 IEEE.

关键词： Quantum computers

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Improving Design Accuracy of a Finite Impulse Response Fractional Order Digital Differentiator Filter Using Quantum-inspired Evolutionary Algorithm 14

Improving Design Accuracy of a Finite Impulse Response Fract...

引用

14th International Conference on Cloud computing, Data Science and Engineering, Confluence 2024

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity School of Engineering and Technology Amity University Noida India Amity Innovation and Design Centre Amity University Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9798350344837

In this paper, a quantum-inspired evolutionary algorithm (QiEA) is applied to determine the optimal coefficients of a finite impulse response fractional-order digital differentiator filter. QiEA brings the power of quantum-inspired methods to the principles of natural evolution, which uses operators to gradually improve solutions over time. The design accuracy in terms of absolute magnitude error (AME) and phase error achieved in QiEA was compared with the AME achieved by the genetic algorithm (GA) and cuckoo search algorithm (CSA). In addition, we investigated the impact of increasing population sizes on these design parameters of the fractional order (FOD) digital differentiator filter. The simulation results demonstrate conclusively that the FOD digital differentiator's design accuracy using QiEA beats both CSA and GA in terms of accuracy, error function reduction, and identifying optimal filter coefficients. © 2024 IEEE.

关键词： Genetic algorithms

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MNIST Fashion Classification Using Quantum Convolutional Neural Networks

MNIST Fashion Classification Using Quantum Convolutional Neu...

引用

2024 IEEE International Conference on Computer Vision and Machine Intelligence, CVMI 2024

作者： Ali, Nasir Tiwari, Abhishek Neiwal, Rahul Kumar Centre for Development of Advanced Computing Embedded Systems Division Noida India Ministry of Electronics and Information Technology Electronics Division New Delhi India

ISBN: (纸本)9798350376876

This paper explores the innovative application of Quantum Convolutional Neural Networks (QCNN) in the classification of the MNIST fashion dataset. Quantum computing presents novel opportunities for enhancing machine learning algorithms, particularly in handling complex datasets. The MNIST fashion dataset is a large collection of Zalando's article images, serves as an ideal candidate for demonstrating the efficacy of QCNN. This paper dives into the implementation of QCNN, compares QCNN's performance with traditional neural networks, and further evaluates QCNN's potential in revolutionizing image classification tasks in the realm of Quantum Machine Learning. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigating Quantum-Inspired Evolutionary Algorithm with Restarts for Solving IEEE CEC 2022 Benchmark Problems 5th

Investigating Quantum-Inspired Evolutionary Algorithm with R...

引用

5th International Conference on Recent developments in Control, Automation and Power Engineering, RDCAPE 2023

ISBN: (纸本)9789819600465

This paper investigates the effectiveness of two restart methods, local and global restarts, in enhancing the performance of the quantum-inspired evolutionary algorithm (QiEA) to solve optimization problems. The study compares the result of these modifications by testing their performance against the widely recognized and challenging IEEE CEC 2022 benchmark functions. The objective is to analyze the impact of the restart methods on the convergence and optimization outcomes of the QiEA. Through experimental evaluation, it was sought to determine whether the inclusion of restart methods in the QiEA framework would lead to improved convergence toward the optimal solution. Also, a novel Greedy Local Restart algorithm is proposed. The results demonstrate that the QiEA variants consistently outperform the basic QiEA, achieving superior optimization results in terms of convergence and accuracy. Restart approaches are thus observed as an indispensable asset in handling complex optimization problems, greatly enhancing the effectiveness and applicability of evolutionary algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Evolutionary algorithms

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Machine Learning Based Framework for Cryptocurrency Price Prediction 3

Machine Learning Based Framework for Cryptocurrency Price Pr...

引用

3rd International Conference on Secure Cyber computing and Communications, ICSCCC 2023

作者： Singh, Mrityunjay Jakhar, Amit Kumar Juneja, Aashima Pandey, Shivam School of Computing Indian Institute of Information Technology Una Una India Jaypee University of Information Technology Department of CS/IT Waknaghat Solan India Centre for Development of Advanced Computing Noida Noida India

ISBN: (纸本)9798350300710

Cryptocurrency, a digital currency, acts as a medium of exchange through the Internet. The main agenda behind cryptocurrency being so popular these days is the desire for reliable, long-term value without the involvement of any central authority like banks. The power lies in the hands of the currency holders which resolve the problems of the traditional currencies by adopting a decentralized system. Predicting the future price of different cryptocurrencies is a prominent area of interest for individuals or investors. In this work, we use a dataset collected from the coinmarketcap website for the duration of September 2014 to March 2022. The outcome of this work is compared to the existing algorithms for time series data analysis namely the Auto Regressive Moving Average Model (ARIMA), FbProphet, and several ensemble models on the basis of their accuracy in predicting the future price. We also create different ensemble frameworks for the prediction of the cryptocurrency price. To form the ensemble models, we initially select the three best-performing regression models on the dataset, namely Extra Trees, Random Forest, and Decision Trees Regressors. Our findings indicate that the ARIMA model performs better than the ensemble model with the lowest RMSE MAE and MSE. © 2023 IEEE.

关键词： Bitcoin

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