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arXiv 2023年

作者： Akter, Mst Shapna Shahriar, Hossain Ahamed, Sheikh Iqbal Gupta, Kishor Datta Rahman, Muhammad Mohamed, Atef Rahman, Mohammad Rahman, Akond Wu, Fan Dept. of Computer Science Kennesaw State University Kennesaw United States Dept. of Information Technology Kennesaw State University Kennesaw United States Dept. of Computer Science Marquette University Wisconsin United States Dept. of Computer Science Clark Atlanta University GA United States Dept. of Information Technology Clayton State University Morrow United States Dept. of Information Technology Georgia Southern University Georgia Dept. of Electrical and Computer Engineering Florida International University Florida United States Dept. of Computer Science and Software Engineering Auburn University Auburn United States Dept of Computer Science Tuskegee University Tuskegee United States

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real-world problems. QML has the potential to address cybersecurity-related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days. © 2023, CC BY.

关键词： Support vector machines

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Systematic Literature Review: Evaluating Effects of Adversarial Attacks and Attack Generation Methods

Systematic Literature Review: Evaluating Effects of Adversar...

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Energy, Power, Environment, Control, and Computing (ICEPECC), International Conference on

作者： Saira Akram Sibghat Ullah Bazai Muhammad Imran Ghafoor Shah Marjan Muhammad Hamza Syed Ali Asghar Shah Dept. of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Engineering Pakistan Television Corporation Lahore Pakistan Dept. of Software Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Computer Science Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan

Advancement in Artificial Intelligence (AI) aims to train the Machine Learning (ML) Models in such a way that they would be able to take decisions spontaneously, however on the other side attackers attempt to manipulate the results generated by these models which makes the application of these models difficult in security-critical areas including classification of medical images, autonomous system installed in vehicles, street lights, malware detection and identification of a person as criminal or innocent. Advancement in research proved the vulnerability of these classifiers due to adversarial attacks that can alter their result in training as well as in testing phase of the model. Causative attacks are the training phase attacks whereas the attacks done at testing phase are the exploratory attacks. This systematic literature Review (SLR) is conducted to gain in-depth knowledge of adversarial attacks, which is the most effective type of exploratory attack, parameters on which these attacks are based, along with the most conventional methods of generating adversarial attacks.

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A Novel Energy-Efficient Cross-Layer Design for Scheduling and Routing in 6TiSCH Networks

arXiv

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arXiv 2024年

作者： Hannachi, Ahlam Jaafar, Wael Bitam, Salim Ouazene, Nabil University of Biskra Dept. Computer Science BP 145 RP Biskra Biskra07000 Algeria Dept. Software and IT Engineering 1100 R. Notre Dame W. MontrealQCH3C 1K3 Canada University of Batna Dept. Computer Science Univ. Batna Batna05000 Algeria

The 6TiSCH protocol stack plays a vital role in enabling reliable and energy-efficient communications for the Industrial Internet of Things (IIoT). However, it faces challenges, including prolonged network formation, inefficient parent switching, high control packet overhead, and suboptimal resource utilization. To tackle these issues, we propose in this paper a novel cross-layer optimization framework aiming to enhance the coordination between the Scheduling Function (SF), the Routing Protocol for Low-Power and Lossy Networks (RPL), and queue management. Our solution introduces a slot-aware parent switching mechanism, early slot reservation to mitigate queue overflow, and a refined slot locking strategy to improve slot availability. To reduce control overhead, the proposed method merges 6P cell reservation information into RPL control packets (DIO/DAO), thus minimizing control exchanges during parent switching and node joining. Optimized slot selection further reduces latency and jitter. Through extensive simulations on the 6TiSCH simulator and under varying network densities and traffic loads, we demonstrate significant improvements over the standard 6TiSCH benchmark in terms of traffic load, joining time, latency, and energy efficiency. These enhancements make the proposed solution suitable for time-sensitive IIoT applications. © 2024, CC BY.

关键词： Queueing networks

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Accelerating N-bit Operations over TFHE on Commodity CPU-FPGA

Accelerating N-bit Operations over TFHE on Commodity CPU-FPG...

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IEEE International Conference on computer-Aided Design

作者： Kevin Nam Hyunyoung Oh Hyungon Moon Yunheung Paek Dept. of ECE & ISRC Seoul National University Dept. of AI•Software Gachon University Dept. of Computer Science and Engineering UNIST

TFHE is a fully homomorphic encryption (FHE) scheme that evaluates Boolean gates, which we will hereafter call Tgates, over encrypted data. TFHE is considered to have higher expressive power than many existing schemes in that it is able to compute not only N-bit Arithmetic operations but also Logical/Relational ones as arbitrary ALR operations can be represented by Tgate circuits. Despite such strength, TFHE has a weakness that like all other schemes, it suffers from colossal computational overhead. Incessant efforts to reduce the overhead have been made by exploiting the inherent parallelism of FHE operations on ciphertexts. Unlike other FHE schemes, the parallelism of TFHE can be decomposed into multilayers: one inside each FHE operation (equivalent to a single Tgate) and the other between Tgates. Unfortunately, previous works focused only on exploiting the parallelism inside Tgate. However, as each N-bit operation over TFHE corresponds to a Tgate circuit constructed from multiple Tgates, it is also necessary to utilize the parallelism between Tgates for optimizing an entire operation. This paper proposes an acceleration technique to maximize performance of a TFHE N-bit operation by simultaneously utilizing both parallelism comprising the operation. To fully profit from both layers of parallelism, we have implemented our technique on a commodity CPU-FPGA hybrid machine with parallel execution capabilities in hardware. Our implementation outperforms prior ones by 2.43× in throughput and 12.19× in throughput per watt when performing N-bit operations under the 128-bit quantum security parameters.

关键词： Quantum computing Layout Graphics processing units Parallel processing Logic gates Throughput Nonhomogeneous media

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Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review

Frameworks, Applications and Challenges in Streaming Big Dat...

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Innovations in computer science & software engineering (ICONICS), International Conference on

作者： Syed Muhammd Nabeel Mustafa Muhammad Umer Farooque Muhammad Tahir Shariq Mahmood Khan Rohail Qamar Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed University of Engineering and Karachi Pakistan

Data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term "streaming Big data" refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. Instantaneous analytics of the real-time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big Data analytics are applied as well as the difficulties encountered.

关键词： Transportation Medical services Big Data Real-time systems Smart grids Internet of Things Sparks

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RELIABILITY ANALYSIS OF MONITORING SYSTEM FOR EXTRATERRESTRIAL HABITAT USING CTMC AND EMPIRICAL EVALUATION

arXiv

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arXiv 2023年

作者： Band, Saurabh Stechmann, Florian Unnikrishnan, Malavika Attarha, Shadi Heinicke, Christiane Willig, Andreas Förster, Anna Dept. Sustainable Communication Networks University of Bremen Germany Center of Applied Space Technology and Microgravity University of Bremen Germany Dept. Computer Science and Software Engineering University of Canterbury New Zealand

Much research has been done to settle a civilization on Mars in recent years. Among the various required resources for this civilization, the habitat is one of the crucial resources to live on Mars. Such an extraterrestrial habitat is designed to provide a safe place to live during the initial missions. It is equipped with monitoring and life support systems to ensure the astronauts’ safety. In this work, we present a robust monitoring system with a use case for extraterrestrial habitats. Similar to a typical monitoring system, it consists of sensor nodes and a gateway connected through a wireless communication channel. In our system, we introduce robustness to various failures that can occur after deployment, namely, board, sensor and gateway failure, in the form of redundancy. For each failure, the problem is tackled differently. For the first two types, we use additional hardware as backup, while for the last type, we use neighbouring devices as backups. The backup devices function as replacements for the failed component, which helps the system to collect the data which would otherwise be lost. We evaluate how much the performance of the system improves by using backup devices. We use a Continuous-Time Markov chain for the theoretical evaluation and an experimental setup that includes the hardware prototype for the empirical evaluation. We also analyze the effect of a simple medium access mechanism on the system’s performance in the presence of heavy noise on the channel. Based on our requirements, we use a simple custom medium access control (MAC) algorithm called Slotted-ALOHA with Random Back-off (SARB) to make communication reliable. We demonstrate that around 30 - 34% of the packets are recovered, with the use of backup devices (redundancy), which would otherwise be lost in case of failures. We also demonstrate that the system’s performance improves by 3.8 - 13.2% with the use of a simple medium access technique (SARB). © 2023, CC BY.

关键词： Sensor nodes

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Generative Adversarial Networks for Anomaly Detection: A Systematic Literature Review

Generative Adversarial Networks for Anomaly Detection: A Sys...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Shah Noor Sibghat Ullah Bazai Muhammad Imran Ghafoor Shah Marjan Saira Akram Fatima Ali Dept. Computer Science Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Electrical Engineering Superior University Pakistan Television Corporation Lahore Pakistan Dept. of Software Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan

In numerous research areas, anomaly identification is a major problem. Identifying and properly classifying data as anomalous is a challenging task that is resolved in various manners over the years. Different approaches like traditional, supervised, unsupervised, and semi-supervised are used for anomaly detection. In the literature, various machine learning-based anomaly detection algorithms exist. It is challenging to choose one anomaly detection algorithm from the several available algorithms because each algorithm puts forward its good detection performance. In recent years, generative adversarial networks have shown remarkable results for anomaly classification. This paper aims to represent a systematic literature review of generative adversarial network-based approaches for anomaly detection and highlights their pros.

关键词： Data privacy Machine learning algorithms Systematics Bibliographies Machine learning Generative adversarial networks Mathematics

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Examining the Benefits of AI in Wearable Sensor-based Healthcare Solutions

Examining the Benefits of AI in Wearable Sensor-based Health...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Prashamsh Takkalapally Balaram Yadav Kasula Sachin C. Patil J. Logeshwaran Department of Engineering Computing and Mathematical Sciences Lewis University USA Dept of Information Technology University Of The Cumberlands Williamsburg KY USA Senior Software Engineer USA Department of Computer Science CHRIST (Deemed to be University) Bengaluru Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The emergence of the AI generation has introduced adjustments to the manner healthcare solutions are advanced and applied. Wearable sensor-based total healthcare answers were revolutionized by leveraging AI in diverse packages. AI may be used to enhance the accuracy and precision of facts analysis, simplify information collection procedures, and pick out affected person-unique styles from the accrued data. Furthermore, AI can provide both actual-time and predictive analytics abilities, which are particularly useful for devising personalized healthcare offerings. Its provision of scalable systems hurries up the traits of various programs and has enabled personalized healthcare answers to be deployed in a shorter length. In spite of a number of the associated challenges, including data privacy troubles, AI-based wearable sensor-based healthcare answers can revolutionize patient tracking and timely detection of capability health conditions, improve preventive fitness care, and decrease healthcare fees.

关键词： Industries Data privacy Accuracy Medical services Artificial intelligence Prognostics and health management Predictive analytics

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture

Multispectral Images and IoT Based Tea Plantation Monitoring...

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Advanced Research in Computing (ICARC), International Conference on

作者： K. P. W. D. V. Kariyawasam P. N. Kahandagamage M. R. R. Fernando J. M. Fernandopulle Vishan Jayasinghearachchi Kapila Dissanayaka Dept. of Software Engineering Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Computer Science Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments.

关键词： Scalability Plantations Systems architecture Crops Real-time systems Internet of Things Resource management Reliability Monitoring Stress

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An improved AI-driven Data Analytics model for Modern Healthcare Environment

An improved AI-driven Data Analytics model for Modern Health...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sachin C. Patil Prashamsh Takkalapally Balaram Yadav Kasula J. Logeshwaran Senior Software Engineer USA Department of Engineering Computing and Mathematical Sciences Lewis University USA Dept of Information Technology University Of The Cumberlands Williamsburg KY USA Department of Computer Science CHRIST (Deemed to be University) Bengaluru Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years.

关键词： Accuracy Hospitals Face recognition Medical services Organizations Manuals Real-time systems Data models Artificial intelligence Information analysis

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