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RSSI ESTIMATION FOR CONSTRAINED INDOOR WIRELESS NETWORKS USING ANN

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arXiv 2024年

作者： Arif, Samrah Khan, M. Arif Rehman, Sabih ur School of Computing Mathematics and Engineering Charles Sturt University Australia

In the expanding field of the Internet of Things (IoT), wireless channel estimation is a significant challenge. This is specifically true for low-power IoT (LP-IoT) communication, where efficiency and accuracy are extremely important. This research establishes two distinct LP-IoT wireless channel estimation models using Artificial Neural Networks (ANN): a Feature-based ANN model and a Sequence-based ANN model. Both models have been constructed to enhance LP-IoT communication by lowering the estimation error in the LP-IoT wireless channel. The Feature-based model aims to capture complex patterns of measured Received Signal Strength Indicator (RSSI) data using environmental characteristics. The Sequence-based approach utilises predetermined categorisation techniques to estimate the RSSI sequence of specifically selected environment characteristics. The findings demonstrate that our suggested approaches attain remarkable precision in channel estimation, with an improvement in MSE of 88.29% of the Feature-based model and 97.46% of the Sequence-based model over existing research. Additionally, the comparative analysis of these techniques with traditional and other Deep Learning (DL)-based techniques also highlights the superior performance of our developed models and their potential in real-world IoT applications. © 2024, CC BY-NC-ND.

关键词： Channel estimation

Cryptanalysis of the SIMON Cypher Using Neo4j

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Cryptanalysis of the SIMON Cypher Using Neo4j

Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Jonathan Cook Sabih ur Rehman M. Arif Khan School of Computing Mathematics and Engineering Charles Sturt University Australia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

The exponential growth in the number of Internet of Things (IoT) devices has seen the introduction of several Lightweight Encryption Algorithms (LEA). While LEAs are designed to enhance the integrity, privacy and security of data collected and transmitted by IoT devices, it is hazardous to assume that all LEAs are secure and exhibit similar levels of protection. To improve encryption strength, cryptanalysts and algorithm designers routinely probe LEAs using various cryptanalysis techniques to identify vulnerabilities and limitations of LEAs. Despite recent improvements in the efficiency of cryptanalysis utilising heuristic methods and a Partial Difference Distribution Table (PDDT), the process remains inefficient, with the random nature of the heuristic inhibiting reproducible results. However, the use of a PDDT presents opportunities to identify relationships between differentials utilising knowledge graphs, leading to the identification of efficient paths throughout the PDDT. This paper introduces the novel use of knowledge graphs to identify intricate relationships between differentials in the SIMON LEA, allowing for the identification of optimal paths throughout the differentials, and increasing the effectiveness of the differential security analyses of SIMON.

关键词： Data privacy Ciphers Data security Knowledge graphs Encryption Internet of Things Protection Probes Computer security

CRYPTANALYSIS OF THE SIMON CYPHER USING NEO4J

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arXiv 2024年

作者： Cook, Jonathan ur Rehman, Sabih Khan, M. Arif School of Computing Mathematics and Engineering Charles Sturt University Australia

关键词： Knowledge graph

LIGHTWEIGHT CRYPTANALYSIS OF IOT ENCRYPTION ALGORITHMS: IS QUOTA SAMPLING THE ANSWER?

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arXiv 2024年

作者： Cook, Jonathan Rehman, Sabih ur Khan, M. Arif School of Computing Mathematics and Engineering Charles Sturt University Australia

Rapid growth in the number of small sensor devices known as the Internet of Things (IoT) has seen the development of lightweight encryption algorithms. Two well-known lightweight algorithms are SIMON and SIMECK which have been specifically designed for use on resource-constrained IoT devices. These lightweight encryption algorithms are based on the efficient Feistel block structure which is known to exhibit vulnerabilities to differential cryptanalysis. Consequently, it is necessary to test these algorithms for resilience against such attacks. While existing state-of-the-art research has demonstrated novel heuristic methods of differential cryptanalysis that improve time efficiency on previous techniques, the large state sizes of these encryption algorithms inhibit cryptanalysis time efficiency. In this paper, we introduce Versatile Investigative Sampling Technique for Advanced Cryptanalysis (VISTA-CRYPT) - a time-efficient enhancement of differential cryptanalysis of lightweight encryption algorithms. The proposed technique introduces a simple framework of quota sampling that produces state-of-the-art results with time reductions of up to 76% over existing techniques. Further, we present a preliminary graph-based analysis of the output differentials for the identification of relationships within the data and future research opportunities to further enhance the performance of differential cryptanalysis. The code designed for this work and associated datasets will be available at https://***/johncook1979/simon-cryptanalysis. © 2024, CC BY-NC-ND.

关键词： Heuristic methods

DEEP LEARNING APPROACHES TO INDOOR WIRELESS CHANNEL ESTIMATION FOR LOW-POWER COMMUNICATION

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arXiv 2024年

作者： Arif, Samrah Arif Khan, Muhammad Rehman, Sabih ur School of Computing Mathematics and Engineering Charles Sturt University Australia

In the rapidly growing development of the Internet of Things (IoT) infrastructure, achieving reliable wireless communication is a challenge. IoT devices operate in diverse environments with common signal interference and fluctuating channel conditions. Accurate channel estimation helps adapt the transmission strategies to current conditions, ensuring reliable communication. Traditional methods, such as Least Squares (LS) and Minimum Mean Squared Error (MMSE) estimation techniques, often struggle to adapt to the diverse and complex environments typical of IoT networks. This research article delves into the potential of Deep Learning (DL) to enhance channel estimation, focusing on the Received Signal Strength Indicator (RSSI) metric - a critical yet challenging aspect due to its susceptibility to noise and environmental factors. This paper presents two Fully Connected Neural Networks (FCNNs)-based Low Power (LP-IoT) channel estimation models, leveraging RSSI for accurate channel estimation in LP-IoT communication. Our Model A exhibits a remarkable 99.02% reduction in Mean Squared Error (MSE), and Model B demonstrates a notable 90.03% MSE reduction compared to the benchmarks set by current studies. Additionally, the comparative studies of our model A with other DL-based techniques show significant efficiency in our estimation models. © 2024, CC BY-NC-SA.

关键词： Internet of things

Ai-Powered Abnormal Behaviour Detection in Smart Aged Care: Incremental Learning with Minimal User Input

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SSRN

SSRN 2024年

作者： Rahman, Md Geaur ur Rehman, Sabih Debnath, Tanmoy Dahshan, Mostafa Khokhar, Rashid Fuskelay, Aayush Islam, Md Zahidul School of Computing Mathematics and Engineering Charles Sturt University Australia

Given the increasing number of elderly individuals, it's essential to implement multi-device monitoring to keep a record of older adults' Activities of Daily Living (ADL). This approach is crucial for supporting independent living and early detection of mental disorders such as depression and Alzheimer's disease. Early signs of these disorders can be identified through deviations in standard behaviour patterns, including reduced attention to personal hygiene, changes in sleep habits, and a decline in social engagement. However, these deviations (also known as anomalies) are often subtle and challenging to detect. The use of non-intrusive monitoring devices, in particular, poses the risk of generating false positives or overlooking significant signs due to the limitations of anomaly detectors. This issue may deter caregivers from adopting these technological solutions. Besides, the definition of an anomaly depends on the individual's situation, requiring approaches that can adapt to unique cases. Existing anomaly detection techniques, which mainly utilise traditional Machine Learning (ML) and Deep Learning (DL) models, face two significant challenges: (i) the lack of complete ground truth data since activities are recorded over time, and (ii) the restricted memory capacity, which limits the amount of ADL data that can be stored for model training. Additionally, these methods can only classify an activity as normal or abnormal without providing further details about the nature of the anomaly, such as disruptions in sleep patterns or personal hygiene, nor can they explain the underlying factors of the anomaly. To address these challenges, this paper introduces an incremental learning system named the Adaptive Learning system for Detecting unusual behaviour of older adults, abbreviated as LeaD. Our proposed method employs an existing methodology to facilitate incremental learning, enabling it to accommodate new anomalies by leveraging past data. LeaD incorporates user f

关键词： Adversarial machine learning

Missing Data Recovery for Downstream Learning in Low-Cost Environmental Sensor Networks 9

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Missing Data Recovery for Downstream Learning in Low-Cost En...

9th International Conference on Information Technology Trends, ITT 2023

作者： Agbo, Benjamin Al-Aqrabi, Hussain Alsboui, Tariq Hussain, Muhammad Hill, Richard Keele University School of Computing and Mathematics Keele United Kingdom Higher Colleges of Technology Faculty of Computer Information Science Sharjah United Arab Emirates University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom

ISBN: (纸本)9798350327502

The development of low-cost sensor networks (LCSNs) has facilitated the generation of vast amounts of environmental monitoring data in air quality monitoring stations. State-of-the-art research has focused on developing robust models to detect and mitigate events which may impact societies. Making adequate predictions is important for meeting the sustainability needs of future generations. Therefore, quality data is required to produce accurate results for downstream analytics and visualisations. When dealing with large amounts of data, it is however common to observe numerous amounts of missing data. This is a challenge for data miners because various methods for data analysis only work well on complete datasets. A traditional approach to handling missing data is to discard instances of missing values and only use complete cases for analysis. However, research has shown that this approach is not practical especially when large amounts of data are missing. This led to an increased need to develop strategies for replacing missing values with plausible values through imputation. This study presents an imputation strategy called k-BFMVI for recovering missing values before training downstream regression models for low cost sensor calibration. Experiments simulated missingness from 10% to 40% using MCAR and MAR mechanisms and the performance of the proposed technique was measured against state-of-the-art techniques. Overall, the proposed algorithm recorded the best imputation accuracy as opposed to existing techniques and showed significant improvements on downstream learning. © 2023 IEEE.

关键词： Sensor networks

Investigation of Heat exchanger performance with delta-wing tape inserts for high Reynolds Number using various machine learning techniques 14

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Investigation of Heat exchanger performance with delta-wing ...

14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Rodrigues, Gizelle Srivastava, Neetu Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

The present study utilizes the support vector regression (SVR) technique with a cubic kernel to forecast the performance of a double-pipe heat exchanger using T-W tape inserts with wing-width ratios of 0.31, 0.47, and 0.63. The SVR model that is established is applied to predict two key parameters, namely, the thermal performance (η) and the friction factor (f). Various scenarios are examined by considering the Reynolds number (Re) range from 5,500 to 14,500 and the wing-width ratio as inputs. The outcomes indicate that the SVR model accurately predicts the experimental data, as evidenced by the minimal mean square error (MSE) obtained when compared to the experimental values. Furthermore, the proposed SVR-based method predicts the thermal parameters with minimal variance. In addition, multiple regression models are utilized to evaluate the effectiveness of the proposed model, which shows that the recommended SVR technique is useful for predicting thermal parameters with minimal variance. It is noteworthy that the SVR model with the cubic kernel outperforms other modeling approaches in predicting the performance of the T-W double-pipe heat exchanger. © 2023 IEEE.

关键词： Mean square error

GPT-Enabled Cybersecurity Training: A Tailored Approach for Effective Awareness

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arXiv 2024年

作者： Al-Dhamari, Nabil Clarke, Nathan School of Engineering Computing & Mathematics University of Plymouth United Kingdom

This study explores the limitations of traditional Cybersecurity Awareness and Training (CSAT) programs and proposes an innovative solution using Generative Pre-Trained Transformers (GPT) to address these shortcomings. Traditional approaches lack personalization and adaptability to individual learning styles. To overcome these challenges, the study integrates GPT models to deliver highly tailored and dynamic cybersecurity learning experiences. Leveraging natural language processing capabilities, the proposed approach personalizes training modules based on individual trainee profiles, helping to ensure engagement and effectiveness. An experiment using a GPT model to provide a real-time and adaptive CSAT experience through generating customized training content. The findings have demonstrated a significant improvement over traditional programs, addressing issues of engagement, dynamicity, and relevance. GPT-powered CSAT programs offer a scalable and effective solution to enhance cybersecurity awareness, providing personalized training content that better prepares individuals to mitigate cybersecurity risks in their specific roles within the organization. © 2024, CC BY.

关键词： Cybersecurity