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Unveiling the Safety Aspects of DevSecOps: Evolution, Gaps and Trends

Recent Advances in computer Science and communications 2023年第3期16卷 61-69页

作者： Ramaj, Xhesika Sánchez-Gordón, Mary Chockalingam, Sabarathinam Colomo-Palacios, Ricardo Department of Computer Science and Communication Faculty of Computer Sciences Engineering and Economics Østfold University College Halden Norway Department of Risk Safety and Security Institute for Energy Technology Halden Norway

Background: The popularity of DevSecOps is on the rise because it promises to integrate a greater degree of security into software delivery pipelines. However, there is also an unacceptable risk related to safety that cannot be overlooked, given the importance of this aspect in many industries. Objective: The objective of this study is to provide an overview of the safety aspects reported in the literature on DevSecOps. This study also characterizes such aspects and identifies the gaps that may lead to future research work. Methods: A systematic literature review was conducted using five well-known academic databases. The search was executed in September 2021 and March 2022 to identify relevant studies. Results: The search returned 114 academic studies. After the screening process, five primary studies published between 2019 and 2021 were selected. These studies were analyzed thoroughly to identify the safety aspects. Then, we categorized them into three main groups: (i) risk-related safety aspects, (ii) human-related aspects, and (iii) management aspects. Conclusion: Safety is an important characteristic that is becoming more critical as the number of critical systems grows. This review reveals that only a scarce number of studies are focusing on safety in DevSecOps. However, those studies gave us some insights into this topic. Therefore, our main observation is that this topic has not yet been completely explored in the academic literature. This review can encourage reflection and discussion between the safety and security communities. © 2023 Bentham Science Publishers.

关键词： Accident prevention

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Statistical Characterization and Performance Analysis of Distributed Multi-RIS-Aided Wireless communication System 6

Statistical Characterization and Performance Analysis of Dis...

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6th International Conference on Electrical Information and communication Technology, EICT 2023

作者： Islam, Mohammad Shariful Mostafa, Md Golam Military Institute of Science and Technology Department of Electrical Electronic and Communication Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370836

This study proposes two distributed multi-reconfigurable intelligent surface (MRIS)-aided systems and performs their statistical characterization and modeling. The mathematical framework is provided for both schemes to calculate the end-to-end (e2e) fading channel distribution. For both the Gamma and Log-Normal distributions, the approximate cumulative distribution functions (CDF) and probability density functions (PDF) show a remarkable alignment with their corresponding true CDFs and PDFs in both schemes. Subsequently, the performance of both schemes is evaluated in terms of outage probability (OP) and ergodic capacity (EC). It is observed that Scheme One achieves better performance than Scheme Two. Results for OP and EC are presented for both schemes, and it is evident that using multiple antennas in the transmission significantly improves the channel capacity. This research contributes valuable insights into optimizing distributed MRIS-aided wireless systems, highlighting the potential benefits of the proposed schemes for advancing wireless communication capabilities. © 2023 IEEE.

关键词： Probability density function

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Performance of MISO Systems with Alamaouti Transmit Diversity and Antenna Selection in TDD and FDD 4th

Performance of MISO Systems with Alamaouti Transmit Divers...

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4th International Conference on Emerging Technology Trends in electronics, communication and Networking, ET2ECN 2021

作者： Trivedi, Dehit H. Joshi, Neel N. Trivedi, Y.N. Department of Electronic and Communication Engineering Institute of Technology Nirma University Ahmedabad382481 India

ISBN: (纸本)9789811967368

In this paper, the focus is to obtain performance of a multiple input and single output (MISO) wireless communication system in a real-time scenarios of time division duplex (TDD) and frequency division duplex (FDD). We consider a MISO system equipped with 2N transmit antennas. In the total N pairs of antenna, there is a spatial correlation in each pair (two antennas) only. We assume quasi-static Rayleigh fading channels. We select one pair of antennas with the highest channel power gain. In case of FDD, we assume antenna selection and detection, both based on imperfect channel state information (CSI), whereas in case of TDD, detection is based on imperfect CSI, but antenna selection is to be done using perfect CSI. Finally, we use Alamouti transmit diversity. For this system, we present the performance in terms of bit error rate (BER) versus avg. signal-to-noise ratio (SNR) and outage probability versus avg. SNR using Monte Carlo simulations. We have seen the adverse effect of spatial correlation and imperfect CSI on the performance. We conclude that TDD outperforms the FDD due to higher antenna selection gain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Antennas

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Machine Learning for Signal Processing in communication Systems

Machine Learning for Signal Processing in Communication Syst...

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2024 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2024

作者： Reddy, PoliLokeshwara Shreyas, Chanda Bhargavi, Pamulapati. Raja, G.V. Rani, D. Leela Maheswari, S. Anurag University Department of Electronics and Communication Engineering Telangana Hyderabad500088 India Kkr & Ksr Institute of Technology and Sciences Department of Computer Science Engineering Andhra Pradesh Guntur522017 India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru562106 India Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India Kongu Engineering College Department of Electrical and Electronic Engineering Tamil Nadu Perundurai638060 India

ISBN: (纸本)9798331543617

The traditional styles of signal processing are passing constraints in their capability to handle the different and dynamic character of ultramodern data transfers. This is because communication systems are getting increasingly complicated. The operation of machine literacy presents a potentially useful answer since it enables data-driven, adaptive approaches that have the eventuality to ameliorate signal processing performance. This composition provides a comprehensive examination of a variety of machine literacy ways and their operations in important disciplines such as modulation recognition, noise reduction, channel estimation, and signal discovery. The study demonstrates how machine literacy ways may vastly increase the effectiveness, delicacy, and robustness of signal-processing tasks in communication systems. This is fulfilled through detailed analysis and comparison. With the help of machine literacy, unborn communication systems will be suitable to attain advanced situations of robotization and intelligence, which will pave the way for further developments in areas similar to wireless dispatches, the Internet of Effects(IoT), and other areas. In light of these findings, it's clear that machine literacy can fully transfigure assiduity by furnishing new shoes and approaches to the optimization of signal processing in the unborn generation of communication networks. © 2024 IEEE.

关键词： Noise abatement

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Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leveraging Spatial and Temporal Dependencies 4

Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leve...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vallileka, N. Rajkumar, G.Vinoth Krishnan, R. Santhana Shankar, S. Vijay Francis Raj, J. Relin Karthikeyan, M. Saravana Psna College of Engineering and Technology Department of Computer Applications Dindigul India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (纸本)9798331523923

Accurate weather forecasting is critical for decision-making in agriculture, disaster management, urban planning, and numerous other domains. Traditional forecasting methods often struggle to capture the intricate spatial and temporal dependencies in weather data. This work proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to address these challenges. The CNN component efficiently extracts spatial features from satellite images, such as cloud formations and precipitation zones, while the LSTM network models the temporal dependencies inherent in meteorological data, such as temperature, humidity, and wind speed. The model is trained on a dataset comprising historical weather records and satellite imagery, spanning several years. PyTorch, a dynamic deep learning framework, is used to implement and train the hybrid architecture. Data preprocessing involves normalization, image augmentation, and sequence structuring to prepare inputs for the model. The CNN layers, built with convolutional and pooling operations, extract spatial features, which are combined with temporal weather data and passed to the LSTM network. The LSTM layers capture long-term dependencies and trends in weather patterns. Fully connected layers map the learned features to final predictions, including temperature, rainfall, and other key weather parameters. The hybrid model is evaluated using metrics such as Root Mean Square Error (RMSE) for numerical predictions and accuracy for classification tasks. Results indicate that the proposed hybrid CNN-LSTM model outperforms standalone models and traditional statistical approaches, demonstrating its ability to leverage spatial and temporal dependencies for accurate weather forecasting. This work highlights the potential of hybrid deep learning models in advancing weather forecasting capabilities, offering a scalable and reliable solution for real-world applications such as e

关键词： Convolutional neural networks

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Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

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Digital communications and Networks 2022年第5期8卷 843-852页

作者： Dinkisa A.Bulti Jyri Hämäläinen Beneyam B.Haile Yihenew Wondie School of Electrical and Computer Engineering Addis Ababa Institute of Technology(AAiT)Ethiopia Department of Communication and Networking Aalto UniversityFinland

Heterogeneous Networks(HetNets)and cell densification represent promising solutions for the surging data traffic demand in wireless *** dense HetNets,user traffic is steered toward the Low-Power Node(LPN)when possible to enhance the user throughput and system capacity by increasing the area spectral ***,because of the transmit power differences in different tiers of HetNets and irregular service demand,a load imbalance typically exists among different serving *** offload more traffic to LPNs and coordinate the Inter-Cell Interference(ICI),Third-Generation Partnership Project(3GPP)has facilitated the development of the Cell Range Expansion(CRE),enhanced Inter-Cell Interference Coordination(eICIC)and Further enhanced ICIC(FeICIC).In this paper,we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe(LPS)*** solution allows the separation of User Association(UA)functions at the User Equipment(UE)and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength(max-RSS)based UA scheme,where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the *** proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service *** show that our method effectively solves the offloading and interference coordination problems in dense HetNets.

关键词： Heterogeneous networks(HetNets) 5G LTE-AUser association Interference coordination Load balancing Cell range expansion Almost blank subframe Further enhanced ICIC Low-power subframe

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Sustainable Waste Management System Using Artificial Intelligence and Satellite communication: A Case Study 3

Sustainable Waste Management System Using Artificial Intelli...

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3rd International Conference on Advancement in Electrical and electronic engineering, ICAEEE 2024

作者： Shiraj, Tasnim Binte Nishat, Sadia Tasnim Chowdhury, Farzana Haque Easha, Umma Habiba Jahan, Afrida Israt Arif, Jayed Hossam-E-Haider, Md. Bangladesh University of Professionals Department of Information and Communication Technology Dhaka Bangladesh Military Institute of Science and Technology Department of Electrical Electronic and Communication Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

The Deep learning (DL) models of Artificial intelligence is becoming a necessary component of modern solid waste management (SWM) system design and planning against a rapidly advancing backdrop. To highlight the problems and obstacles associated with utilizing integrated technologies and satellite-based systems, this study provides a critical analysis of the existing SWM methods across South Asia. DL models and image Datasets can enhance the performance of the previous prototypes. There are three categories in which dataset description and preprocessing are categorized: Datasets in waste management, dataset preprocessing in waste management, and solid waste monitoring and management using RFID, GIS and GSM. The technologies discussed in the methodology section constitute the foundation for the satellite technology and AI-based SWM systems reviewed in this article. To make it easier to plan and create a sustainable new system, this review may help the reader understand the fundamentals of the pre-trained DL models also known as transfer learning that are now in the trend. Also image processing models DL and transfer learning models through satellite communication, and their application in SWM. © 2024 IEEE.

关键词： Satellites

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Optimized Mutual Authentication and AES Encryption Framework for Enhanced Security in Multi-Server Environments 2

Optimized Mutual Authentication and AES Encryption Framework...

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2nd International Conference on Computational Intelligence, communication Technology and Networking, CICTN 2025

作者： Kulkarni, Soham Sunil Kumar, Anant Gakhar, Suket Sharma, Vipin Kumar Arora, Sneha Department of Computer Science University of California Irvine Irvine United States Department of Computer Science and Engineering Manipal Institute of Technology Manipal India Department of Computer Science and Engineering Kurukshetra University Kurukshetra India Department of Electronics and Communication Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331530389

This paper introduces a secure authentication framework by applying Mutual Authentication Coupled with Optimised AES (Advanced Encryption Standard) Encryption to improve the security of the multi-server environment. By addressing inefficiencies in traditional mutual authentication protocols without encryption, this framework significantly improves both performance and resilience. Encryption time was reduced by 25% and authentication time by 16.67% while CPU (Central Processing Unit) usage was reduced by 23.08%, with a corresponding 21.43% improvement in memory efficiency. The proposed system demonstrates a 70% improvement in attack resistance. Dynamic revocation mechanisms mitigate unauthorized access and malicious activity in real time. The protocol is designed for resource-constrained environments to ensure secure, scalable, and efficient communication in multi-server architectures, further increasing system reliability and user trust in cloud-based services. © 2025 IEEE.

关键词： Authentication Protocol

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Time Series Models for Radiated Electromagnetic Energy of Lightning Strokes Detected by World Wide Lightning Location Network

Time Series Models for Radiated Electromagnetic Energy of Li...

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2022 International Conference on Recent Advances in Wireless communications and Emerging Technologies, RAWCET 2022

作者： Rafi, Mehdi Hasan Mostafa, Golam Department of Electrical Electronic and Communication Engineering Military Institute of Science and Technology Dhaka Bangladesh

Lightning is a significant atmospheric event and has a wide-ranging influence on the Earth's climate. Recently a sensor station of World Wide Lightning Location Network (WWLLN) is hosted at Dhaka (23.81°N, 90.41°E) and one year (Jan 2021 to Dec 2021) lightning strokes energy data is investigated. In an effort to switch the analysis from descriptive to predictive, WWLLN lightning strokes energy data is modeled using time series analysis. The autoregressive integrated moving average (ARIMA) model has been applied to predict the data. According to the process of model selection, the auto-correlation function (ACF) and partial auto-correlation function (PACF) are examined to determine the auto-regressive (AR) and moving-average (MA) terms. Standard diagnostic tests are performed to ensure the goodness of fit of the chosen model. After necessary diagnostic tests, ARIMA (3,1,0)×(2,1,0)12 is selected as the best fitted model. © Published under licence by IOP Publishing Ltd.

关键词： Time series analysis

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Advanced Semantic communication Techniques for IoT Using Disentangled Information Bottleneck

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IEEE Internet of Things Journal 2025年第12期12卷 20122-20137页

作者： Xie, Wenwu Xiong, Ming Yang, Liang Wang, Ji Li, Xingwang Yang, Zhihe School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China

This paper explores the impact of source data compression on the performance of task execution at the receiver side of a communication system, and investigates the interference and impact of channel environment variations on semantic coding features. In order to further optimize the performance of the semantic communication model, a semantic communication framework (DIB-DeepSC) based on Disentangled Information Bottleneck (DisenIB) is proposed, which improves the inference accuracy of the model by separating and decoupling irrelevant information in the source data, thus compressing valid information related to downstream task execution to a greater extent, reduced communication overhead. Meanwhile, the influence of the Lagrange multiplier β in the classical Information Bottleneck (IB) framework is eliminated, which avoids the need to manually optimize β several times in the semantic communication model. And the dynamic coding method (DIB-DE) is further designed based on adaptive weights, which can dynamically adjust the coding features according to the channel conditions and enhance the robustness of the model. Numerous experiments show that the proposed DIB-DeepSC framework combined with the DIB-DE dynamic encoding communication scheme possesses better semantic extraction and task inference performance relative to the benchmark methods. This scheme is expected to realize more efficient and reliable semantic transmission in practical communication systems and provides new ideas for developing practical semantic communication systems. © 2014 IEEE.

关键词： Channel coding

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