Processing Power Research Articles

Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

ABSTRACTBotnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively.

GraphSmart: a method for green and accurate IoT water monitoring

Water scarcity is nowadays a critical global concern and an efficient management of water resources is paramount. This paper presents an original approach for monitoring Water Distribution Systems (WDSs) through Internet of Things (IoT) that involves the integration of multiple sensors placed across the distribution network to accurately measure water flow. To enhance energy efficiency for green monitoring and communication process, we harness the power of graph theory and graph signal processing to represent in a tunable and accurate way the water flow and simultaneously minimize the number of IoT sensors communicating those measurements. We propose a graph model where water flow is represented as signal on graph and we introduce an algorithm, named GraphSmart, designed to reconstruct the graph signal when certain measurements are unknown or missing. Our framework is applied on synthetic realistic environment within the context of LoRaWAN (Long Range Wide Area Network), an infrastructure and protocol designed for ultra-low-power IoT devices. Our findings show that GraphSmart significantly reduces energy consumption while ensuring precise flow estimation. Our research demonstrates high potential for energy-efficient and accurate water flow monitoring, paving the way to improve the management of WDSs and enabling water operators to address water scarcity challenges.

Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm

Flint Hall Farm in Godstone, Surrey, UK, is situated adjacent to the London Orbital Motorway, or M25, and contains several landslide systems which pose a significant geohazard risk to this critical infrastructure. The site has been routinely monitored by geotechnical engineers following a landslide that encroached onto the hard shoulder in December 2000; current in situ instrumentation includes inclinometers and piezoelectric sensors. Interferometric Synthetic Aperture Radar (InSAR) is an active remote sensing technique that can quantify millimetric rates of Earth surface and structural deformation, typically utilising satellite data, and is ideal for monitoring landslide movements. We have developed the hardware and software for an Unmanned Aerial Vehicle (UAV), or drone radar system, for improved operational flexibility and spatial–temporal resolutions in the InSAR data. The hardware payload includes an industrial-grade DJI drone, a high-performance Ettus Software Defined Radar (SDR), and custom Copper Clad Laminate (CCL) radar horn antennas. The software utilises Frequency Modulated Continuous Wave (FMCW) radar at 5.4 GHz for raw data collection and a Range Migration Algorithm (RMA) for focusing the data into a Single Look Complex (SLC) Synthetic Aperture Radar (SAR) image. We present the first SAR image acquired using the drone radar system at Flint Hall Farm, which provides an improved spatial resolution compared to satellite SAR. Discrete targets on the landslide slope, such as corner reflectors and the in situ instrumentation, are visible as bright pixels, with their size and positioning as expected; the surrounding grass and vegetation appear as natural speckles. Drone SAR imaging is an emerging field of research, given the necessary and recent technological advancements in drones and SDR processing power; as such, this is a novel achievement, with few authors demonstrating similar systems. Ongoing and future work includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslides and other geotechnical hazards and infrastructure.

Security Enhancement Scheduling Model for IoT‐Based Smart Cities Through Machine Learning Method

ABSTRACTThe Internet of Things (IoT) is rapidly evolving, and this has supported the adoption of a new computing paradigm that moves processing power to the network's edge. The job must be assigned to the computer nodes, where their associated data is available, to minimize overheads generated by data transmissions in the network, because the edge nodes have limited processing power and are vulnerable to security. Hence, the paper introduces a novel security‐enhanced scheduling model for IoT‐based smart cities utilizing machine learning techniques. Initially, nodes are initialized using the LHK‐Means algorithm. Subsequently, tasks, representing requests from multiple users to access IoT data, are scheduled. Anomaly detection tasks are then identified using an L2‐Norm‐based fuzzy model. Normal tasks are processed by the BN‐CNN model, which schedules data collection tasks through the initialized IoT device nodes. Comparative analysis with existing models illustrates the effectiveness of the proposed approach in terms of accuracy, precision, recall, sensitivity, specificity, and f‐measure.

New Lightweight Cryptosystem for IoT in Smart City Environments

Internet of Things (IoT) devices, user interfaces (UI), software, as well as communication networks are all deployed within Smart Cities topology. The security approach designed for Internet of Things IoT should be able to prevent and detect both internal and external attacks. The problem in IoT network that not every linked node or device has an adequate amount of processing power. This means that data encryption and other related activities will be impossible and means that the security of any kind must be lightweight. A trustworthy security solution that stops illegal access to private data on the network is necessary for maintaining the privacy of information on the Internet of Things. Cryptographic processes need to be quicker and more compact without sacrificing security. The aim of this study is to reduce the execution time and power consumption of encryption processes without compromise the complexity of the encryption algorithm. This research presents a new lightweight cryptographic technique to protect various multimedia and real-time traffics across IoT network, by using two S-box in SubByte of encryption process, without affecting its performance. In this study, different audio samples will be used to test the new algorithm efficiency. Comparing the suggested method to the most advanced standard algorithm, it can reduce the cryptography process's execution time as well as energy consumption while maintaining the required security level. The outcomes demonstrate good performance in terms of power usage and delay. The new technique consumed a roughly 0.2 µJ for encryption process while the typical AES algorithm consumed 0.29 µJ, this mean the new algorithm achieved (33% power savings), while maintaining a good complexity level (security) within the process of encryption according to the results in tables I, II, and the comparison in table III. The novelty of this work can be showed by using dual XOR S-box technique which increased the complexity of SubByte process making it more secure without overload the processing performance, in addition to the reduction in encryption rounds which contribute to enhance the performance without compromise the security. Making it more suited for the Internet of Things (IoT) used in smart city environments.

Towards Ensemble Feature Selection for Lightweight Intrusion Detection in Resource-Constrained IoT Devices

The emergence of smart technologies and the wide adoption of the Internet of Things (IoT) have revolutionized various sectors, yet they have also introduced significant security challenges due to the extensive attack surface they present. In recent years, many efforts have been made to minimize the attack surface. However, most IoT devices are resource-constrained with limited processing power, memory storage, and energy sources. Such devices lack the sufficient means for running existing resource-hungry security solutions, which in turn makes it challenging to secure IoT networks from sophisticated attacks. Feature Selection (FS) approaches in Machine Learning enabled Intrusion Detection Systems (IDS) have gained considerable attention in recent years for having the potential to detect sophisticated cyber-attacks while adhering to the resource limitations issues in IoT networks. Apropos of that, several researchers proposed FS-enabled IDS for IoT networks with a focus on lightweight security solutions. This work presents a comprehensive study discussing FS-enabled lightweight IDS tailored for resource-constrained IoT devices, with a special focus on the emerging Ensemble Feature Selection (EFS) techniques, portraying a new direction for the research community to inspect. The research aims to pave the way for the effective design of futuristic FS/EFS-enabled lightweight IDS for IoT networks, addressing the critical need for robust security measures in the face of resource limitations.

Artificial Intelligence in Diabetes Mellitus Prediction: Advancements and Challenges - A Review

Abstract: Poor dietary habits and a lack of understanding are contributing to the rapid global increase in the number of diabetic people. Therefore, a framework that can accurately forecast a large number of patients based on clinical details is needed. Artificial intelligence (AI) is a rapidly evolving field, and its implementations to diabetes, a worldwide pandemic, have the potential to revolutionize the strategy of diagnosing and forecasting this chronic condition. Algorithms based on artificial intelligence fundamentals have been developed to support predictive models for the risk of developing diabetes or its complications. In this review, we will discuss AI-based diabetes prediction. Thus, AI-based new-onset diabetes prediction has not beaten the statistically based risk stratification models, in traditional risk stratification models. Despite this, it is anticipated that in the near future, a vast quantity of well-organized data and an abundance of processing power will optimize AI's predictive capabilities, greatly enhancing the accuracy of diabetic illness prediction models.

KnowBug: Enhancing Large language models with bug report knowledge for deep learning framework bug prediction

Understanding and predicting the bug type is crucial for developers striving to enhance testing efficiency and reduce software release problems. Bug reports, although semi-structured, contain valuable semantic information, making their comprehension critical for accurate bug prediction. Recent advances in large language models (LLMs), especially generative LLMs, have demonstrated their power in natural language processing. Many studies have utilized these models to understand various forms of textual data. However, the capability of LLMs to fully understand bug reports remains uncertain. To tackle this challenge, we propose KnowBug, a framework designed to augment LLMs with knowledge from bug reports to improve their ability to predict bug types. In this framework, we utilize bug reports from open-source deep learning frameworks, design specialized prompts, and fine-tune LLMs to assess KnowBug’s proficiency in understanding bug reports and predicting different bug types.

AI-assisted marking: Functionality and limitations of ChatGPT in written assessment evaluation

Generative artificial intelligence technologies, such as ChatGPT, bring an unprecedented change in education by leveraging the power of natural language processing and machine learning. Employing ChatGPT to assist with marking written assessment presents multiple advantages including scalability, improved consistency, eliminating biases associated with human subjectivity. This work aimed to evaluate the usefulness, reliability and accuracy of ChatGPT in marking written assessments of varied types and to identify its limitations and challenges. ChatGPT was instructed using a set of prompts to mark the assessment based on a rubric. ChatGPT was able to evaluate and assess both coding and reflective assessments and to distinguish between assignments of different quality, demonstrating high consistency and accuracy for higher quality assessments, comparable to a human marking. ChatGPT was also able to generate textual detailed justifications based on the rubric and assessment task description. There was a significant difference in the outcomes generated by different prompts. These preliminary findings suggest that utilising ChatGPT as a marking assistant can increase written assessment marking efficiency, reduce cost and potentially decrease the unfairness and bias by providing a moderating perspective. Implications for practice or policy: Assessment designers could reconsider the design, purpose and objectives of written assessments and leverage ChatGPT effectively for teaching and learning. Assessors might consider adapting the technology as a grading aid, to support a human-in-the-loop grading process, providing additional resources and time, moderating and refining individual feedback, to increase consistency and quality. Curriculum and programme leaders could develop guidelines around the ethical use of generative AI-assisted assessment practice, monitor and regulate the ongoing evaluation and refinement.

A Lightweight Image Encryption based on Improving LED using Rossler Attractor

Lightweight image encryption refers to the use of encryption algorithms designed to secure images while considering the limitations of resources, such as processing power and memory, typically found in devices like IoT devices, mobile phones, and embedded systems. This paper proposes a lightweight image encryption based on an LED encryption algorithm and a method for a pseudo number generator called Rossler attractor. Image is split into their three-color bands (RGB) and then partitioned into a block for encryption, a three-equations model (Rossler Attractor) is used for a pseudo number generator, a specific operation applied for producing key scheduled for rounds in LED to seem as random as possible. The experiment explains the objective test to ensure the performance. The contribution of this work is to present an efficient method for lightweight encryption that is applied to color images with a pseudo number generator-based Rossler attractor dependent on a three-dimensional equation. The technique makes it sensitive to initial conditions and could seem like dynamic encryption that resisted many types of attacks. Visually test represented by the histogram of the image before and after encryption is noted that the result is uniform and differs from the input one. The correlation of the output image is a low value which means no correlation is there between pixels horizontal, vertical and diagonal. The similarity test also applied such as MSE, PSNR and SSIM which also explain no similarity between plain and encrypted images. The randomness of generated numbers was tested using the standard test NIST for evaluation of the input key

FPGA Implementation of Compact Architecture for Lightweight Hash Algorithm for Resource Constrained Devices

The security implications of the emerging world of smart devices brought about by the Internet of things' recent rise are enormous. IoT devices typically have restricted resources, such as minimal memory, low processing power, and a short battery life, in addition to their stated security level. In the context of IoT, lightweight cryptographic primitives are presented taking into account the trade-off between speed and security assurance. In this research, we proposed lightweight cryptographic hash methods and optimized hardware for devices with limitations. The size, speed, efficiency, and power consumption of our suggested solutions are examined on FPGA systems. Bit permutation, linear transformation, and S-Box functionality are used in the suggested design. To analyze large data sets and Internet of Things applications, conventional hash algorithms need memory and time. Thus, the demand arises for a lightweight cryptographic protocol that is both rapid and safe. This technique offers the necessary security criteria of conventional hash functions and the parameters for lightweight cryptographic protocols. The proposed architecture is tested and validated based on three key criteria: memory, speed, and power consumption, which are important factors in determining its lightweight nature. Based on the findings, the proposed architecture outperforms other lightweight protocols regarding memory usage, performance, and power consumption. For an appropriate FPGA-based application deployment, a thorough comparison of our suggested designs is detailed on various FPGA families.

Optimization of laser cladding powder ratio and process parameters based on MOGWO algorithm

Optimizing the process parameters is crucial to obtaining a high-quality fusion coating. The fitted regression model for each response was then analyzed by creating a central composite test with process parameters and power ratios (laser power, scanning speed, and Ni-clad graphite content) as inputs and the quality of the fused coating (dilution rate, microhardness, and width of the fused coating) as responses. The laser cladding process parameters and power ratios were optimized using the multi-objective Gray Wolf Optimization (MOGWO) algorithm. The best combinations of process parameters and power ratios were laser power of 1596 W, scanning speed of 21.7 mm/s, and Ni-clad graphite percentage of 18.4 %. Experimental validation demonstrates the superiority and accuracy of the MOGWO algorithm. The dilution rate of the optimized fusion-clad coating is increased by 47.4 % and the microhardness is increased by 17.8 %.

Working in a relational way is everything: Perceptions of power and value in a drug policy-making network

BackgroundThe development of drug policies has been a major focus for policy-makers across North America in light of the ongoing public health emergency caused by the overdose crisis. In this context, the current study examined stakeholders’ experiences and perceptions of power and value in a drug policy-making process in a North American city using qualitative, questionnaire, and social network data.MethodsWe interviewed 18 people who participated in the development of a drug policy proposal between October 2021 and March 2022. They represented different groups and organizations, including government (n = 3), people who use drugs-led advocacy organizations (n = 5), other drug policy advocacy organizations (n = 5), research (n = 3) and police (n = 2). Most of them identified as men (n = 8) and white (n = 16), and their ages ranged between 30 and 80 years old (median = 50). Social network analysis questionnaires and semi-structured qualitative interviews were administered via Zoom. Social network data were analysed using igraph in R, and qualitative data were analysed using thematic analysis. The analyses explored perceptions of value and power within a drug policy-making network.ResultsThe policy-making network showed that connections could be found across participants from different groups, with government officials being the most central. Qualitative data showed that inclusion in the network and centrality did not necessarily translate into feeling powerful or valued. Many participants were dissatisfied with the process despite having structurally advantageous positions or self-reporting moderately high quantitative value scores. Participants who viewed themselves as more valued acknowledged many process shortcomings, but they also saw it as more balanced or fair than those who felt undervalued.ConclusionsWhile participation can make stakeholders and communities feel valued and empowered, our findings highlight that inclusion, position and diversity of connections in a drug policy-making network do not, in and of itself, guarantee these outcomes. Instead, policy-makers must provide transparent terms of reference guidelines and include highly skilled facilitators in policy discussions. This is particularly important in policy processes that involve historical power imbalances in the context of a pressing public health emergency.

AI-Enhanced Edge Device for Real-Time Snoring Detection

This paper presents the development of a cutting-edge, non-invasive edge device designed to monitor snoring and provide timely, moderate haptic feedback to users. Utilizing the Qualcomm Snapdragon 8cx Gen 3 processor, the device offers robust computing power and AI capabilities for real-time processing, making it a versatile tool for health monitoring applications. The system integrates a high-fidelity MEMS microphone array capable of capturing nuanced audio signals and a TDK piezoelectric haptic actuator, which delivers precise alerts through customized vibrations. The research explores the potential of this advanced hardware in detecting and managing obstructive sleep apnea (OSA), a condition often underdiagnosed due to a lack of patient awareness. By leveraging state-of-the-art digital signal processing and deep learning techniques, the device aims to enhance user awareness and intervention in sleep-related disorders, offering a promising new avenue for improving patient outcomes and quality of life.

A novel Real-time Control System for next generation gravitational-wave detectors

This contribution presents the INFN Pisa group research and development efforts aimed at defining and implementing a novel Real-time Control System (RCS) for next-generation interferometers, focusing on the initial phase of the Einstein Telescope (ET) gravitational wave detector. The RCS handles complex closed-loop systems, managing data collection from sensing elements, processing and actuator control within well defined time constraints. It integrates multiple spatially distributed control units, facilitating communications among them, with external systems as well as with users. Two distinct hardware approaches are explored: “Katane” and “Zancle”. These systems primarily differ in their core processing elements. The “Katane” system is built on powerful DSP processor boards, based on the Super-Attenuator control system, developed at INFN Pisa for Advanced Virgo detector. It uses the MicroTCA.4 architecture standard, ensuring flexibility, easy maintenance, and fast data exchange among the various custom modules, including several types of Front-End data converter boards, FPGA-based pre-processing boards, DSPs and standard processors. On the other hand, ”Zancle” explores GPU-based processing, aiming to use cost-effective consumer market systems and to leverage substantial computational power of these devices, also incorporating machine learning algorithms.

Explanations of and interventions against affective polarization cannot afford to ignore the power of ingroup norm perception.

Affective polarization, or animosity toward opposing political groups, is a fundamentally intergroup phenomenon. Yet, prevailing explanations of it and interventions against it have overlooked the power of ingroup norm perception. To illustrate this power, we begin with evidence from 3 studies which reveal that partisans' perception of their ingroup's norm of negative attitudes toward the outgroup is exaggerated and uniquely predicts their own polarization-related attitudes. Specifically, our original data show that in predicting affective polarization (i.e. how one feels about one's partisan outgroup), the variance explained by ingroup norm perception is 8.4 times the variance explained by outgroup meta-perception. Our reanalysis of existing data shows that in predicting support for partisan violence (i.e. how strongly one endorses and is willing to engage in partisan violence), ingroup norm perception explains 52% of the variance, whereas outgroup meta-perception explains 0%. Our pilot experiment shows that correcting ingroup norm perception can reduce affective polarization. We elucidate the theoretical underpinnings of the unique psychological power of ingroup norm perception and related ingroup processes. Building on these empirical and theoretical analyses, we propose approaches to designing and evaluating interventions that leverage ingroup norm perception to curb affective polarization. We specify critical boundary conditions that deserve prioritized attention in future intervention research. In sum, scientists and practitioners cannot afford to ignore the power of ingroup norm perception in explaining and curbing affective polarization.

Prediction of cavitation using machine learning techniques on centrifugal pump

Abstract When the local static pressure drops below the fluid’s vapour pressure at that particular temperature, a dynamic process known as cavitation takes place in the liquid. Due to cavitation, the performance (total head and flow rate) of the pump will deteriorate, and hence the vibration and noise will develop. Both the performance and cavitation investigations are carried out for a low-specific-speed pump as per the testing standards. The results obtained during cavitation studies are extracted to train the machine learning algorithms. The flow rate, speed, torque, suction head, and delivery head are considered as input variables for machine learning, with total head, Net Positive Suction Head (NPSH), efficiency, and noise magnitude being output variables. The current studies employ four machine learning techniques: random forest, support vector machines, extreme gradient boosting, and linear regression. In the analysis, notable performance was observed for the random forest and extreme gradient boosting algorithms among others consistently demonstrated superior predictive capabilities for the output parameters of the pumps. For linear regression, random forest, and extreme gradient boosting, the coefficient correlation values are around one for all output parameters except for noise. Similar observation is depicted for coefficient of determination and normalized root mean squared error. These ensure the level of noise during cavitation is very dynamic. These findings will enable the pump user to incorporate AI techniques for the diagnosis of centrifugal pump operation with reference to the nominal flow rate (best efficiency flow). Through this technology, preventive and predictive maintenance of pumps can be adapted with less downtime in process industries, power plants, chemical industries, oil and gas refineries, and so on.

SDERP: Stable‐aware differential evolution‐based routing protocol for mobile wireless networks

SummaryA mobile ad hoc network (MANET) is a network with non‐wired communications and non‐centralized administration where all its nodes are mobile. Firstly, one of the leading drawbacks of wireless networks is their energy consumption because the constituent nodes of the network have a restricted battery capacity yet greater consumption caused by the mobility of the nodes, their processing power, the frequent sending of data, and so on. Secondly, data packets may be lost due to traffic, noise, or the mobility of nodes, whose data transmission in real‐time applications would be compromised by this loss. In order to resolve the problem of energy consumption and that of the loss of data packets for MANETs, we propose a new stable‐aware differential evolution‐based routing protocol (SDERP). We include a fitness function to select the best new path from all new paths issued at the end of this algorithm. This fitness function is based on energy constraints and link stability. The performance of our proposal is compared with OPAOMDV‐EE, FT‐AORP, EE‐LB‐AOMDV and AOMDV. The results show that our SDERP has better network performance than the other routing protocols. Specifically, it obtains a packet delivery ratio that is higher by up to 17%, an overhead ratio reduced by up to 33%, an energy efficiency better by up to 39%, and end‐to‐end delay reduced by up to 16%.

Implementation of resource-efficient fetal echocardiography detection algorithms in edge computing.

Recent breakthroughs in medical AI have proven the effectiveness of deep learning in fetal echocardiography. However, the limited processing power of edge devices hinders real-time clinical application. We aim to pioneer the future of intelligent echocardiography equipment by enabling real-time recognition and tracking in fetal echocardiography, ultimately assisting medical professionals in their practice. Our study presents the YOLOv5s_emn (Extremely Mini Network) Series, a collection of resource-efficient algorithms for fetal echocardiography detection. Built on the YOLOv5s architecture, these models, through backbone substitution, pruning, and inference optimization, while maintaining high accuracy, the models achieve a significant reduction in size and number of parameters, amounting to only 5%-19% of YOLOv5s. Tested on the NVIDIA Jetson Nano, the YOLOv5s_emn Series demonstrated superior inference speed, being 52.8-125.0 milliseconds per frame(ms/f) faster than YOLOv5s, showcasing their potential for efficient real-time detection in embedded systems.

Evolutionary shift of a tipping point can precipitate, or forestall, collapse in a microbial community.

Global ecosystems are rapidly approaching tipping points, where minute shifts can lead to drastic ecological changes. Theory predicts that evolution can shape a system's tipping point behaviour, but direct experimental support is lacking. Here we investigate the power of evolutionary processes to alter these critical thresholds and protect an ecological community from collapse. To do this, we propagate a two-species microbial system composed of Escherichia coli and baker's yeast, Saccharomyces cerevisiae, for over 4,000 generations, and map ecological stability before and after coevolution. Our results reveal that tipping points-and other geometric properties of ecological communities-can evolve to alter the range of conditions under which our microbial community can flourish. We develop a mathematical model to illustrate how evolutionary changes in parameters such as growth rate, carrying capacity and resistance to environmental change affect ecological resilience. Our study shows that adaptation of key species can shift an ecological community's tipping point, potentially promoting ecological stability or accelerating collapse.