Harnessing Machine Learning Intelligence Against Cyber Threats

Advances in business strategy and competitive advantage book series, Год журнала: 2024, Номер unknown, С. 319 - 352

Опубликована: Авг. 28, 2024

The spread of cyberthreats in the digital age presents serious concerns to national security, stability economy, and personal privacy. Traditional security methods are unable keep up with increasing sophistication size cyberattacks. With facilitating quick identification mitigation cyberthreats, machine learning (ML) has revolutionary potential improve cybersecurity measures. But applying ML this field also brings important moral legal issues, particularly light international cybercrimes. This chapter comprehensively explores learning's dual nature cybersecurity, emphasizing both its advantages disadvantages. It talk about state cyber threats today, how is being incorporated into ramifications using investigations.

Язык: Английский

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Weakly-supervised thyroid ultrasound segmentation: Leveraging multi-scale consistency, contextual features, and bounding box supervision for accurate target delineation

Computers in Biology and Medicine, Год журнала: 2025, Номер 186, С. 109669 - 109669

Опубликована: Янв. 13, 2025

Язык: Английский

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Enhancing Clustering Efficiency in Heterogeneous Wireless Sensor Network Protocols Using the K-Nearest Neighbours Algorithm

Sensors, Год журнала: 2025, Номер 25(4), С. 1029 - 1029

Опубликована: Фев. 9, 2025

Wireless Sensor Networks are formed by tiny, self-contained, battery-powered computers with radio links that can sense their surroundings for events of interest and store process the sensed data. nodes wirelessly communicate each other to relay information a central base station. Energy consumption is most critical parameter in (WSNs). Network lifespan directly influenced energy sensor nodes. All sensors network send receive data from station (BS) using different routing protocols algorithms. These use two main types clustering: hierarchical clustering flat clustering. Consequently, effective within (WSN) essential establishing secure connections among nodes, ensuring stable lifetime. This paper introduces novel approach improve efficiency, reduce length connections, increase lifetime heterogeneous employing K-Nearest Neighbours (KNN) algorithm optimise node selection mechanisms four protocols: Low-Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Threshold-sensitive Efficient (TEEN), Distributed Energy-efficient (DEC). Simulation results obtained MATLAB (R2024b) demonstrate efficacy proposed algorithm, revealing modified achieve shorter distances between cluster heads reduced consumption, improved compared original protocols. The KNN-based enhances network’s operational efficiency security, offering robust solution management WSNs.

Язык: Английский

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Ensemble-Based Machine Learning Techniques for Adaptive Wireless Sensor Networks

Advances in computer and electrical engineering book series, Год журнала: 2025, Номер unknown, С. 319 - 360

Опубликована: Фев. 7, 2025

Wireless sensor networks (WSN) have gained popularity in next-generation IoT connectivity due to their sustainability and low maintenance. However, the dynamic nature of energy sources environmental conditions presents challenges security reliability WSNs, particularly mitigating various network attacks. Machine learning offers solutions these by enabling adaptive real-time behaviour. This chapter addresses WSN applying ML techniques a multi-class dataset attacks such as normal, flooding, TDMA, grayhole, blackhole. SMOTE is applied manage class imbalance, an ensemble framework proposed with classifiers logistic regression, random forest, gradient boost, xtreme decision tree, LGBM, SVM, CatBoost were predict WSN-DS dataset. The models are rigorously tested evaluated using accuracy, precision, recall, F1-score. Gradient catboost outperform all other achieving 98% accuracy.

Язык: Английский

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Unveiling the Invisible: Powering Security Threat Detection in WSN With AI

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(9-11)

Опубликована: Апрель 11, 2025

ABSTRACT Security in wireless sensor networks (WSNs) is of paramount importance due to their pervasive deployment critical infrastructure and sensitive environments. Despite ubiquitous nature, WSNs are vulnerable various security threats, ranging from unauthorized access data manipulation network disruption. In response these challenges, this paper proposes a novel approach leveraging the Base Stacked Long Short‐Term Memory with Attention Models AdaBoost Ensemble (BSLAM‐AE) architecture enhance WSNs. The proposed model designed address unique characteristics challenges WSNs, combining deep learning ensemble techniques detect mitigate threats effectively. BSLAM‐AE incorporates stacked LSTM attention mechanisms, enabling analysis time‐series detection subtle anomalies or breaches. addition, an ensemble‐learning component iteratively trains set models improve predictive accuracy robustness. Implemented PyCharm integrated development environment, experimental results demonstrate efficacy model, achieving impressive 98% detecting Overall, represents significant advancement WSN security, offering comprehensive efficient solution for mitigating threats. By techniques, provides enhanced reliability, thereby safeguarding against potential attacks ensuring integrity availability infrastructure.

Язык: Английский

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‘As of my last knowledge update’: How is content generated by ChatGPT infiltrating scientific papers published in premier journals?

Learned Publishing, Год журнала: 2024, Номер 38(1)

Опубликована: Дек. 24, 2024

Abstract The aim of this paper is to highlight the situation whereby content generated by large language model ChatGPT appearing in peer‐reviewed papers journals recognized publishers. demonstrates how identify sections that indicate a text fragment was generated, is, entirely created, ChatGPT. To prepare an illustrative compilation appear indexed Web Science and Scopus databases possessing Impact Factor CiteScore indicators, SPAR4SLR method used, which mainly applied systematic literature reviews. Three main findings are presented: highly regarded premier journals, articles bear hallmarks AI models, whose use not declared authors (1); many these identified already receiving citations from other scientific works, also placed found (2); and, most belong disciplines medicine computer science, but there such as environmental engineering, sociology, education, economics management (3). This aims continue add recently initiated discussion on models like creation scholarly works.

Язык: Английский

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Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers

Mathematics, Год журнала: 2024, Номер 12(11), С. 1720 - 1720

Опубликована: Май 31, 2024

In the modern world, evolution of internet supports automation several tasks, such as communication, education, sports, etc. Conversely, it is prone to types attacks that disturb data transfer in network. Efficient attack detection needed avoid consequences an attack. Traditionally, manual limited by human error, less efficiency, and a time-consuming mechanism. To address problem, large number existing methods focus on techniques for better efficacy detection. However, improvement significant factors accuracy, handling larger data, over-fitting versus fitting, tackle this issue, proposed system utilized Random Grove Blend Weighted MLP (Multi-Layer Perceptron) Layers classify network attacks. The used its advantages solving complex non-linear problems, datasets, high accuracy. computation requirements great deal labeled training data. resolve random info grove blend weight weave layer are incorporated into attain this, UNSW–NB15 dataset, which comprises nine attack, detect Moreover, Scapy tool (2.4.3) generate real-time dataset classifying efficiency presented mechanism calculated with performance metrics. Furthermore, internal external comparisons processed respective research reveal system’s efficiency. model utilizing attained accuracy 98%. Correspondingly, intended contribute associated enhancing security.

Язык: Английский

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Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Июнь 5, 2024

Abstract This paper presents a groundbreaking online educational platform that utilizes facial expression recognition technology to track the progress of students within classroom environment. Through periodic image capture and data extraction, employs ResNet50, CBAM, TCNs for enhanced recognition. Achieving accuracies 91.86%, 91.71%, 95.85%, 97.08% on RAF-DB, FER2013, CK + , KDEF datasets, respectively, proposed model surpasses initial ResNet50 in accuracy detection students' learning states. Comparative evaluations against state-of-the-art models using datasets underscore significance results institutions. By enhancing emotion accuracy, improving feature relevance, capturing temporal dynamics, enabling real-time monitoring, ensuring robustness adaptability environments, this approach offers valuable insights educators enhance teaching strategies student outcomes. The combined capabilities contribute uniquely dynamic changes expressions over time, thereby facilitating accurate interpretation emotions engagement levels more effective monitoring behaviors real-time.

Язык: Английский

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A Novel Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model

Deleted Journal, Год журнала: 2024, Номер 4(2), С. 120 - 128

Опубликована: Авг. 28, 2024

Cybersecurity refers to the actions that are used by people and companies protect themselves their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks still a major concern in computer community. Many gaps, like Denial of Service, spam, phishing, other types attacks, reported daily, attack numbers growing. Intrusion detection is protection method detect report any traffic automatically may affect security, such as internal external maloperations. This paper an anomaly intrusion system based on new RNA encoding ResNet50 Model, where done splitting training records into different groups. These groups protocol, service, flag, digit, each group represented number characters can represent group's values. The phase converts sequences, allowing comprehensive representation dataset. model, utilizing ResNet architecture, effectively tackles challenges achieves high rates types. KDD-Cup99 Dataset both testing. testing dataset includes do not appear dataset, which means future. efficiency suggested calculating rate (DR), false alarm (FAR), accuracy. achieved DR, FAR, accuracy equal 96.24%, 6.133%, 95.99%. experimental results exhibit improve detection.

Язык: Английский

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Text Mining Algorithm Naive Bayes Classifier to Improve Quality Sentiment Analysis Passport Mobile Application

Опубликована: Март 4, 2024

Mobile Passport is an application that can be used as a digital service for people in Indonesia to apply new passport and official online replacement from the Directorate General of Immigration replacing APAPO (Online Service Application). User reviews are output big data generated result Internet Things. The problem formulation this research how implementation Naive Bayes text mining classifier algorithm analyze contained well accuracy, precision recall values. This uses KDD (Knowledge Discovery database) method which consists selection, preprocessing, transformation, mining, evaluation using R Studio tool. resulting knowledge information process useful base decision making. because its reliability handling quickly accurate predictions based on class probabilities, thus enabling obtain consistent reliable results.

Язык: Английский

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