A novel Bayesian optimizable ensemble bagged trees model for cryptocurrency fraud prediction approach DOI
Monire Norouzi

Security and Privacy, Journal Year: 2024, Volume and Issue: 7(6)

Published: May 19, 2024

Abstract Nowadays, the prediction of cryptocurrency side effects on critical aspects exchange rates in intelligent business is one main challenges financial market. Cryptocurrency defined as a set digital information concerning internal protocols marketing, such blockchain, which operates according to decentralized architecture. On other hand, fraud activities Ethereum transfer and management now increase affect safe transactional processes. This article presents new machine‐learning approach Detection based Bayesian Optimizable Ensemble Bagged Trees (BOEBT) algorithm. Moreover, goal this study derive accuracy model using different algorithms compare their evaluation parameters together. The performance proposed machine learning was evaluated by MATLAB tool. experimental results show that BOEBT algorithm merits achieving 99.21% 99.14% F1‐Score for prediction.

Language: Английский

Recent Advances and Challenges in Industrial Robotics: A Systematic Review of Technological Trends and Emerging Applications DOI Open Access
Claudio Urrea, John Kern

Processes, Journal Year: 2025, Volume and Issue: 13(3), P. 832 - 832

Published: March 12, 2025

Industrial robotics has shifted from rigid, task-specific tools to adaptive, intelligent systems powered by artificial intelligence (AI), machine learning (ML), and sensor integration, revolutionizing efficiency human–robot collaboration across manufacturing, healthcare, logistics, agriculture. Collaborative robots (cobots) slash assembly times 30% boost quality 15%, while reinforcement enhances autonomy, cutting errors energy use 20%. Yet, this review transcends descriptive summaries, critically synthesizing these trends expose unresolved tensions in scalability, cost, societal impact. High implementation costs legacy system incompatibilities hinder adoption, particularly for SMEs, interoperability gaps—despite frameworks, like OPC UA—stifle multi-vendor ecosystems. Ethical challenges, including workforce displacement cybersecurity risks, further complicate progress, underscoring a fragmented field where innovation outpaces practical integration. Drawing on systematic of high-impact literature, study uniquely bridges technological advancements with interdisciplinary applications, revealing disparities economic feasibility equitable access. It critiques the literature’s isolation trends—cobots’ safety, ML’s perception’s precision—proposing following cohesive research directions: cost-effective modularity, standardized protocols, ethical frameworks. By prioritizing interoperability, sustainability, paper charts path evolve inclusively, offering actionable insights researchers, practitioners, policymakers navigating dynamic landscape.

Language: Английский

Citations

1
A comprehensive systematic review of intrusiondetection systems: emerging techniques,challenges, and future research directions DOI Creative Commons
Arjun Kumar Bose Arnob, Rajarshi Roy Chowdhury,

Nusrat Alam Chaiti

et al.

Journal of Edge Computing, Journal Year: 2025, Volume and Issue: unknown

Published: April 15, 2025

The role of Intrusion Detection Systems (IDS) in the protection against increasing variety cybersecurity threats complex environments, including Internet Things (IoT), cloud computing, and industrial networks. This study evaluates existing state-of-the-art IDS methodologies using Deep Learning (DL) approaches, advanced feature engineering techniques. research also highlights success models such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Explainable AI (XAI) improving detection accuracy well computational efficiency interoperability. Blockchain quantum computing technologies are explored to improve data privacy, resilience, scalability decentralized resource-constrained environments. work primarily identifies key challenges, real-time anomaly detection, adversarial robustness, imbalance datasets, assist researchers investigating further opportunities. Focusing on future filling these gaps, proceeds toward developing lightweight, adaptive, ethical frameworks that can operate across dynamic heterogeneous In this paper, opportunities, strategies critically synthesized create a useful resource for academics, researchers, industry practitioners.

Language: Английский

Citations

1
BRL-ETDM: Bayesian reinforcement learning-based explainable threat detection model for industry 5.0 network DOI
Arun Kumar Dey, Govind P. Gupta, Satya Prakash Sahu

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(6), P. 8243 - 8268

Published: April 9, 2024

Language: Английский

Citations

4
Hybrid feature extraction and integrated deep learning for cloud-based malware detection DOI
Pham Sy Nguyen, Tran Nhat Huy, Tong Anh Tuan

et al.

Computers & Security, Journal Year: 2024, Volume and Issue: unknown, P. 104233 - 104233

Published: Dec. 1, 2024

Language: Английский

Citations

4
A systematic literature review for load balancing and task scheduling techniques in cloud computing DOI Creative Commons

Nisha Devi,

Sandeep Dalal, Kamna Solanki

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(10)

Published: Sept. 5, 2024

Language: Английский

Citations

4
Comparative Analysis of Advanced Machine Learning Models for Exploit Detection in Intrusion Detection Systems DOI

Aadil Khan,

Deepali Gupta, Sheifali Gupta

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Abstract The integrity of network infrastructure against malicious exploit attacks relies mostly on Intrusion Detection Systems (IDS). These techniques are very essential for identifying and lowering threats before they start to cause significant damage. This manuscript evaluates three advanced Machine Learning (ML) models CatBoost, XGBoost, Long Short-Term Memory (LSTM) a real-world traffic dataset determine their suitability IDS applications. Every model is evaluated using key metrics: accuracy, precision, recall, F1-score, error measures including Root Mean Squared Error (RMSE) (MSE). Based the results, Catboost exceeds other with 98.55% accuracy lowest rates. Given CatBoost's remarkable performance, it fitting real-time systems where reducing false positives negatives extremely crucial. XGBoost provides balanced computationally affordable solution even if significantly less accurate; ideal scenarios requiring fast responses limited resources. Strong in sequential pattern recognition, LSTM has higher rate positives, suggesting that further tuning needed improve its overall reliability surroundings. possibility enhancing performance gradient boosting such as CatBoost cybersecurity underlined this study.

Language: Английский

Citations

0
A comparative assessment of machine learning algorithms in the IoT-based network intrusion detection systems DOI Creative Commons
Milan Samantaray, Ram Chandra Barik, Anil Kumar Biswal

et al.

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 11, P. 100478 - 100478

Published: May 15, 2024

The rapid increase in online risks is a reflection of the exponential growth Internet Things (IoT) networks. Researchers have proposed numerous intrusion detection techniques to mitigate harm caused by these threats. Enterprises use systems (IDSs) and prevention (IPSs) keep their networks safe, stable, accessible. Network solutions lately integrated powerful Machine Learning (ML) safeguard IoT Selecting proper data features for effectively training such ML models critical maximizing accuracy computational efficiency. However, efficiency degrades high-dimensional spaces, it crucial suitable feature extraction method eliminate extraneous from classification procedure. false positive rate many ML-based IDSs also rise when samples used train are unbalanced. This study provides detailed overview UNSW-NB15(DS-1) NF-UNSWNB15(DS-2) datasets detection, which will be utilized develop evaluate our models. In addition, this model uses MaxAbsScaler algorithm implement filter-based scaling strategy . Then, condensed set perform several techniques, including Support Vector Machines (SVM), K-nearest neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), considering multiclass classification. Accuracy tests scheme were improved 60% 94% using MaxAbsScaler-based method.

Language: Английский

Citations

3
Hybrid deep learning models with spotted hyena optimization for cloud computing enabled intrusion detection system DOI
Fatimah Alhayan, Muhammad Kashif Saeed, Randa Allafi

et al.

Journal of Radiation Research and Applied Sciences, Journal Year: 2025, Volume and Issue: 18(2), P. 101523 - 101523

Published: April 15, 2025

Language: Английский

Citations

0
Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT DOI Creative Commons
Mahmoud Ragab, Mohammed Basheri,

Nasser Albogami

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 110, P. 438 - 450

Published: Oct. 14, 2024

Language: Английский

Citations

2
A multi-node attack scheme based on community partitioning in large scale infrastructure networks DOI
Beibei Li, Wei Hu

Computer Networks, Journal Year: 2024, Volume and Issue: 245, P. 110386 - 110386

Published: April 4, 2024

Language: Английский

Citations

1