Ensuring network security with a robust intrusion detection system using ensemble-based machine learning

Array, Journal Year: 2023, Volume and Issue: 19, P. 100306 - 100306

Published: July 1, 2023

Intrusion detection is a critical aspect of network security to protect computer systems from unauthorized access and attacks. The capacity traditional intrusion (IDS) identify unknown sophisticated threats constrained by their reliance on signature-based detection. Approaches based machine learning have shown promising results in identifying malicious No algorithm-based model, however, able accurately consistently detect all different kinds Besides that, the existing models are tested for specific dataset. In this research, novel ensemble-based machine-learning technique presented. Numerous public datasets multiple ensemble strategies, including Random Forest, Gradient Boosting, Adaboost, XGBoost, Bagging, Simple Stacking, will be employed evaluate performance proposed approach. most relevant features selected using correlation analysis, mutual information, principal component analysis. Our research methods demonstrates that approach Forest outperforms approaches terms accuracy FPR, typically exceeding 99% with better evaluation metrics like Precision, Recall, F1-score, Balanced Accuracy, Cohen's Kappa, etc. This strategy may useful tool strengthening safety networks against emerging cyber threats.

Language: Английский

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Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

Energies, Journal Year: 2023, Volume and Issue: 16(10), P. 4025 - 4025

Published: May 11, 2023

The use of machine learning and data-driven methods for predictive analysis power systems offers the potential to accurately predict manage behavior these by utilizing large volumes data generated from various sources. These have gained significant attention in recent years due their ability handle amounts make accurate predictions. importance particular momentum with transformation that traditional system underwent as they are morphing into smart grids future. transition towards embed high-renewables electricity is challenging, generation renewable sources intermittent fluctuates weather conditions. This facilitated Internet Energy (IoE) refers integration advanced digital technologies such Things (IoT), blockchain, artificial intelligence (AI) systems. It has been further enhanced digitalization caused COVID-19 pandemic also affected energy sector. Our review paper explores prospects challenges using provides an overview ways which constructing can be applied order them more efficient. begins description role operations. Next, discusses systems, including benefits limitations. In addition, reviews existing literature on this topic highlights used Furthermore, it identifies opportunities associated methods, quality availability, discussed. Finally, concludes a discussion recommendations research application future grid-driven powered IoE.

Language: Английский

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Role of activity-based learning and ChatGPT on students' performance in education

Computers and Education Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6, P. 100219 - 100219

Published: April 3, 2024

This study investigates the impact of activity-based learning and utilization ChatGPT on students' academic performance within educational framework. The aims to assess effectiveness in comparison traditional methods, while also evaluating potential benefits drawbacks integrating as an tool. employs a comparative approach, analyzing outcomes students exposed versus those using conventional methods. Additionally, examines usage education through surveys trials determine its contribution personalized feedback, interactive learning, innovative teaching findings reveal that enhances engagement, motivation, critical thinking skills. Students participating demonstrate improved achievement, which is attributed their active involvement practical application knowledge. Similarly, integration offers novel avenues for individualized assistance, fostering understanding exploration complex concepts. In conclusion, proves be student-centered approach by participation engagement. showcases enhance experiences conversations methodologies, despite considerations regarding limitations ethical implications.

Language: Английский

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Current trends in AI and ML for cybersecurity: A state-of-the-art survey

Cogent Engineering, Journal Year: 2023, Volume and Issue: 10(2)

Published: Oct. 25, 2023

Language: Английский

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Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 12699 - 12719

Published: Jan. 1, 2024

Energy efficiency and safety are two essential factors that play a significant role in operating wireless sensor network. However, it is claimed these naturally conflicting. The level of electrical consumption required by security system directly proportional to its degree complexity. Wireless networks require additional measures above the capabilities conventional network protocols, such as encryption key management. potential application machine learning techniques address concerns frequently discussed. These devices will have complete artificial intelligence capabilities, enabling them understand their environment respond. During training phase, machine-learning systems may face challenges due large amount data complex nature procedure. This article focuses on algorithms used solve issues networks. also different types attacks layers Moreover, this study addresses several unsolved issues, including adapting accommodate sensors' functionalities configuration. Furthermore, open field must be solved.

Language: Английский

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A review on machine learning techniques for secured cyber-physical systems in smart grid networks

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 1268 - 1290

Published: Jan. 9, 2024

The smart grid (SG) is an advanced cyber-physical system (CPS) that integrates power infrastructure with information and communication technologies (ICT). This integration enables real-time monitoring, control, optimization of electricity demand supply. However, the increasing reliance on ICT infrastructures has made SG-CPS more vulnerable to cyberattacks. Hence, securing from these threats crucial for its reliable operation. In recent literature, machine learning (ML) techniques and, recently, deep (DL) have been used by several studies implement cybersecurity countermeasures against cyberattacks in SG-CPS. Nevertheless, achieving high performance state-of-the-art constrained certain challenges, including hyperparameter optimization, feature extraction selection, lack models' transparency, data privacy, attack data. paper reviews advancement using ML DL It analyzes constraints need be addressed improve achieve implementation. various types cyberattacks, requirements, security standards protocols are also discussed establish a comprehensive understanding context will serve as guide new experienced researchers.

Language: Английский

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Deep learning for intelligent demand response and smart grids: A comprehensive survey

Computer Science Review, Journal Year: 2024, Volume and Issue: 51, P. 100617 - 100617

Published: Feb. 1, 2024

Electricity is one of the mandatory commodities for mankind today. To address challenges and issues in transmission electricity through traditional grid, concepts smart grids demand response have been developed. In such systems, a large amount data generated daily from various sources as power generation (e.g., wind turbines), distribution (microgrids fault detectors), load management (smart meters electric appliances). Thanks to recent advancements big computing technologies, Deep Learning (DL) can be leveraged learn patterns predict peak hours. Motivated by advantages deep learning grids, this paper sets provide comprehensive survey on application DL intelligent response. Firstly, we present fundamental DL, response, motivation behind use DL. Secondly, review state-of-the-art applications including forecasting, state estimation, energy theft detection, sharing trading. Furthermore, illustrate practicality via cases projects. Finally, highlight presented existing research works important potential directions

Language: Английский

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A Blockchain-Based Scalability Solution with Microgrids Peer-to-Peer Trade

Energies, Journal Year: 2024, Volume and Issue: 17(4), P. 915 - 915

Published: Feb. 15, 2024

In recent years, advancements in rooftop solar panel technology have sparked a revolution the electricity markets. This has given rise to new concept of energy exchange—the ability for consumers and producers trade localized energy. been made possible by emergence blockchain technology, which gained significant traction Its unique facilitate peer-to-peer (P2P) transactions it promising solution trilemma scalability, security, decentralization. However, while shown great potential, is still its early stages development yet reach full potential. To fully understand potential P2P trading, important explore depth. study proposes blockchain-based scalability with focus on trading. strategy supported empirical modeling, utilizing data gathered from trial case study. The results this demonstrate that suggested technique outperforms base-layer models terms maintaining essential elements security proposed not only revolutionize markets but also broader implications. By providing more secure decentralized platform address issues distribution inequality promote adoption renewable With individuals communities opportunity take control their usage production, reducing reliance traditional centralized systems. lower costs contributes overall goal carbon emissions mitigating effects climate change. combination applications create shift toward sustainable will benefit positive impact environment global market. transition occur, crucial governments companies continue support invest these advancements.

Language: Английский

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Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 11, 2025

The growing number of connected devices in smart home environments has amplified security risks, particularly from Man-in-the-Middle (MitM) attacks. These attacks allow cybercriminals to intercept and manipulate communication streams between devices, often remaining undetected. Traditional rule-based methods struggle cope with the complexity these attacks, creating a need for more advanced, adaptive intrusion detection systems. This research introduces AEXB Model, hybrid deep learning approach that combines feature extraction capabilities an AutoEncoder classification power XGBoost. By combining complementary methods, model enhances accuracy significantly reduces false positives. Model's methodology encompasses robust preprocessing steps, including data cleaning, scaling, dimensionality reduction, followed by comprehensive engineering selection techniques, such as Recursive Feature Elimination (RFE) correlation analysis. applying this Intrusion Detection Smart Home (IDSH) dataset, achieves impressive 97.24% accuracy, demonstrating its effectiveness identifying anomalous network behavior indicative MitM Additionally, model's real-time rapid responses threats, thus providing continuous protection dynamic environments.

Language: Английский

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Deep learning for intrusion detection in IoT networks

Peer-to-Peer Networking and Applications, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 9, 2025

Language: Английский

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