Internet of vehicles intrusion detection method based on CFS-COA feature selection and spatio-temporal feature extraction DOI
Zhongjun Yang, J.X. Zhang,

Borcherng Su

et al.

The Computer Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 7, 2024

Abstract With the rapid spread of Internet Vehicles (IoV) technology, vehicle network security is facing increasingly severe challenges. Intrusion detection technology has become a crucial tool for ensuring information IoV. Since traffic data IoV large and spatio-temporal characteristics, most previous studies are based on single deep learning method to extract temporal or spatial features, which does not fully features data. To address above issues, feature extraction model with selection proposed. First, solve problem long time huge traffic, new proposed screen optimal subset by combining correlation-based crayfish optimization algorithm (CFS-COA). Second, selected used in that combines Temporal Convolutional Network Bidirectional Gated Recurrent Unit (TCN-BiGRU) classification. Finally, performance evaluated using two types datasets: NSL-KDD UNSW-NB15 datasets external communications, Car-Hacking dataset in-vehicle networks. The experimental results indicate demonstrates high classification lightweight achieving 100% accuracy dataset.

Language: Английский

Data Security and Privacy Concerns in Drone Operations DOI
Siva Raja Sindiramutty, N. Z. Jhanjhi, Chong Eng Tan

et al.

Advances in information security, privacy, and ethics book series, Journal Year: 2024, Volume and Issue: unknown, P. 236 - 290

Published: Jan. 26, 2024

The widespread use of drones across various industries is leading to significant transformations. However, the resulting concerns about data security and privacy are quite significant. This section offers a thorough exploration these important issues, providing insights into challenges they pose potential ways address them. Starting with an overview increasing utility drones, this chapter highlights importance strong protocols for privacy. By examining complexities collection storage, it reveals different types that gather, delves storage techniques, vulnerabilities, setting stage effective countermeasures. At core discussion cybersecurity risks, which range from cyberattacks on drone systems unauthorized access tampering data. To sum up, serves as comprehensive guide understanding, addressing, mitigating related in operations.

Language: Английский

Citations

19
Deep Learning-driven Methods for Network-based Intrusion Detection Systems: A Systematic Review DOI Creative Commons
Ramya Chinnasamy, Malliga Subramanian,

Sathishkumar Veerappampalayam Easwaramoorthy

et al.

ICT Express, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

1
Optimizing Intrusion Detection for IoT: A Systematic Review of Machine Learning and Deep Learning Approaches With Feature Selection and Data Balancing DOI Open Access
S Kumar Reddy Mallidi, Rajeswara Rao Ramisetty

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2025, Volume and Issue: 15(2)

Published: March 28, 2025

ABSTRACT As the Internet of Things (IoT) continues expanding its footprint across various sectors, robust security systems to mitigate associated risks are more critical than ever. Intrusion Detection Systems (IDS) fundamental in safeguarding IoT infrastructures against malicious activities. This systematic review aims guide future research by addressing six pivotal questions that underscore development advanced IDS tailored for environments. Specifically, concentrates on applying machine learning (ML) and deep (DL) technologies enhance capabilities. It explores feature selection methodologies aimed at developing lightweight solutions both effective efficient scenarios. Additionally, assesses different datasets balancing techniques, which crucial training models perform accurately reliably. Through a comprehensive analysis existing literature, this highlights significant trends, identifies current gaps, suggests studies optimize frameworks ever‐evolving landscape.

Language: Английский

Citations

1
OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems DOI Creative Commons

Siva Surya Narayana Chintapalli,

Satya Prakash Singh, Jaroslav Frnda

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(8), P. e29410 - e29410

Published: April 1, 2024

Currently, the Internet of Things (IoT) generates a huge amount traffic data in communication and information technology. The diversification integration IoT applications terminals make vulnerable to intrusion attacks. Therefore, it is necessary develop an efficient Intrusion Detection System (IDS) that guarantees reliability, integrity, security systems. detection considered challenging task because inappropriate features existing input slow training process. In order address these issues, effective meta heuristic based feature selection deep learning techniques are developed for enhancing IDS. Osprey Optimization Algorithm (OOA) proposed selecting highly informative from which leads differentiation among normal attack network. Moreover, traditional sigmoid tangent activation functions replaced with Exponential Linear Unit (ELU) function propose modified Bi-directional Long Short Term Memory (Bi-LSTM). Bi-LSTM used classifying types ELU makes gradients extremely large during back-propagation faster learning. This research analysed three different datasets such as N-BaIoT, Canadian Institute Cybersecurity Dataset 2017 (CICIDS-2017), ToN-IoT datasets. empirical investigation states framework obtains impressive accuracy 99.98 %, 99.97 % 99.88 on CICIDS-2017, datasets, respectively. Compared peer frameworks, this high better interpretability reduced processing time.

Language: Английский

Citations

7
A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security DOI Creative Commons
K. Sai Prasad,

P. Udayakumar,

E. Laxmi Lydia

et al.

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

Published: Jan. 17, 2025

Adversarial attacks were commonly considered in computer vision (CV), but their effect on network security apps rests the field of open investigation. As IoT, AI, and 5G endure to unite understand potential Industry 4.0, events incidents IoT systems have been enlarged. While networks efficiently deliver intellectual services, vast amount data processed collected also creates severe concerns. Numerous research works keen project intelligent intrusion detection (NIDS) avert exploitation through smart applications. Deep learning (DL) models are applied perceive alleviate numerous against networks. DL has a considerable reputation NIDS, owing its robust ability identify delicate differences between malicious normal activities. diversity aimed at influencing techniques for protection, whether these methods exposed adversarial examples is unidentified. This study introduces Two-Tier Optimization Strategy Robust Attack Mitigation (TTOS-RAAM) model security. The major aim TTOS-RAAM technique recognize presence attack behaviour IoT. Primarily, utilizes min-max scaler scale input into uniform format. Besides, hybrid coati-grey wolf optimization (CGWO) approach utilized optimum feature selection. Moreover, employs conditional variational autoencoder (CVAE) detect attacks. Finally, parameter adjustment CVAE performed by utilizing an improved chaos African vulture (ICAVO) model. A wide range experimentation analyses outcomes observed under aspects using RT-IoT2022 dataset. performance validation portrayed superior accuracy value 99.91% over existing approaches.

Language: Английский

Citations

0
Design of advanced intrusion detection in cybersecurity using ensemble of deep learning models with an improved beluga whale optimization algorithm DOI Creative Commons
Fatimah Alhayan,

Nuha Alruwais,

Mohammad Alamgeer

et al.

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 121, P. 90 - 102

Published: Feb. 26, 2025

Language: Английский

Citations

0
AI-driven cybersecurity framework for software development based on the ANN-ISM paradigm DOI Creative Commons
Habib Ullah Khan, Rafiq Ahmad Khan, Hathal Salamah Alwageed

et al.

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

Published: April 18, 2025

Abstract With the increasing reliance on software applications, cybersecurity threats have become a critical concern for developers and organizations. The answer to this vulnerability is AI systems, which help us adapt little better, as traditional measures in security failed respond upcoming threats. This paper presents an innovative framework using AI, by Artificial Neural Network (ANN)—Interpretive Structural Modeling (ISM) model, improve threat detection, assessment, risk response during development. helps realize dynamic, intelligent part of Software Development life cycle (SDLC). Initially, existing risks coding are systematically evaluated identify potential gaps integrate best practices into proposed model. In second phase, empirical survey was conducted validate findings systematic literature review (SLR). third hybrid approach employed, integrating ANN real-time detection assessment. It utilizes ISM analyze relationships between vulnerabilities, creating structured understanding interdependencies. A case study last stage test evaluate AI-driven Mitigation Model Secure Coding. multi-level categorization system also used assess maturity across five key levels: Ad hoc, Planned, Standardized, Metrics-Driven, Continuous Improvements. identifies 15 vulnerabilities coding, along with 158 mitigating these risks. areas insecure develops scalable model address different levels. results show that outperforms systems detecting weaknesses simultaneously fixing problems. During Levels 1–3 improvement process, advanced methods protect against Our analysis reveals organizations at 4 5 still need entirely shift AI-based protection tools techniques. provides managers valuable insights, enabling them select enhancements tailored their organization's development stages. supports automated analysis, helping stay vigilant introduces novel ANN-ISM modeling formalisms. By merging secure principles, research enhances connection AI-generated insights real-world usage.

Language: Английский

Citations

0
Advancements in Grid Resilience: Recent Innovations in AI-Driven Solutions DOI Creative Commons

Sana Hafez,

Mohammad Alkhedher, Mohamed Ramadan

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105042 - 105042

Published: April 1, 2025

Language: Английский

Citations

0
IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine DOI

Nguyen Van Thieu,

Essam H. Houssein, Diego Oliva

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: unknown, P. 129062 - 129062

Published: Nov. 1, 2024

Language: Английский

Citations

3
TOWARDS IMPROVED THREAT MITIGATION IN DIGITAL ENVIRONMENTS: A COMPREHENSIVE FRAMEWORK FOR CYBERSECURITY ENHANCEMENT DOI Creative Commons
Hewa Balisane, Ehigiator Egho-Promise,

Emmanuel Lyada

et al.

International Journal of Research -GRANTHAALAYAH, Journal Year: 2024, Volume and Issue: 12(5)

Published: June 14, 2024

In today's digital landscape, cybersecurity has become a critical concern due to the increasing sophistication of cyber threats. Traditional measures are often inadequate against evolving attacks, necessitating development comprehensive and adaptive threat mitigation frameworks. This study aims address this gap by proposing robust framework that integrates advanced technologies such as artificial intelligence (AI), machine learning (ML), blockchain enhance detection, response, recovery capabilities. The adopts layered defense mechanism, real-time monitoring, proactive hunting provide holistic approach cybersecurity. By examining current methodologies identifying their limitations, research highlights necessity for enhanced strategies. Through mixed-methods involving online surveys literature review, develops flexible, scalable, capable countering sophisticated Key recommendations include adopting technologies, continuous training, enhancing sharing, implementing strategy, conducting regular security audits. improve organizational resilience, ensuring safety integrity environments in face an ever-evolving landscape.

Language: Английский

Citations

2