Genetic Algorithm based feature selection and Naïve Bayes for anomaly detection in fog computing environment

Onah, John Oche; Abdulhamid, Shafií Muhammad; Mohammed, Abdullahi; Hassan, Ibrahim Hayatu; Al-Ghusham, Abdullah

doi:10.1016/j.mlwa.2021.100156

Cited by 40 publications

(23 citation statements)

References 3 publications

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“…Furthermore, to solve real-world issues, evolutionary computation employs an optimization search technique like genetic algorithms. For instance, a genetic algorithm is used in cybersecurity to detect irregularities in a fog computing environment 145 . A genetic algorithm is also utilized for optimal feature selection to identify Android malware using machine learning techniques 146 .…”

Section: Hybrid Approach Searching and Optimizationmentioning

confidence: 99%

Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Sarker

2023

Security and Privacy

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Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber-attacks, threats, risks, damages, or unauthorized access. Artificial Intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real-world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI-based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this paper, we provide a comprehensive view on "Cybersecurity Intelligence and Robustness", emphasizing multi-aspects AI-based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero-day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI-based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

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Section: Hybrid Approach Searching and Optimizationmentioning

confidence: 99%

Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Sarker

2023

Security and Privacy

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“…The results revealed that the proposed optimal learning model attained medium-level detection accuracy, but with the limitation of many false alarms that may lead to misleading classification. In [39], the authors presented a Genetic Algorithm Wrapper-Based feature selection and Nave Bayes for Anomaly Detection Model (GANBADM) in a Fog Environment that removes immaterial features. GANBADM used the NSL-KDD dataset for result evaluation.…”

Section: B Anomaly Detection In Fog and Fog-to-things Environmentmentioning

confidence: 99%

Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

et al. 2023

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Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication.Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learningbased dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era.

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“…Ferrag et al 9 used different classifier methods based on DT and rule-based concept to detect Internet of things attacks. Onah et al 10 adopted NB for anomaly detection in fog computing. Alrashdi et al 2 proposed an intelligent anomaly detection system for smart city based on RM.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, we review six representative supervised anomaly detection methods in recent studies, such as logistic regression (LR), 7 support vector machine (SVM), 8 decision tree (DT), 9 naive Bayes (NB), 10 random forest (RM), 2 and multi-layer perceptron (MLP). 11 We also perform a fine-grained evaluation of these algorithms under low-quality data.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for Internet of things anomaly detection under low-quality data

Han

Yang

2022

International Journal of Distributed Sensor Networks

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With the popularization of Internet of things, its network security has aroused widespread concern. Anomaly detection is one of the important technologies to protect network security. To meet the needs of automatic and intelligent detection, supervised machine learning is widely used in anomaly detection. However, the existing schemes ignore the problem of data quality, which leads to the unsatisfactory detection effect in practice. Therefore, practitioners may not know which algorithm to choose due to the lack of review and evaluation of anomaly detection methods under low-quality data. To address this problem, we give a detailed review and evaluation of six supervised anomaly detection methods, as well as release the core code of feature extractor for pcap format traffic traces and anomaly detection methods for reuse. We evaluate the methods on two public datasets (one is a simulated network dataset and the other is a real Internet of things dataset). We believe that our work and insights will help practitioners quickly understand and develop anomaly detection schemes for Internet of things and can provide reference for future research.

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Genetic Algorithm based feature selection and Naïve Bayes for anomaly detection in fog computing environment

Cited by 40 publications

References 3 publications

Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

Machine learning for Internet of things anomaly detection under low-quality data

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