SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection

Daoud, Wided Ben; Mahfoudhi, Sami

doi:10.32604/cmc.2022.020141

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…These factors are evaluated with the help of true positive (TP), true negative (TN), false positive (FP), and false negative (FN). However, we rely essentially on two main metrics: accuracy and the F-measure which represents the harmonic mean of recall and precision [25].…”

Section: Resultsmentioning

confidence: 99%

Missing Data Recovery in the E-health Context Based on Machine Learning Models

Rahmany¹,

Mahfoudhi²,

Freihat³

et al. 2022

AAIML

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Diabetes mellitus is a set of metabolic illnesses characterized by abnormally high blood sugar levels. In 2017, 8.8% of the world’s population had diabetes. By 2045, it is expected that this percentage will have risen to approximately 10%. Missing data, a prevalent problem even in a well-designed and controlled study, can have a major impact on the conclusions that can be derived from the available data. Missing data may decrease a study’s statistical validity and lead to erroneous results due to distorted estimations. In this study, we hypothesize that (a) replacing missing values using machine learning techniques rather than the mean value and group mean value and (b) using SVM kernel RBF classifier will result in the highest level of accuracy in comparison to traditional techniques such as DT, RF, NB, SVM, AdaBoost, and ANN. The classification results improved significantly when using regression to replace the missing values over the group median or the mean. This is a 10% improvement over previously developed strategies that have been reported in the literature.

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Section: Resultsmentioning

confidence: 99%

Missing Data Recovery in the E-health Context Based on Machine Learning Models

Rahmany¹,

Mahfoudhi²,

Freihat³

et al. 2022

AAIML

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“…In addition to the methods mentioned above, there are also other different proposals such as [25], [26], [27], [28], [29], [30] in intrusion detection. Stressing privacy preservation in the IoT is important.…”

Section: Stefanova Et Al Inmentioning

confidence: 99%

A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing

Najafli,

Haghighat,

Karasfi

2024

Preprint

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The increasing growth of the Internet of Things and its open and shared character has led to a rise in new attacks exponentially. As a consequence, the quick detection of attacks in IoT environments is essential. The intrusion detection system (IDS) is responsible for protecting and detecting the type of attacks. Creating an IDS that works in real-time and adapts to environmental changes is critical. In this paper, we propose a deep reinforcement learning-based (DRL) adaptive IDS that addresses the mentioned challenges. DRL-based IDS helps to create a decision agent, who controls the interaction with the indeterminate environment and performs binary detection (normal/intrusion) in fog. For multi-class classification of attacks, we use the ensemble method in the cloud. The proposed approach is evaluated on the CICIDS2018 dataset, in binary and multiclass classification. The results show that the proposed model achieves a comparable performance in detecting intrusions and identifying attacks and it reduces the prediction time significantly. Furthermore, it is a suitable solution for underrepresented attacks. Overall, combining multiple methods can be a great way for an intrusion detection system.

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“…The IoT-MDD is distributed, flexible, and has a high detection rate. SIMAD, a secure, intelligent method for detecting assaults in IoT-fog environments [11], was proposed by Daoud and Mahfoudhi (2022). Expanding upon the lamentation of De Souza et al regarding the necessity for more advanced IDS, Daoud and Mahfoudhi propose a rational strategy to reinforce network security.…”

Section: Outline Of Iot-based Malicious Device Detection and Idsmentioning

confidence: 99%

Securing IoT networks: A fog-based framework for malicious device detection

Kumar Lingamallu,

Balasubramani,

Arvind

et al. 2024

MATEC Web Conf.

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Ensuring device security is a significant obstacle to effectively implementing the Internet of Things (IoT) and fog computing in today's Information Technology (IT) landscape. Researchers and IT firms have investigated many strategies to safeguard systems against unauthorized device assaults, often known as outside device assaults. Cyber-attacks and data thefts have significantly risen in many corporations, organizations, and sectors due to exploiting vulnerabilities in safeguarding IoT gadgets. The rise in the variety of IoT gadgets and their diverse protocols has increased zero-day assaults. Deep Learning (DL) is very effective in big data and cyber-security. Implementing a DL-based Gated Recurrent Unit (GRU) on IoT devices with constrained resources is unfeasible due to the need for substantial computational power and robust storage capacities. This study introduces an IoT-based Malicious Device Detection (IoT-MDD) that is dispersed, resilient, and has a high detecting rate for identifying various IoT cyber-attacks using deep learning. The suggested design incorporates an Intrusion Detection System (IDS) on fog nodes because of its decentralized structure, substantial processing capabilities, and proximity to edge gadgets. Tests demonstrate that the IoT-MDD model surpasses the performance of the other models. The study found that the cybersecurity architecture effectively detects malicious gadgets and decreases the percentage of false IDS alarms.

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SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection

Cited by 7 publications

References 25 publications

Missing Data Recovery in the E-health Context Based on Machine Learning Models

Missing Data Recovery in the E-health Context Based on Machine Learning Models

A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing

Securing IoT networks: A fog-based framework for malicious device detection

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