Attack Detection on IoT Based Smart Cities using IDS Based MANFIS Classifier and Secure Data Transmission Using IRSA Encryption

Duraisamy, Ayyer; Subramaniam, M.

doi:10.1007/s11277-021-08362-x

Cited by 15 publications

(4 citation statements)

References 33 publications

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“…However, comparative security and performance analyses of this scheme have not been carried out. Similarly, feasibility, scalability and comparative analyses against the state of the art techniques are missing in 54 .…”

Section: Related Workmentioning

confidence: 99%

Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme

Nyangaresi,

Abduljabbar,

Mutlaq

et al. 2024

Sci Rep

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Advancements in cloud computing, flying ad-hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels. Although many security techniques have been designed to address this issue, most of them are still vulnerable to attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings and blockchain. In this paper, we leverage on biometrics, error correction codes and fuzzy commitment schemes to develop a secure and energy efficient authentication scheme for the smart cities. This is informed by the fact that biometric data is cumbersome to reproduce and hence attacks such as side-channeling are thwarted. We formally analyze the security of our protocol using the Burrows–Abadi–Needham logic logic, which shows that our scheme achieves strong mutual authentication among the communicating entities. The semantic analysis of our protocol shows that it mitigates attacks such as de-synchronization, eavesdropping, session hijacking, forgery and side-channeling. In addition, its formal security analysis demonstrates that it is secure under the Canetti and Krawczyk attack model. In terms of performance, our scheme is shown to reduce the computation overheads by 20.7% and hence is the most efficient among the state-of-the-art protocols.

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Section: Related Workmentioning

confidence: 99%

Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme

Nyangaresi,

Abduljabbar,

Mutlaq

et al. 2024

Sci Rep

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show abstract

“…However, DRL needs a lot of data and a lot of computation. A modified adaptive neuro‐fuzzy inference system (MANFIS)‐based instruction detection system (IDS) was proposed by Duraisamy et al 13 to highlight attack detection in smart cities. The MANFIS classifier was introduced in this article to detect the attack on IoT smart cities.…”

Section: Literature Surveymentioning

confidence: 99%

A novel‐cascaded ANFIS‐based deep reinforcement learning for the detection of attack in cloud IoT‐based smart city applications

Almasri

Alajlan

2023

Concurrency and Computation

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SummaryVast usages of Internet of Things (IoT) devices in various smart applications have laid a foundation for the evolution of modern smart cities. The increasing dependency of smart city applications on communication and information technologies enhances operational efficiency, sustainability, and automation of city services. However, due to the heterogeneous nature of IoT devices, the network faces critical security issues while executing continued network operations and services, particularly by cyber‐attacks. One of the predominant and rampant cyber‐attacks in smart city applications is botnet attacks. Therefore, a novel deep learning model for the detection and isolation of cyber‐attacks is proposed in the cloud IoT‐based smart city applications to protect against such cyber‐attacks. The proposed framework utilizes two different modules to automatically detect and isolate the malicious traffic emanating from compromised IoT devices with more efficiency. Here, two different datasets namely the IoT network intrusion and the ISCX 2012 IDs datasets are utilized for the evaluation of the proposed framework. In the first phase, the compromised device which communicates malicious network traffics through the network is identified using a cascaded adaptive neuro‐fuzzy inference system (CANFIS). After detection, IP address of abnormal traffic is recorded and informed to the system administrator. In the second phase, communication pathways of compromised devices with other normal devices are blocked and the compromised devices are isolated from the network using the modified deep reinforcement learning (MDRL) approach. The analytic result shows that the proposed framework achieves a greater accuracy rate of about 98.7% as compared to other state‐of‐art methods.

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“…Routing protocols and communication standards need to be further investigated. Also, novel datasets need to be designed which will be helpful for researchers and scientists for further experimentations [77][78][79].…”

Section: Data Testingmentioning

confidence: 99%

Cognitive Lightweight Logistic Regression-Based IDS for IoT-Enabled FANET to Detect Cyberattacks

Rahman

Aziz

Usman

et al. 2023

Mobile Information Systems

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In recent few years, flying ad hoc networks are utilized more for interconnectivity. In the topological scenario of FANETs, IoT nodes are available on ground where UAVs collect information. Due to high mobility patterns of UAVs cause disruption where intruders easily deploy cyberattacks like DoS/DDoS. Flying ad hoc networks use to have UAVs, satellite, and base station in the physical structure. IoT-based UAV networks are having many applications which include agriculture, rescue operations, tracking, and surveillance. However, DoS/DDoS attacks disturb the behaviour of entire FANET which lead to unbalance energy, end-to-end delay, and packet loss. This research study is focused about the detail study of machine learning-based IDS. Also, cognitive lightweight-LR approach is modeled using UNSW-NB 15 dataset. IoT-based UAV network is introduced using machine learning to detect possible security attacks. The queuing and data traffic model is utilized to implement DT, RF, XGBoost, AdaBoost, Bagging and logistic regression in the environment of IoT-based UAV network. Logistic regression is the proposed approach which is used to estimate statistical possibility. Overall, experimentation is based on binomial distribution. There exists linear association approach in logistic regression. In comparison with other techniques, logistic regression behaviour is lightweight and low cost. The simulation results presents logistic regression better results in contrast with other techniques. Also, high accuracy is balanced well in optimal way.

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Attack Detection on IoT Based Smart Cities using IDS Based MANFIS Classifier and Secure Data Transmission Using IRSA Encryption

Cited by 15 publications

References 33 publications

Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme

Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme

A novel‐cascaded ANFIS‐based deep reinforcement learning for the detection of attack in cloud IoT‐based smart city applications

Cognitive Lightweight Logistic Regression-Based IDS for IoT-Enabled FANET to Detect Cyberattacks

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