A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City

Elsaeidy, Asmaa; Jamalipour, Abbas; Munasinghe, Kumudu S.

doi:10.1109/access.2021.3128701

Cited by 39 publications

(15 citation statements)

References 47 publications

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“…The risk value of the node is calculated based on successive rate and risk probability. Initially, the nodes (17) From the above equation, M denotes the nodes population size, N ij represents the nodes position i in the j − th dimension (Dm), n maxi − n mini denotes the interval of the nodes, and r (1, Dm) random numbers which is distributed uniformly in a Dm dimensional vector. After node initialization, the nodes are trying to finding the solution for optimization problem by iterative manner.…”

Section: Intrusion Scenario Reconstructionmentioning

confidence: 99%

“…Compared with the traditional IDS scenarios, blockchain mainly focuses on cyber-attacks with good tracing of behaviours and also provides a reliable connection to normal IoT devices [15,16]. Hence, security of IoT environment is improved and targeted intrusion prevention is reached while authenticating all IoT devices into the third party or security managers [17]. Evidence collection and analysis can be helpful in IDS, which compares the device pattern to the logically related transactions for attacks detection.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

Kably¹,

Benbarrad²,

Alaoui³

et al. 2023

Computers, Materials &Amp; Continua

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Blockchain merges technology with the Internet of Things (IoT) for addressing security and privacy-related issues. However, conventional blockchain suffers from scalability issues due to its linear structure, which increases the storage overhead, and Intrusion detection performed was limited with attack severity, leading to performance degradation. To overcome these issues, we proposed MZWB (Multi-Zone-Wise Blockchain) model. Initially, all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm (EBA), considering several metrics. Then, the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph (B-DAG), which considers several metrics. The intrusion detection is performed based on two tiers. In the first tier, a Deep Convolution Neural Network (DCNN) analyzes the data packets by extracting packet flow features to classify the packets as normal, malicious, and suspicious. In the second tier, the suspicious packets are classified as normal or malicious using the Generative Adversarial Network (GAN). Finally, intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization (IMO) is used for attack path discovery by considering several metrics, and the Graph cut utilized algorithm for attack scenario reconstruction (ASR). UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator (NS-3.26). Compared with previous performance metrics such as energy consumption, storage overhead accuracy, response time, attack detection rate, precision, recall, and F-measure. The simulation result shows that the proposed MZWB method achieves high performance than existing works

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Section: Intrusion Scenario Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

Kably¹,

Benbarrad²,

Alaoui³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Elsaeidy et al [260] focused on demonstrating by the trial tests that the uses of the CNN, with a moderately basic structure contrasted with those utilized in literature, offers fascinating discovery and classify the IM opening stator winding faults outcomes. The best possible recognition of the beginning between turn short-circuits of the IM stator winding was accomplished in the wake of estimating the most extreme 2000 examples of the demonstrative signstator current transient.…”

Section: Smart Energy Management System In Smart Homesmentioning

confidence: 99%

“…It will not be a door-to-door service but rather operates with predetermined stops within 150 meters of the destination. A centralized control system will continually contact the cab, which will take the quickest route to the destination [260]. The complete route that taxis may travel is made up of 87 stops.…”

Section: F Smart Mobilitymentioning

confidence: 99%

A Decade Review on Smart Cities: Paradigms, Challenges and Opportunities

et al. 2022

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Smart City has been an emerging research domain for Government, Businesses, and researchers in the last few years. The Indian government is also interested and investing lots of funds to develop smart cities. These cities are technology-based and require interdisciplinary research and development for successful implementation. Over the last few decades, various technological interventions have created a tendency to provide smart everyday objects to make human life more comfortable. The emergence of the smart city paradigm is a response to creating a future city that guarantees the well-being and rights of its citizens from the perspective of industrial development: industry, urban planning, environment, and sustainable development. There are several subdomains in the smart city for the research. To work with the different subdomains in a smart city, proper guidance about the background of the smart city is required. This research paper is a guide for the same. This research paper represents a systematic literature review of the smart city domain. This paper carries out a systematic review of research papers published in various well-reputed journals like IEEE, Springer, Elsevier, etc., between 2011 and. This paper will help the government, businesses, and researchers aiming to enhance the smart city concept. Initially, this paper discusses the origin and emergence of this concept, followed by a few definitions and characteristics with the real roadmap and primary supporting pillars of the smart city. This paper discusses a typical architecture having different layers like Sensing, Transportation, Data Management, and Application Layers. There are various supporting technologies and platforms for the smart city; hence implementations are impossible without these technologies and media. This research paper discusses different components of the smart city. A broad literature survey is being done to observe various challenges, opportunities, and future trends in the smart city. This research paper can guide the researchers and provide the research direction in the smart city domain.

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“…In fact, as shown in [30][31][32], machine learning (ML) technologies are used in intrusion detection systems, but dataset pre-processing and anomaly traffic detection are time-consuming and complex [33,34]. However, using deep learning (DL) technologies [35][36][37][38], features may be mapped to a higher degree with more distinct feature spaces. Combining DL technologies and a traditional ML model reduces the preprocessing time [39].…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

et al. 2022

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Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization–whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO–WOA optimization algorithm’s fitness value is defined as the validation set’s cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO–WOA–CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO–WOA–CNN algorithm against APSO–CNN, SVM, and FNN. The simulation results suggest that APSO–WOA–CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO–WOA–CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO–CNN algorithm, and the APSO–CNN algorithm achieves the best performance, as compared to other algorithms.

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A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City

Cited by 39 publications

References 47 publications

Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

A Decade Review on Smart Cities: Paradigms, Challenges and Opportunities

A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

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