A Blockchain-Oriented Framework for Cloud-Assisted System to Countermeasure Phishing for Establishing Secure Smart City

Kumar, Narendra; Goel, Vikas; Ranjan, Raju; Altuwairiqi, Majid; Alyami, Hashem; Asakipaam, Simon Atuah

doi:10.1155/2023/8168075

Cited by 3 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aggressors can use vulnerable sites to execute malicious intent [5]. For instance, attackers inject cross-site scripting into susceptible sites to acquire the sensitive data of the target or execute phishing.…”

Section: Introductionmentioning

confidence: 99%

Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model

Aljebreen,

Alrayes,

Aljameel

et al. 2023

Sustainability

View full text Add to dashboard Cite

With the enhancement of the Internet of Things (IoT), smart cities have developed the idea of conventional urbanization. IoT networks permit distributed smart devices to collect and process data in smart city structures utilizing an open channel, the Internet. Accordingly, challenges like security, centralization, privacy (i.e., execution data poisoning and inference attacks), scalability, transparency, and verifiability restrict faster variations of smart cities. Detecting malicious URLs in an IoT environment is crucial to protect devices and the network from potential security threats. Malicious URL detection is an essential element of cybersecurity. It is established that malicious URL attacks mean large risks in smart cities, comprising financial damages, losses of personal identifications, online banking, losing data, and loss of user confidentiality in online businesses, namely e-commerce and employment of social media. Therefore, this paper concentrates on the proposal of a Political Optimization Algorithm by a Hybrid Deep Learning Assisted Malicious URL Detection and Classification for Cybersecurity (POAHDL-MDC) technique. The presented POAHDL-MDC technique identifies whether malicious URLs occur. To accomplish this, the POAHDL-MDC technique performs pre-processing to transform the data to a compatible format, and a Fast Text word embedding process is involved. For malicious URL recognition, a Hybrid Deep Learning (HDL) model integrates the features of stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM). Finally, POA is exploited for optimum hyperparameter tuning of the HDL technique. The simulation values of the POAHDL-MDC approach are tested on a Malicious URL database, and the outcome exhibits an improvement of the POAHDL-MDC technique with a maximal accuracy of 99.31%.

show abstract

Section: Introductionmentioning

confidence: 99%

Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model

Aljebreen,

Alrayes,

Aljameel

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

Analysis of Techniques and Methods for Health Informatics: A Quantum Leap in Health Care

Akoramurthy,

Surendiran

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Enhancing cybersecurity in cloud-assisted Internet of Things environments: A unified approach using evolutionary algorithms and ensemble learning

Aljebreen,

Mengash,

Mahmood

et al. 2024

MATH

View full text Add to dashboard Cite

<abstract> <p>Internet of Things (IoT) security is an umbrella term for the strategies and tools that protect devices connected to the cloud, and the network they use to connect. The IoT connects different objects and devices through the internet to communicate with similarly connected machines or devices. An IoT botnet is a network of infected or cooperated IoT devices that can be remotely organized by cyber attackers for malicious purposes such as spreading malware, stealing data, distributed denial of service (DDoS) attacks, and engaging in other types of cybercrimes. The compromised devices can be included in any device connected to the internet and communicate data with, e.g., cameras, smart home appliances, routers, etc. Millions of devices can include an IoT botnet, making it an attractive tool for cyber attackers to launch attacks. Lately, cyberattack detection using deep learning (DL) includes training neural networks on different datasets to automatically detect patterns indicative of cyber threats, which provides an adaptive and proactive approach to cybersecurity. This study presents an evolutionary algorithm with an ensemble DL-based botnet detection and classification (EAEDL-BDC) approach. The goal of the study is to enhance cybersecurity in the cloud-assisted IoT environment via a botnet detection process. In the EAEDL-BDC technique, the primary stage of data normalization using Z-score normalization is performed. For the feature selection process, the EAEDL-BDC technique uses a binary pendulum search algorithm (BPSA). Moreover, a weighted average ensemble of three models, such as the modified Elman recurrent neural network (MERNN), gated recurrent unit (GRU), and long short-term memory (LSTM), are used. Additionally, the hyperparameter choice of the DL approaches occurs utilizing the reptile search algorithm (RSA). The experimental outcome of the EAEDL-BDC approach can be examined on the N-BaIoT database. The extensive comparison study implied that the EAEDL-BDC technique reaches a superior accuracy value of 99.53% compared to other approaches concerning distinct evaluation metrics.</p> </abstract>

show abstract

A Blockchain-Oriented Framework for Cloud-Assisted System to Countermeasure Phishing for Establishing Secure Smart City

Cited by 3 publications

References 35 publications

Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model

Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model

Analysis of Techniques and Methods for Health Informatics: A Quantum Leap in Health Care

Enhancing cybersecurity in cloud-assisted Internet of Things environments: A unified approach using evolutionary algorithms and ensemble learning

Contact Info

Product

Resources

About