Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Alrowais, Fadwa; Almasoud, Ahmed S.; Marzouk, Radwa; Al‐Wesabi, Fahd N.; Hilal, Anwer Mustafa; Rizwanullah, Mohammed; Motwakel, Abdelwahed; Yaseen, Ishfaq

doi:10.32604/cmc.2022.025204

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…The first step discussed above i.e., attaining the feature indication in which fruitful information regarding the URL is saved in a vector so that the ML methods can be implied to it. Several kinds of features have been assumed earlier such as content features, lexical features, popular features, and host-based features [9,10]. However, lexical features are the most widely used features as they have proved to yield superior outcomes and are comparatively simple to attain [11].…”

Section: Introductionmentioning

confidence: 99%

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Hilal¹,

Hashim²,

Mohamed³

et al. 2023

Computers, Materials &Amp; Continua

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Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs. To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model. The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository. The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

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Section: Introductionmentioning

confidence: 99%

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Hilal¹,

Hashim²,

Mohamed³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

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“…Numerous current policies were offered for presenting energy efficient (EE) routing in WSNs. But, clustering-related protocols were mostly used for routing [9,10]. The energy can be effectively used when sending the data packets to the BS.…”

Section: Introductionmentioning

confidence: 99%

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Rajesh¹,

Acharya²,

Hajiyev³

et al. 2023

Computers, Materials &Amp; Continua

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Recently, Internet of Things (IoT) has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications. Wireless sensor network (WSN) is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management, intelligent transportation, home automation, smart buildings, and other such IoT-based application. The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is, for balancing the energy amongst sensor nodes. The clustering and routing procedures assumed that Non-Polynomial (NP) hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems. With this motivation, this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning (BCEHGS-RP) scheme for IoT assisted WSN. The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supported WSN environment. In addition, an effective multihop route planning approach was designed by the use of EHGS technique. The proposed EHGS technique is derived from the concept of Hill Climbing strategy (HCS) and HGS algorithm. Moreover, a fitness function with two parameters namely residual energy (RE) and intercluster distance to elect optimal routes. The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes.

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“…Most of the privacy and security methods advanced by authors were intended to ensure security measures on the IoD network [10,11]. The approaches focused on mitigating problems which influence the secure localization of drones or privacy and security necessities linked with the IoD network system.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Search-and-Rescue Optimization-Enabled Secure Route Planning Scheme for Internet of Drones Environment

et al. 2022

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The Internet of Drones (IoD) is greatly developed and promotes many civil applications. However, it can still be prone to several security problems which threaten public safety. The issue of security poses further problems upon linking the IoD to the Internet, as its data stream is exposed to attack. For secure communication between drones, an effective route planning scheme with a major intention of accomplishing security is needed. With this aim, this study develops an enhanced search-and-rescue optimization-enabled secure route planning (ESRO-SRP) scheme for the IoD environment. The presented ESRO-SRP technique mainly aims to derive a set of optimal routes to the destination. In addition, the ESRO-SRP algorithm is derived by the integration of the quasi-oppositional-based learning (QOBL) concept with the conventional SRO algorithm. Moreover, the presented ESRO-SRP technique derived a fitness function encompassing different input parameters such as residual energy, distance, and degree of trust. The experimental validation of the ESRO-SRP technique is carried out under several aspects, and the results demonstrated the enhancements of the ESRO-SRP model over recent approaches. The ESRO-SRP model has provided an increased packet delivery ratio (PDR) of 86%, whereas the BRUe-IoE, ORP-FANET, UAVe-WSN, and TR-UAV Swarm approaches have accomplished a minimal PDR of 79.60%, 73.60%, 67.60%, and 63.20%, respectively.

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Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Cited by 9 publications

References 18 publications

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Enhanced Search-and-Rescue Optimization-Enabled Secure Route Planning Scheme for Internet of Drones Environment

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