Cloud computing and 5G challenges and open issues

Ullah, Arif; Aznaoui, Hanane; Şahin, Canan Batur; Rafie, Mahnaz; Dinler, Özlem Batur; Imane, Laassar

doi:10.11591/ijaas.v11.i3.pp187-193

Cited by 8 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accuracy (AC) is defined by (20) as the proportion of samples that have been correctly identified to all samples (41) [28].…”

Section: Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Proposed algorithm base optimization scheme for intrusion detection using feature selection

Laassar,

Hadi

2024

IJAAS

View full text Add to dashboard Cite

<span>The number of devices linked to the internet is rapidly increasing as the internet has become ingrained in every aspect of modern life. However, certain issues are getting worse, and their resolutions are not well-defined. One of the main issues is convergence and speed for communication between different internet of things (IoT) devices and their security. For that purpose, in this paper, an improved artificial bee colony (ABC) algorithm with binary search equations along with neural networks is proposed, known as the artificial bee colony algorithm with binary search equations (BABCN) algorithm for intrusion detection in terms of convergence and speed for communication. The depth-first search framework and binary search equations on which the artificial bee colony algorithm with binary search equations algorithm is built improve the algorithm’s capacity for exploitation and speed up convergence. The initial weight and threshold value of the ABC neural networks are optimized using an algorithm to prevent them from entering a local optimum during the training procedure and accelerating training. The NSL-KDD dataset was used, and based on the results; the proposed algorithm improves classification and has high intrusion detection ability in the network. The proposed has undergone tests to be evaluated, and the results show that it performs better in detection accuracy, time, and false positive rate.</span>

show abstract

“…Accuracy (AC) is defined by (20) as the proportion of samples that have been correctly identified to all samples (41) [28].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…In ( 13) and ( 14) modify the threshold and weight values between the hidden layer and the output layer. The weight and threshold values between the input layer and the hidden layer are changed in accordance with (15) [27], [28].…”

mentioning

confidence: 99%

Proposed algorithm base optimization scheme for intrusion detection using feature selection

Laassar,

Hadi

2024

IJAAS

View full text Add to dashboard Cite

show abstract

“…This model is the most difficult and will enhance the voice of the operator the most after implementation [1] . Some research reviewed the current research status, key enabling technologies, current trends, and challenges of 5GC and public cloud in detail analyzed the existing problems of the current network and the requirements of various scenarios for 5GC network capabilities and proposed potential evolution solutions for the challenges [2][3][4] [5] . Some research focuses on the 5G core network cloud resource planning model, deployment scheme, and its characteristics.…”

Section: Introductionmentioning

confidence: 99%

Research and verification of lightweight 5GC on the public cloud

Liu,

Zhu,

et al. 2023

Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023)

View full text Add to dashboard Cite

5GC migration to the cloud has become the common choice of operators and telecom equipment providers. Compared to the complete 5G core (5GC) of the large network, the lightweight 5GC is better suited for the public cloud. Low cost, easy operation and maintenance, resource flexibility, and business agility have become the main drivers of lightweight 5GC deploy on the public cloud. This paper systematically summarizes the deployment scheme of lightweight 5GC on the public cloud and selects the central cloud sharing mode for experimental verification. It designs two links: cloud process test and cloud function test, evaluates the complexity of lightweight 5GC on the cloud and to summarize the current problems on the cloud through experiments. The results show that lightweight 5GC on the public cloud is feasible.

show abstract

Semantic-based vulnerability detection by functional connectivity of gated graph sequence neural networks

Şahin

2023

Soft Comput

Self Cite

View full text Add to dashboard Cite

In computer security, semantic learning is helpful in understanding vulnerability requirements, realizing source code semantics, and constructing vulnerability knowledge.Nevertheless, learning how to extract and select the most valuable features for software vulnerability detection remains difficult. We establish a hypothesis that the source projects' vulnerable functions include the project-independent vulnerable patterns shared among vulnerabilities across different software projects. The trained Gated Graph Sequence Neural Networks (GGNNs) can be utilized to discover the said patterns in order to detect vulnerabilities on a target project. It is necessary to identify vulnerable programming patterns by considering the context. Therefore, we use functional connectivity (FC) based on Gated Graph Neural Networks as a learning representation of features to capture long-term dependency to understand a high-level representation of potential vulnerabilities to learn context-dependence. The experimental findings indicate that the suggested Model can select relevant discriminative features and achieve superior performance than benchmark methods.

show abstract

Cloud computing and 5G challenges and open issues

Cited by 8 publications

References 28 publications

Proposed algorithm base optimization scheme for intrusion detection using feature selection

Proposed algorithm base optimization scheme for intrusion detection using feature selection

Research and verification of lightweight 5GC on the public cloud

Semantic-based vulnerability detection by functional connectivity of gated graph sequence neural networks

Contact Info

Product

Resources

About