A Novel Tunicate Swarm Algorithm With Hybrid Deep Learning Enabled Attack Detection for Secure IoT Environment

Taher, Fatma; Elhoseny, Mohamed; Hassan, Mohammed K.; El‐Hasnony, Ibrahim M.

doi:10.1109/access.2022.3226879

Cited by 6 publications

(2 citation statements)

References 35 publications

(42 reference statements)

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“…Second, they must continue looking for their greatest search agent. Finally, they need to keep close to the agent [20]. The swarm knowledge of the additional tunicates in a www.ijacsa.thesai.org statistical framework is employed for updating their locations relative to the ideal solution.…”

Section: Tunicate Swarm Optimizationmentioning

confidence: 99%

Enhancing Water Quality Forecasting Reliability Through Optimal Parameterization of Neuro-Fuzzy Models via Tunicate Swarm Optimization

Kumar,

Godla

et al. 2024

IJACSA

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Forecasting water quality is critical to environmental management because it facilitates quick decisionmaking and resource allocation. On the opposite hand, current methods are not always able to produce reliable forecasts, which is often due to challenges in parameter optimization for complex models. This research presents a novel approach to enhance the forecasting accuracy of water quality by optimizing neuro-fuzzy models using Tunicate Swarm Optimisation (TSO). The introduction highlights the limitations of current techniques as well as the necessity for precise estimates of water quality. One of the drawbacks is that neuro-fuzzy models are not well-modelled, which makes it harder for them to identify the minute patterns in data on water quality. The suggested approach is unique in that it applies TSO, an optimization algorithm inspired by nature that emulates tunicates' behaviour, to the neuro-fuzzy models' parameter optimization process. The highly complex parameter space is effectively navigated by TSO's swarm intelligence, which strikes a balance between exploration and exploitation to improve model performance. To optimize model parameters, the process comprises three steps: creating an objective function, defining the neuro-fuzzy model, and seamlessly integrating TSO. By mimicking the motions of tunicates as they look for the best conditions in the marine environment, TSO constantly optimizes the variables. Experiments demonstrate that the proposed strategy is more effective than traditional optimization techniques in forecasting water quality. As seen by the optimised neuro-fuzzy model's increased prediction accuracy and several dataset validations, Tunicate Swarm Optimisation has potential for reliable environmental forecasting. This work presents a potential path for improved environmental decisionmaking systems by offering an optimisation strategy inspired by nature that overcomes the limitations of existing methods and enhances water quality forecasting tools.

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Section: Tunicate Swarm Optimizationmentioning

confidence: 99%

Enhancing Water Quality Forecasting Reliability Through Optimal Parameterization of Neuro-Fuzzy Models via Tunicate Swarm Optimization

Kumar,

Godla

et al. 2024

IJACSA

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“…Therefore, APK files need to be opened to extract the required data from the app's files. In our experiments, AAPT2 is used to access application-specific files [50]. As an attribute, applications' access to the AndroidManifest.xml file reveals which permissions they require.…”

Section: Feature Extractionmentioning

confidence: 99%

A Proposed Artificial Intelligence Model for Android-Malware Detection

Taher,

Al Fandi,

Al Kfairy

et al. 2023

Informatics

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There are a variety of reasons why smartphones have grown so pervasive in our daily lives. While their benefits are undeniable, Android users must be vigilant against malicious apps. The goal of this study was to develop a broad framework for detecting Android malware using multiple deep learning classifiers; this framework was given the name DroidMDetection. To provide precise, dynamic, Android malware detection and clustering of different families of malware, the framework makes use of unique methodologies built based on deep learning and natural language processing (NLP) techniques. When compared to other similar works, DroidMDetection (1) uses API calls and intents in addition to the common permissions to accomplish broad malware analysis, (2) uses digests of features in which a deep auto-encoder generates to cluster the detected malware samples into malware family groups, and (3) benefits from both methods of feature extraction and selection. Numerous reference datasets were used to conduct in-depth analyses of the framework. DroidMDetection’s detection rate was high, and the created clusters were relatively consistent, no matter the evaluation parameters. DroidMDetection surpasses state-of-the-art solutions MaMaDroid, DroidMalwareDetector, MalDozer, and DroidAPIMiner across all metrics we used to measure their effectiveness.

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