Enhancing Phishing URL Detection Accuracy in Software-Defined Networks (SDNs) through Feature Selection and Machine Learning Techniques

A. Usha; George Chellin Chandran

doi:https://doi.org/10.54216/JCIM.170216

Journal of Intelligent Systems and Internet of Things | 2025

Hybrid AI Ensemble for Real-Time Adaptive Optimization in Solar Energy Systems

Solar energy systems play a crucial role in fulfilling global energy needs sustainably; however, their performance is often affected by dynamic environmental factors. This study investigates the use of Artificial Intelligence (AI) for real-time optimization and adaptive control to improve the operational efficiency of solar energy systems. The research specifically addresses output variability arising from fluctuations in solar irradiance, temperature, and panel soiling, limitations that conventional control approaches fail to manage effectively. The primary goal is to develop intelligent AI-based models capable of predicting and automatically adjusting critical system parameters in real time, thereby reducing manual intervention and enhancing operational reliability. Data from a solar photovoltaic (PV) and thermal hybrid testbed in Jodhpur, India were collected over a six-month period. The Indian Meteorological Department provided more than 10000 hourly data samples that included weather and seasonal variations. An NI DAQ system with high-precision sensors was used to measure important parameters such as solar irradiance panel, and ambient temperatures wind speed inclination angle and energy output. For predictive control, the suggested methodology uses a hybrid ensemble framework that combines Extreme Gradient Boosting (XGBoost), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Deep Neural Networks (DNN). In this framework, XGBoost carries out variable importance ranking to determine the dominant influencing factors ANFIS enables adaptive operational control and DNNs forecast energy output. In contrast to previous research that concentrated on distinct AI methods this work presents a cohesive hybrid approach that integrates feature significance analysis adaptive optimization and forecasting accuracy into a single system. The hybrid ensemble model outperforms individual approaches in achieving stable and effective energy generation according to evaluation using RMSE, R2, and MEF metrics. Furthermore, its compatibility with IoT-enabled edge devices underscores its potential for large-scale, real-time, and automated solar energy management within future smart grid infrastructures, advancing global efforts toward sustainable energy transitions.

groups

Srinivasa Chanakya Muramshetti mail -

Kishore Kunal mail -

R. Murugadoss mail -

Vairavel Madeshwaren mail

link https://doi.org/10.54216/JISIoT.170218

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Low-Cost Multi-Sensor Localization for Indoor AGVs

Indoor transportation systems are a key area of development, where Automated Guided Vehicles (AGVs) help to increase efficiency and reduce labor costs. However, high-precision positioning technologies such as LiDAR and GNSS are expensive, making them unsuitable for widespread use. This research has developed a low-cost positioning system for indoor AGVs using multiple sensors, including CCTV, UWB, inertial measurement units (IMUs), and encoders. The experiment was carried out under both static and dynamic conditions. In static tests, Trilateration distance measurements show a lower positioning error than the triangular method, with a maximum error of 1.4464 m (x-axis) and 1.0464 m (y-axis) in dynamic tests. The integrated Encoder and IMU sensor data yielded the lowest error (RMSE = 0.0732 m at 0.4 m/s, 0.0678 at 0.27 m/s), Next is CCTV, while UWB has the highest error rate. The application of a Parallel Sensor Fusion architecture optimized using a Generalized Reduced Gradient (GRG) nonlinear algorithm, significantly reduced localization errors. The RMSE values decreased to 0.0623 m (0.4 m/s) and 0.0411 m (0.27 m/s). The results, in a controlled environment laboratory, indicate that combining multiple sensors will improve the positioning accuracy. Combining the encoder and IMU effectively reduces accumulated errors and increases system stability. While Adjust the weight of the sensor offline, this proposed system offers a cost-effective positioning solution for indoor AGVs, which contributes to the development of affordable and accurate AGV navigation systems.

groups

Nopparut Khaewnak mail -

Siripong Pawako mail -

Akkharachai Kosiyanurak mail -

Suradet Tantrairatn mail -

Jiraphon Srisertpol mail

link https://doi.org/10.54216/JISIoT.170219

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Enhanced Lightweight Cryptography-based Authentication Protocol for IoT Devices

The rapid advancement of telecommunication infrastructures and endpoint technologies has led to a significant incorporation of Internet of Things devices in modern lifestyles. IoT involves a wide range of applications, such as connected video surveillance systems for security, wearable body sensors for health monitoring, and temperature sensors for environmental control in agricultural fields. These devices are essential for gathering and transmitting data in real-time. However, data acquisition and transmission processes are often exposed to serious security threats, particularly concerning data integrity, user privacy, and communication reliability. Conventional security mechanisms are typically inappropriate to resource constrained IoT devices. Thus, to overcome these challenges, extensive research has been devoted to developing secure communication frameworks, with a particular focus on robust authentication and key agreement protocols. Authentication is essential to guarantee the legitimacy of the information source, and many proposed AKA schemes rely on asymmetric cryptographic techniques. In this paper, we introduce an Enhanced Lightweight Cryptography-based Authentication Protocol for IoT devices, conceived to meet the computational constraints of IoT devices by employing simple XOR and hashing operations. The protocol enables mutual authentication between IoT devices and routers without the need to share credentials directly. Prior to authentication, an offline registration phase is conducted through an Authentication Server (AS), which generates unique key parameters based on the identifiers of the devices and routers. These parameters are securely distributed to both parties. Authentication is then performed using these pre-shared parameters in a computationally efficient yet secure manner that safeguards against common security threats. Theoretical analysis demonstrates that the proposed protocol is resistant to several common attacks, including man-in-the-middle, impersonation, session key disclosure, replay, and eavesdropping attacks. Additionally, the protocol ensures device anonymity and data privacy while maintaining lightweight performance suitable for constrained IoT environments.

groups

Sanâ Elaoudi mail -

Marouane Sebgui mail -

Slimane Bah mail

link https://doi.org/10.54216/JISIoT.170220

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

A Developed Non-Polynomial Spline Method for Solving Fuzzy Partial Differential Equations

This research introduces a novel approach to the non-polynomial spline dependent method for solving fuzzy partial differential equations. The tensor product of non-polynomial spline functions is derived in order to obtaining a solution to fuzzy partial differential equations, such as fuzzy hyperbolic and parabolic equations. The advantage of this method is that it simplifies the complex procedure that arises from the term of the typical product of a fuzzy number by fuzzy functions. Examples are presented to show that the outcomes of the research indicate that the technique is extremely useful to construct the solution to the desired fuzzy partial differential equations.

groups

Ahmed Hanoon Abud mail -

Laheeb Muhsen Noman mail -

Ahmed Bakheet mail

link https://doi.org/10.54216/IJNS.270124

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

MFWX: Multi-Scale CNN with Multi-Frequency Channel Attention and Weighted Particle Swarm Optimization for Enhanced Brain Tumor Segmentation and Classification

The research-automated segmentation of brain tumors occurs due to the need to enhance diagnosis and/or treatment planning. The existing techniques suffer the effects of scale variation, redundant features, and the high dimensionality that causes ambiguous findings. We suggest the model named MFWX, which unites Multi-Scale CNN, Multi-Frequency Channel Attention (MFCA), Weighted Particle Swarm Optimization (WPSO) to identify features and XGBoost methods to classify them. The Multi-Scale CNN will capture the structure of the tumor at multiple resolutions, MFCA adjusts the features by zeroing in on significant frequency zones and WPSO eliminates redundancy to heavy-hit the strong forecasts of XGBoost. However, MFWX attained 94.2 accuracy and 92.5 Dice on the BraTS-2020 dataset surpassing ResNet50, EfficientNet-B7, and U-Net. It achieved an accuracy of 96.7%, and Dice of 95.1% on BraTS-2018, and performed well on classes of tumors. Ablation experiments proved the necessity of every part. In general, MFWX presents an efficient, clinically meaningful, scalable solution that outsmarts the current segmentation techniques.

groups

Mohammed Nazneen Fathima mail -

Prabhjot Singh1 mail -

Simrandeep Singh mail

link https://doi.org/10.54216/JISIoT.170221

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

A Real-Time Sign Language Recognition Framework Using Deep Learning and Internet of Things

  Sign language is a vital communication mean for hearing-impaired individuals, combining manual gestures with non-manual signs like facial expressions and body movements, often requiring both hands and sequential actions. Recently, an automatic Sign Language Recognition (SLR) has gained increasing attention, with Machine Learning and Deep Learning systems achieving competitive performance. While convolutional neural network has been widely employed owing to their effectiveness in image-based recognition tasks, existing methods, however, often struggle with efficiency, adaptability, and real-time deployment. This paper proposes an Internet of Things-Integrated Deep Learning Model for Real-Time SLR to enhance the communication among individuals with hearing-impairment and non-signers. The framework employs IoT-based wearable sensors for capturing hand and finger movements, followed by Sobel filtering for noise reduction. MobileNetV3 is applied for lightweight feature extraction, while a Variational AutoEncoder enables robust sign detection. To further improve performance, an Improved Sparrow Search Algorithm is introduced for hyperparameter tuning, constituting the novelty of this work. Experimental results show that the proposed framework achieves an outstanding accuracy of 99.05% when compared to state-of-the-art systems, validating its robustness and effectiveness for real-time SLR applications

groups

Lama Al Khuzayem mail -

Soukeina Elhassen mail

link https://doi.org/10.54216/FPA.210225

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

An Efficient Hybrid Approach Model for SARS-CoV-2 Prediction Using an Optimized Deep Learning Recurrent Neural Network and Fuzzy inference

SARS-CoV2 virus has affected the peoples in worldwide with several issues, like health and economy. Moreover, mathematical definition of fractal dimension affords a method for calculating the non-linear dynamic behaviour difficulty revealed through time series of countries. The fuzzy logic model illustrates and manages the characteristic uncertainty of classification issue. In this paper, an effectual SARS-CoV2model is developed using optimized Deep learning model through time series data. The derived features are derived from the input sequential data for disease forecasting. Moreover, over sampling scheme is exploited for data augmentation, which enhances the prediction process. Fuzzy systems and various distance measures are calculated for choosing most significant features. The Deep Recurrent Neural network (DRNN) is applied for performing SARS-CoV2prediction, in which DRNN is trained through designed Fractional Water Poor and Rich Optimization (FrWPRO) method. Meanwhile, the training process of DRNN using hybrid optimization model from scratch proves that, the designed SARS-CoV2prediction method accomplishes better performance compared to other existing approaches with Mean Square Error (MSE), Root MSE (RMSE), and Mean Absolute Percentage Error (MAPE) of 0.1425, and 0.3775, and 0.3467 respectively.

groups

Zaid Derea mail -

Ammar Kazm mail -

Jasim Mohammed mail -

Oday Ali Hassen mail -

Esraa Saleh Alomari mail

link https://doi.org/10.54216/FPA.210227

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Secure and Efficient Novel Keystream Generator for Stream Ciphers

The Internet of Things real-time communications depend on a secure stream of data. For the secure communications, a stream cipher with the features of ease and speediness is appropriate. The development and testing of a novel cryptographic algorithm with the goal of enhancing encryption performance. This paper introduces novel A matrix-based nonlinear pseudorandom key stream generation method inspired by the principles of fundamental recursive relationship of Reinforcement Learning, aiming to enhance diffusion and randomness in stream ciphers. We also incorporate the encryption approach based on the Counter based transformation of keystream generation (CBTKSG) method to enhance the speed, which is particularly well-suited for efficiently handling large file sizes since it delivers fast throughput. The technique was thoroughly bench marked and compared to other well-known encryption schemes. Performance has significantly improved without sacrificing security, according to the data. The keystream output was placed through the NIST SP 800-22 statistical test suite to verify its cryptographic strength. It passed every test with high p-values, indicating high randomness quality. The cipher has a strong avalanche effect, meets standard security criteria like IND-CPA and IND-CCA, and resists common cryptanalysis methods including related-key, differential, and linear attacks.

groups

Chaithanya S. mail -

Siddesh G. K. mail

link https://doi.org/10.54216/JISIoT.170222

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Enhancing Breast Cancer Detection in CESM Mammograms: Impact of Data Augmentation on U-NET Segmentation Performance

When using mammography to diagnose breast cancer, segmenting medical scans is a crucial step.  Accurate segmentation facilitates early diagnosis, which in turn makes it possible to administer individualized treatment plans, ultimately improving patient outcomes. However, for these Deep Learning (DL) models to be trained efficiently and perform optimally, they require access to large datasets.   The lack of sufficient photographs in many publicly available datasets to adequately train deep learning models is a common flaw.   Therefore, this work aims to examine the effects of various affine data augmentations on the Dice Score of a U-NET model utilizing a recently released public dataset of Contrast-Enhanced Spectral Mammography (CESM) images.  The collection consists of 1003 CESM images and matching segmentation masks made by a certified radiologist.   Modifying certain model parameters on the CESM dataset and investigating the impact of single and combination data augmentations on the model's overall performance are the objectives of the study. Images that were moved in the x-direction and sheared vertically were used to train the best-performing model.  On the test set, the model's Dice Score was 56.6%, which was 9% better than the baseline result and showed how crucial data augmentation is when working with small datasets.

groups

Taha Y. Abdulqader mail -

Kifaa Hadi Thanoon mail -

Shatha A. Baker mail

link https://doi.org/10.54216/JISIoT.170223

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Enhancing Phishing URL Detection Accuracy in Software-Defined Networks (SDNs) through Feature Selection and Machine Learning Techniques

Phishing attacks remain a persistent and ever-evolving threat to both networked systems and their users' privacy. In response to this formidable challenge, our research delves into an innovative approach designed to enhance the precision of phishing Uniform Resource Locator (URL) detection within the dynamic and programmable realm of Software-Defined Networks (SDNs). By harnessing feature selection capabilities and adaptive machine learning techniques, our proposed framework aims to fortify security measures in SDNs against these malicious campaigns. Our methodology's core is the deliberate selection of discriminative features from the extensive network data attributes. This feature selection process is meticulously designed to identify the most relevant characteristics associated with phishing URLs, thereby enabling the extraction of invaluable insights for more precise detection. These carefully chosen features then serve as inputs for a dynamic machine-learning model, trained to adapt and evolve alongside the constantly changing landscape of phishing attacks. Within the SDN environment, our framework optimizes utilizing network resources and controller processing power. It achieves this by reducing the dimensionality of input data, resulting in improved detection accuracy and a decrease in false positives. The adaptive nature of our machine-learning model ensures rapid recognition of emerging phishing tactics, thereby reducing the risk of succumbing to novel and sophisticated attacks. To validate the effectiveness of our approach, we conducted extensive experiments and evaluations within an SDN testbed, utilizing real-world phishing URL datasets. The results consistently demonstrate that our framework surpasses conventional methods, achieving higher detection accuracy and adaptability to evolving threats. In summary, our research represents a significant stride in the ongoing battle against phishing attacks by leveraging the dynamic capabilities of SDNs. The synergy between feature selection and adaptive machine learning techniques empowers SDNs to sustain accurate and effective phishing URL detection, ultimately reinforcing network security and safeguarding user privacy in an ever-evolving threat landscape.

groups

A. Usha Ruby mail -

George Chellin Chandran J. mail

link https://doi.org/10.54216/JCIM.170216

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new