Hasan Hashim’s research while affiliated with Taibah University and other places

The proliferation of Internet of Things (IoT) devices has brought about an increased threat of botnet attacks, necessitating robust security measures. In response to this evolving landscape, deep learning (DL)-based intrusion detection systems (IDS) have emerged as a promising approach for detecting and mitigating botnet activities in IoT environments. Therefore, this paper thoroughly reviews existing literature on botnet detection in the IoT using DL-based IDS. It consolidates and analyzes a wide range of research papers, highlighting key findings, methodologies, advancements, shortcomings, and challenges in the field. Additionally, we performed a qualitative comparison with existing surveys using author-defined metrics to underscore the uniqueness of this survey. We also discuss challenges, limitations, and future research directions, emphasizing the distinctive contributions of our review. Ultimately, this survey serves as a guideline for future researchers, contributing to the advancement of botnet detection methods in IoT environments and enhancing security against botnet threats.

This paper investigates the creation of real-time experimentation in Artificial Intelligence (AI) by means of a code-driven approach, which addresses the dynamic nature of AI applications in contemporary contexts. The complexities of real-world scenarios are frequently not captured by traditional AI experimentation, which heavily depends on static datasets and preset criteria. This study illustrates the process of adapting and enhancing AI models in live environments by combining real-time data processing with continuous method optimization. The methodology entails the establishment of real-time data channels, the execution of AI models in dynamic conditions, and the application of numerical analysis to quantify performance enhancements. The primary findings. indicate that real-time experimentation substantially enhances the accuracy, productivity, and flexibility of models in comparison to conventional methods. The results are corroborated by meticulous numerical experiments, which encompass metrics such as precision, recall, accuracy, and processing times. This research contributes to the expanding field of AI by illustrating the efficacy of real-time, code-driven testing and offering practical insights. This work has a wide-ranging impact on a variety of industries, as the demand for real-time, adaptive AI solutions becomes more urgent. These methods could be further refined and additional applications across various AI domains could be explored in future research.

Abstract: Background/Objectives:The emergence of monkeypox outside its endemic region in Africa has raised significant concerns within the public health community due to its rapid global dissemination. Early clinical differentiation of monkeypox from similar diseases, such as chickenpox and measles, presents a challenge. The Monkeypox Skin Lesion Dataset (MSLD) used in this study comprises monkeypox skin lesions, which were collected primarily from publicly accessible sources. The dataset contains 770 original images captured from 162 unique patients. The MSLD includes four distinct class labels: monkeypox, measles, chickenpox, and normal. Methods: This paper presents an ensemble model for classifying the monkeypox dataset, which includes transformer models and support vector machine (SVM). The model development process begins with an evaluation of seven convolutional neural network (CNN) architectures. The proposed model is developed by selecting the top four models based on evaluation metrics for performance. The top four CNN architectures, namely EfficientNetB0, ResNet50, MobileNet, and Xception, are used for feature extraction. The high-dimensional feature vectors extracted from each network are then concatenated and optimized before being inputted into the SVM classifier. Results: The proposed ensemble model, in conjunction with the SVM classifier, achieves an accuracy of 95.45%. Furthermore, the model demonstrates high precision (95.51%), recall (95.45%), and F1 score (95.46%), indicating its effectiveness in identifying monkeypox lesions. Conclusions: The results of the study show that the proposed hybrid framework achieves robust diagnostic performance in monkeypox detection, offering potential utility for enhanced disease monitoring and outbreak management. The model's high diagnostic accuracy and computational efficiency indicate that it can be used as an additional tool for clinical decision support.

Intrusion Detection Systems, specifically Network Anomaly Detection Systems (NADSs) are vital tools in network security. The NADSs are affected by data imbalance issues in classifying minority classes. Also, designing an efficient detection framework is sought after to achieve a higher detection rate for minority classes, especially when utilizing ensemble learning methods. To solve the issue of imbalanced data, a hybrid method of sampling techniques is proposed. This imbalance processing tool integrates the Synthetic Minority Oversampling Technique (SMOTE) and the K-means clustering algorithm (SKM). SMOTE over-samples the minority class, and K-means is used to perform a cluster-based under-sampling. We use Denoising Autoencoder (DAE) to select the top 15 features to reduce data dimensionality based on their higher weights. For anomaly detection, the XGBoost algorithm is deployed and the SHapley Additive exPlanation (SHAP) approach is deployed to provide explanations of the proposed techniques. The performance of the SKM-XGB model is assessed using the NSL-KDD and UNSW-NB15 datasets. A comparative analysis and series of experiments were carried out using several ensemble models with multiple base classifiers. The experimental findings indicate that the model's detection rate for binary classification and multiclass classification using the UNSW-NB15 dataset is 99.01% and 97.49%, respectively. The model achieves a 99.37% detection rate for binary classification and a 99.22% detection rate for multiclass classification on the NSL-KDD dataset. We conducted a comparative analysis of various ensemble models with multiple base classifiers. The results indicate that SKM-XGB outperforms the other investigated models and outperforms the performance of state-of-the-art models.

A zero-confirmation transaction is a transaction that has not yet been confirmed on the blockchain and is not yet part of the blockchain. The network propagates zero-confirmation transactions quickly, but they are not secured against double-spending attacks. In this study, the proposed method is used to secure zero-confirmation transactions by using the security hashing algorithm 512 in elliptic curve cryptography (ECDSA) instead of the security hashing algorithm 256. This is to generate a cryptographic identity to secure the transactions in zero-confirmation transactions instead of security hashing algorithm 256. The results show that SHA-512 is greater than SHA-256 in throughput. Additionally, SHA-512 offers better throughput performance than SHA-256 while also having a larger hash size. Results also show that SHA-512 is more secure than SHA-256.

The world has recently been plagued by the pandemic of Corona Virus Disease 2019 (COVID-19). Since it is reported in Wuhan city of China, on the 8th of December 2019, the COVID-19 invaded every country around the world. As of October 24th, 2020, a total of 42,549,383 confirmed cases of COVID-19 were officially announced and the death toll was 1,150,163. Globally, huge volumes of datasets are generated regarding COVID-19 pandemic to open new research arena for machine learning and artificial intelligence researchers. In this work, an integration of data warehouse with deep learning approach, namely LSTM model, is introduced to predict the spread of the COVID-19 in selected countries. We present the design and development of COVID-warehouse, a data warehouse that integrates and stores the COVID-19 data made available daily by different countries. The basic idea of the framework is to use a COVID19 time-series dataset for analysis by machine learning models to make forecasting of future trend based on present values. Ultimately, the proposed prediction model can be applied to predict for other countries as the nature of the virus is the same everywhere. In terms of R2 metric, the experimental results of the decision tree model outperforms other models for recovery cases compared with confirmed and death cases. Recovery cases have a R2 of 0.996011, death cases have a R2 of 0.993124 and confirmed cases have a R2 of 0.991676. Finally, our results emphasize the importance of enforcing the public health advice of social distancing as well as applying the infection control measures to combat COVID-19 before it becomes too late.

Misinformation is highly circulating on social media which harmfully affect users of these platforms and the online content value. Previous efforts to decrease misinformation distribution on social media have mainly focused on the development of misinformation detection algorithms. To extend these efforts, this paper adopts gamification techniques to minimize the spread of misinformation on social media and proposes a three-phase requirement engineering method for the design of a Gamified Misinformation-aware Social Media (GMSM). The method combines the strengths of well-known requirement engineering approaches in a sequence that offers software engineers better understanding of users’ requirements on the adoption of gamification to minimize the spread of misinformation on social media. This can lead to a better coverage of important users’ requirements thus, a better user satisfaction and a higher quality of online content.