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We live in an era when time is a precious resource. Thus, dealing with the vast amount of data collected from different resources for various purposes requires creating systems that can process the data in a reasonable time to make it worthwhile. Accessing big data in machine learning and artificial intelligence models creates efficient, robust models. In this work, we present a method to create a multi-class classification model using Apache-spark. The model is built and trained with the CIC-DDOS2019 dataset to build a DDoS Attack detection model. Ensemble modeling was used to improve the accuracy and robustness of the model. At the same time, Apache-spark was used to distribute the vast amount of training and testing data over the models used to create the intrusion detection model. The proposed model has achieved high accuracy (99.94\%) while reducing the execution time to almost the half when applied without Apache-spark. [JJCIT 2024; 10(2.000): 123-137]

Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

Privacy-preserving of medical information (such as medical records and images) is an essential right for patients to ensure security against undesired access parties. This right is typically protected by law through firm regulations set by healthcare authorities. However, sensitive-private data usually requires the application of further security and privacy mechanisms such as encipherment (encryption) techniques. ’Medical images’ is one such example of highly demanding security and privacy standards. This is due to the quality and nature of the information carried among these images, which are usually sensitive-private information with few features and tonal variety. Hence, several state-of-the-art encryption mechanisms for medical images have been proposed and developed; however, only a few were efficient and promising. This paper presents a hybrid crypto-algorithm, MID-Crypt, to secure the medical image communicated between medical laboratories and doctors’ accounts. MID-Crypt is designed to efficiently hide medical image features and provide high-security standards. Specifically, MID-Crypt uses a mix of Elliptic-curve Diffie–Hellman (ECDH) for image masking and Advanced Encryption Standard (AES) with updatable keys for image encryption. Besides, a key management module is used to organize the public and private keys, the patient’s digital signature provides authenticity, and integrity is guaranteed by using the Merkle tree. Also, we evaluated our proposed algorithm in terms of several performance indicators including, peak signal-to-noise ratio (PSNR) analysis, correlation analysis, entropy analysis, histogram analysis, and timing analysis. Consequently, our empirical results revealed the superiority of MID-Crypt scoring the best performance values for PSNR, correlation, entropy, and encryption overhead. Finally, we compared the security measures for the MID-Crypt algorithm with other studies, the comparison revealed the distinguishable security against several common attacks such as side-channel attacks (SCA), differential attacks, man-in-the-middle attacks (MITM), and algebraic attacks.