Iraqi Journal of Data Science

WSNs have increasingly become part and parcel of the current and emerging communication systems, hence the need for solid measures to protect this critical infrastructure from evolving cyber threats. This research investigates the development of a Supervised Machine Learning-based IDS using MLP, SVM, and LR to increase the detection of network attacks for prevention purposes. A new method is proposed that entails the integration of an inter-dataset evaluation strategy that harmonizes two heterogeneous data structures and simultaneously aims to protect models and sensitive data. The conceived framework assesses the accuracy of a variety of machine learning metrics, such as accuracy, precision, recall, and the F1 score. As we can see from the results, detection efficiency was significantly improved with the incorporation of inter-dataset routing. MLP achieved a maximum accuracy of 99.1% with a recall of 100% in cross-dataset testing, showcasing its robustness in identifying all positive instances. SVM demonstrated a precision of 99.4% in certain scenarios, effectively minimizing false positives and enhancing classification confidence. Logistic Regression also showed stable precision values, contributing to consistent detection performance. These analyses stress how existing techniques for machine learning algorithms used in IDS designs cover a wide variety of purposive uses, with one being the prevention of distributed denial-of-service attacks. The practical implications of this research include the recommendation of the usage of proper algorithms justified for the desired security level in the networks to the administrators of the networks and security entailment specialists. The work done consolidating the result and outcome of this study points a way forward for subsequent studies on WSN security, with the view of providing a springboard for the development of intrusion detection systems in view of the dynamic nature of cybersecurity.

The increasing complexity of how humans interact with and process information has demonstrated significant advancements in Natural Language Processing (NLP), transitioning from task-specific architectures to generalized frameworks applicable across multiple tasks. Despite their success, challenges persist in specialized domains such as translation, where instruction tuning may prioritize fluency over accuracy. Against this backdrop, the present study conducts a comparative evaluation of ChatGPT-Plus and DeepSeek (R1) on a high-fidelity bilingual retrieval-and-translation task. A single standardize prompt directs each model to access the Arabic-language news section of the College of Medicine, University of Baghdad, retrieve the three most recent articles, and translate them into English. ChatGPT-Plus fulfilled the prompt successfully, extracting authentic Arabic content and delivering fluent, semantically accurate English translations. DeepSeek (R1), by contrast, failed to retrieve the requested articles and instead produced only generic procedural advice – evidence of its lack of real-time web access and a retrieval-augmented generation (RAG) mechanism.

Intrusion Detection Systems (IDS) are elementary building blocks of network security that can be used to detect unauthorized access and malicious activity. But traditional IDS approaches often suffer from problems such as high false positives, inability to adapt quickly to new threats, and scalability. This paper presents an advanced intrusion detection model that uses machine learning algorithms like Random Forest, Support Vector Machine (SVM), and Neural Networks to enhance detection. Using the KDD Cup 1999 data, the framework was highly preprocessed, feature engineered, and hyperparameters adjusted to achieve optimal performance. The Neural Network model outperformed other algorithms at 92.5% accuracy, 93.8% recall, and 92.4% F1-score, proving its ability to identify complex attack patterns with minimal false positives effectively. Additionally, the proposed framework reflected significant improvement over existing IDS solutions that always achieve accuracies of 80–85%. Intrusion Detection Systems (IDS) are important components of security, assuming the task of monitoring, detecting, and responding to unauthorized activities in network frameworks. This work's most notable contributions are its integration of sophisticated machine learning methods, systematic assessment of detection performance on a wide range of attack types, and comparison with well-established IDS benchmarks. In spite of facing issues like the complexity of the dataset and computational requirements, findings point to the efficacy of machine learning-based IDS in countering modern-day cybersecurity threats. Real-time data fusion and improving model interpretability for real-world implementation are areas that need to be addressed in the future.

Wireless Sensor Networks are critical from the security point of view because of their distributed nature and resource constraints. Artificial Intelligence techniques have shown promising results in intrusion detection, but their performance optimization is paramount. This paper proposes a new approach based on combining Multi-Layer Perceptron neural networks with the Cuckoo Optimization Algorithm for efficient intrusion detection in WSN. Our methodology involves three main steps: (1) data preprocessing using the k-nearest neighbor for missing value imputation and normalization, (2) reduction of dimensionality through Principal Component Analysis, reducing the features from 41 to 38 dimensions, and (3) neural network optimization using COA for weight and bias parameter tuning. Our approach has yielded an accuracy of 99.1% in intrusion detection using the NSL-KDD dataset, which shows an improvement of about 3% compared to traditional methods. The proposed system performs better in terms of detection accuracy, reduction of false alarm rate, and computational efficiency.

With the enhancement of technology facilitating the expansion of businesses and thoughts, more and more people are applying for loans for personal or business use. However, banks have limited assets, which limit the amount of loans that can be granted. Identifying the right persons to grant loans to can be a time-consuming process. Banks seek to grant loans to individuals who can repay the loan on time, enabling the bank to obtain maximum profits. This work aims to solve the loan default problem with minimum costs to banks. This work consists of five main stages: pre-processing, feature extraction, machine learning techniques, evaluation models, and performance analysis to select the best machine learning models. Then, two datasets with different features are used. The first dataset has five features, and the second contains eighteen features. We are splitting the datasets into various training percentages (40%, 50%, 60% and 70%). The rest of the dataset is used for testing using only the Weka application. KNN is applied with different cross-validations, such as 15, 10, and 5, and different numbers of nearest neighbours (1, 5, 10, and 15). For the first dataset, the highest accuracy is 97.47% with two cross-validation values, 15 and 10, in the 10 nearest neighbours. The KNN was also implemented on the second dataset to compute the highest accuracy, 88.21% in three cross-validation values (15, 10, and 5) with the 15 nearest neighbours. Then, logistic regression is applied to compare the results of the correct classification value computed at the highest value of 96.93% with the (70% training set for the first dataset. The highest accuracy was obtained at 88.32% after splitting the second dataset (40%) for training and the rest for testing.

Object discovery has advanced significantly with the emergence of deep learning models; however, existing algorithms often fail to deliver highly accurate and feature-focused detection, particularly in challenging visual environments. This study addresses the limitation by proposing a novel framework that integrates the high-level detection capabilities of YOLOv8 with the precision-focused characteristics of SURF-based feature extraction (referred to here as "Surzet"). The proposed method leverages YOLOv8 to perform comprehensive object detection while utilizing Surzet to enhance the identification of fine-grained features and local properties, ensuring robustness to scale and rotation. Experimental evaluation on complex image datasets revealed that this hybrid model significantly outperforms YOLOv8 alone, showing higher detection accuracy and a noticeable reduction in false positives. The initial results demonstrate that integrating YOLOv8 with Surzet creates a more reliable and precise object discovery framework. This approach holds great promise for high-level detection and detailed feature recognition applications.