Babylonian Journal of Machine Learning

Artificial Intelligence Predictions in Cyber Security: Analysis and Early Detection of Cyber Attacks

Meaad Ali Khalaf, Amani Steiti — Thu, 09 May 2024 00:00:00 +0000

The landscape of cyber-attacks has changed due, to the upward push of digitalization and interconnected structures. This necessitates the need for revolutionary techniques to emerge as aware of and mitigate these threats at a degree. This studies delves into the correlation amongst cyber security and artificial intelligence (AI) with a focus on how AI can decorate detection of cyber-attacks via assessment, prediction and different strategies. By harnessing machine mastering, neural networks and records analytics predictive models driven with the useful resource of AI have emerged as an approach to deal with the ever evolving demanding situations posed through cyber threats. The number one goal of this observe is to look at the effectiveness of AI powered prediction fashions, in cyber security. It ambitions to evaluate how nicely those AI based systems carry out as compared to cyber security techniques emphasizing their capability to proactively locate and mitigate cyber threats as a way to minimize their effect. Additionally ability obstacles and ethical issues associated with AI based cyber security answers are also discussed. Also using AI algorithms to Analysis and Early Detection of Cyber Attacks using python programming language. The research's conclusions are extremely important for the field of cyber security since they provide information about how threat mitigation and incident response will develop in the future. This research helps to develop cutting-edge cyber security solutions by addressing the dynamic and constantly-evolving landscape of cyber threats.

AI-Powered Anomaly Detection for Kubernetes Security: A Systematic Approach to Identifying Threats

Arvind Kumar Bhardwaj, P.K. Dutta , Pradeep Chintale — Tue, 20 Aug 2024 00:00:00 +0000

This study delves into the intricacies of AI-based threat detection in Kubernetes security, with a specific focus on its role in identifying anomalous behavior. By harnessing the power of AI algorithms, vast amounts of telemetry data generated by Kubernetes clusters can be analyzed in real-time, enabling the identification of patterns and anomalies that may signify potential security threats or system malfunctions. The implementation of AI-based threat detection involves a systematic approach, encompassing data collection, model training, integration with Kubernetes orchestration platforms, alerting mechanisms, and continuous monitoring. AI-powered threat detection offers numerous advantages, including predictive threat detection, increased accuracy and scalability, shorter response times, and the ability to adapt to evolving threats. However, it also presents challenges, such as ensuring data quality, managing model complexity, mitigating false positives, addressing resource requirements, and maintaining security and privacy standards. The proposed AI-powered anomaly detection framework for Kubernetes security demonstrated significant improvements in threat identification and mitigation. Through real-time analysis of telemetry data and leveraging advanced AI algorithms, the system accurately identified over 92% of simulated security threats and anomalies across various Kubernetes clusters. Additionally, the integration of automated alerting mechanisms and response protocols reduced the average response time by 67%, enabling rapid containment of potential breaches.

Image Enhancement using Convolution Neural Networks

Hasan Ahmed Salman, Ali Kalakech — Thu, 25 Jan 2024 00:00:00 +0000

The research presents a comprehensive exploration of the topic of image enhancement using convolutional neural networks (CNN).The research goes deeper into the advanced field of image processing based on the use of neural networks to automatically and efficiently improve the quality and detail of images. The thesis shows that convolutional neural networks are one of the types of deep neural networks, which are specially designed to gain knowledge from big data and extract complex features and patterns found in images. The different layers of the grid are discussed in detail, dealing with images incrementally and extracting different attributes in each layer. The research also highlights CNN's ability to detect, learn and improve important details found in images through convolutions, filtering and data aggregation processes. The proposed CNN image enhancement model was developed and tested on both medical and normal images. The images were optimized using the proposed model and compared with other models. Various quality measures were used to evaluate the results. The results showed that the proposed model can significantly improve the quality of images.

A Fuzzy Wavelet Neural Network (FWNN) and Hybrid Optimization Machine Learning Technique for Traffic Flow Prediction

Karthika Balasubramani, Uma Maheswari Natarajan — Thu, 11 Jul 2024 00:00:00 +0000

Traffic go with the flow forecasting is essential in urban planning and management, optimizing transportation structures and resource allocation. However, accurately predicting visitors glide is tough because of its inherent complexity, nonlinearity, and diverse uncertain factors. The trouble declaration underscores the issue in as it should be forecasting site visitors flow, mainly in urban environments characterized through dynamic and complex site visitor’s styles. In the existing paintings there are numerous traditional devices getting to know models used for visitors flow prediction, however those conventional strategies show off barriers in reaching excessive prediction accuracy. Therefore, the proposed work targets to put into effect hybrid optimization techniques for correct prediction in shipping machine. Here fuzzy wavelet neural community (FWNN) is used to address complicated nonlinear structures with uncertain conditions and hybrid optimization method called hybrid firefly and particle swarm optimization (HFO-PSO) which combines the exploration and exploitation talents of firefly and this fusion allows the version to capture intricate visitor’s styles efficiently and optimize the prediction technique, improving accuracy and efficiency. Moreover, the prediction performance of the proposed model is established and compared by means of the usage of distinct measures.

Intrusion Detection System Based on Machine Learning Algorithms:( SVM and Genetic Algorithm)

Abdulazeez Alsajri, Amani Steiti — Thu, 18 Jan 2024 00:00:00 +0000

The widespread utilization of the internet and computer systems has resulted in notable security concerns, characterized by a surge in intrusions and vulnerabilities. Malicious users manipulate internal systems, resulting in the exploitation of software flaws and default setups. With the integration of the internet into society, there is an emergence of new risks such as viruses and worms, which highlights the importance of implementing robust security measures. Intrusion detection systems (IDS) are security technologies utilized to monitor and analyze network traffic or system activity with the purpose of identifying hostile behavior. This article presents a proposed method for detecting intrusion in network traffic using a hybrid approach, which combines a genetic algorithm and an SVM algorithm. The model underwent training and testing on the KDDCup99 dataset, with a reduction in features from 42 to 29 using the hybrid approach. The results demonstrated that throughout the system testing, it exhibited a remarkable accuracy of 0.999. Additionally, it achieved a true positive value of 0.9987 and a false negative rate of 0.012.

Advanced Brain Tumor Classification Using DEEPBELEIF-CNN Method

Sat, 15 Jun 2024 00:00:00 +0000

Computer-aided research to improve image decoding is a long-standing theme in medical imaging. A variety of imaging techniques, including ultrasound imaging, The Magnetic Resonance Imaging (MRI), and Computed Tomography (CT), are generally depleted to estimate tumors in the prostate, lung, brain, breast, and liver. The study used MRI images of the brain to identify the tumors. Brain tumors are an almost common and cruel disease that can significantly shorten life expectancy. It is important to use MRI images to locate and classify contaminating tumors. There are many tumors including gliomas, meningiomas, pituitary tumors, and no tumors. One of the most difficult aspects of brain tumor assortment is the diagnosis and prevention of tumor type. Accurate tumor classification helps to assess disease progression and select therapeutic strategies. To resolve the issue, a Deep Belief Neural- Convolutional Neural Network (DeepBeliefCNN) method was proposed. At first phase, we preprocess the brain tumor MRI dataset by using 2D Wavelet Filter method. This method allows for the analysis of images at multiple resolutions, thus enabling detection of features at different scales. This effectively reduces noise in medical images while preserving important details. This improves the definition of tumor boundaries and other important features, helping in accurate diagnosis and analysis. Then preprocessing the dataset segment the preprocessed images based on Watershed method. It is a powerful image segmentation technique used to outline objects in images. When used for image segmentation of a brain tumor, it can help identify tumor boundaries within the brain. Markers are assigned to areas of interest in the image. These markers can be manually set by experts or generated automatically using techniques such as distance transforms and morphological functions. At last, the dataset is classify by using the DeepBeleifCNN method. The DeepBeleifCNN approach combines the hierarchical feature extraction capabilities of DBN with the spatial feature extraction capabilities of CNN. This allows a detailed understanding of brain tumor images and improves classification performance. This method successfully generalizes to new and unrecognized brain tumor images because of its capability to study healthy and biased features in together pre-training and fine-tuning stages. The investigational outcomes illustrate that our deployed methodology beats the existing method in accuracy, sensitivity, F1 score, specificity and error rate.

Random Forest Algorithm Overview

Hasan Ahmed Salman , Ali Kalakech , Amani Steiti — Sat, 08 Jun 2024 00:00:00 +0000

A random forest is a machine learning model utilized in classification and forecasting. To train machine learning algorithms and artificial intelligence models, it is crucial to have a substantial amount of high-quality data for effective data collecting. System performance data is essential for refining algorithms, enhancing the efficiency of software and hardware, evaluating user be-havior, enabling pattern identification, decision-making, predictive modeling, and problem-solving, ultimately resulting in improved effectiveness and accuracy. The integration of diverse data collecting and processing methods enhances precision and innovation in problem-solving. Utilizing diverse methodologies in interdisciplinary research streamlines the research process, fosters innovation, and enables the application of data analysis findings to pattern recognition, decision-making, predictive modeling, and problem-solving. This approach also encourages in-novation in interdisciplinary research. This technique utilizes the concept of decision trees, con-structing a collection of decision trees and aggregating their outcomes to generate the ultimate prediction. Every decision tree inside a random forest is constructed using random subsets of data, and each individual tree is trained on a portion of the whole dataset. Subsequently, the outcomes of all decision trees are amalgamated to derive the ultimate forecast. One of the bene-fits of random forests is their capacity to handle unbalanced data and variables with missing values. Additionally, it mitigates the issue of arbitrary variable selection seen by certain alterna-tive models. Furthermore, random forests mitigate the issue of overfitting by training several de-cision trees on random subsets of data, hence enhancing their ability to generalize to novel data. Random forests are highly regarded as one of the most efficient and potent techniques in the domain of machine learning. They find extensive use in various applications such as automatic categorization, data forecasting, and supervisory learning.

A Advancements in Machine Learning and Deep Learning for Early Diagnosis of Chronic Kidney Diseases: A Comprehensive Review

Akeel Shaker Mahmoud, Olfa Lamouchi , Safya Belghith — Tue, 17 Sep 2024 00:00:00 +0000

Chronic kidney disease (CKD) is a prevalent and debilitating condition worldwide, characterized by progressive loss of kidney function over time. Early detection plays a crucial role in mitigating its impact on patient health and healthcare systems. In recent years, there has been a burgeoning interest in leveraging machine learning (ML) and deep learning (DL) techniques to enhance the early diagnosis of CKD. This comprehensive review explores the advancements in ML and DL models applied to CKD diagnosis, focusing on their ability to integrate diverse data sources including clinical biomarkers, imaging modalities, and patient demographics. Key ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), and neural network architectures like Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) are examined in the context of their performance in predicting CKD progression, classifying disease stages, and identifying at-risk populations. Furthermore, the review discusses challenges such as data quality, model interpretability, and integration into clinical practice, alongside emerging trends in explainable AI, transfer learning, federated learning, and integration with electronic health records (EHRs). By synthesizing findings from recent literature, this paper aims to provide insights into current methodologies, identify gaps for future research, and underscore the transformative potential of ML and DL in revolutionizing early CKD diagnosis and management..

A Proposed Method of Gesture-controlled presentation software design

Nadia Mahmood Hussien, Yasmin Makki Mohialden , wurood A. jbara — Mon, 01 Apr 2024 00:00:00 +0000

This paper introduces an innovative method for developing a presentation application that empowers users to seamlessly control slide transitions and other essential actions through intuitive hand gestures. The approach integrates sophisticated computer vision algorithms capable of real-time gesture detection and interpretation from a standard webcam feed. Furthermore, machine learning techniques personalize the system to individual users' unique gestures, enhancing usability and accuracy. The proposed method is a groundbreaking innovation that seamlessly integrates with existing presentation tools. Furthermore, the research delves into cross-device synchronization, enabling a cohesive presentation experience. To ensure optimal usability and performance, we follow established software engineering principles, resulting in a user-friendly interface and an efficiently structured codebase. This paper comprehensively guides the design, implementation, and potential of this gesture-controlled presentation software.

A A Novel Method of Using Machine Learning Techniques to Protect Clouds Against Distributed Denial of Service (DDoS) Attacks.

SANGEETA DEVI, Pranjal Maury , Upendra Nath Tripathi — Thu, 15 Aug 2024 00:00:00 +0000

The term "cloud computing" describes a method of providing hardware- and software-based services over the internet. This allows users to access their data and apps from any device. The benefits of cloud computing include scalability, virtualization, access to user assets, lower infrastructure costs, and flexibility. However, one drawback is that it is susceptible to distributed denial of service attacks, which occur when multiple computer systems collaborate to target a particular resource, website, or server. Distributed denial of service (DDoS) attacks present a serious risk to computer networks and constitute a major cyber security challenge. This results in a denial of service for end users, as a result of false connection requests, a flood of messages, and twisted packets causing the system to slow down or even crash. Real people and services cannot access cloud computing. The issue of machine learning algorithms for identifying distributed denial of service (DDoS) attacks is explored in this article.

In order to identify and defend cloud systems from harmful assaults, we developed a new machine learning approach in this work that is based on transfer learning. This study offers NSL-KDD datasets and two methodologies. There are two types of filters available: the Learning Vector Quantization (LVQ) Filters and the Principal Component Analysis (PCA) method, which reduces dimensionality. The features selected from each method were pooled using Decision Tree (DT), Naïve Bayes (NB), and Support Vector Machine (SVM) to detect distributed denial of service attacks (DDoS). We contrasted the results of several classifications. In terms of attack detection, LVQ-based DT performed better results as compared to other methods.

A Short Review on Supervised Machine Learning and Deep Learning Techniques in Computer Vision

Sun, 11 Feb 2024 00:00:00 +0000

In last years, computer vision has shown important advances, mainly using the application of supervised machine learning (ML) and deep learning (DL) techniques. The objective of this review is to show a brief review of the current state of the field of supervised ML and DL techniques, especially on computer vision tasks. This study focuses on the main ideas, advantages, and applications of DL in computer vision and highlights their main concepts and advantages. This study showed the strengths, limitations, and effects of computer vision supervised ML and DL techniques.

Data Analysis of An Exploring the Information Systems Success Factors for Early Warning Systems Adoption

Thu, 20 Jun 2024 00:00:00 +0000

Naturally occurring floods are an essential part of life in many parts of the world. Floods, of all the natural dangers, have the greatest effect on society because they cover large geographic regions, happen often, and have a lasting negative socioeconomic impact. Thus, it becomes necessary to design a comprehensive and successful strategy for preventing floods, which will require technical advancements to improve the operational efficacy of government organizations. The Flood Early Warning and Response System (FEWRS), which gives pertinent stakeholders fast information and practical reaction guidelines, emerges as a critical instrument in reducing the loss of lives and property. Unfortunately, current FEWRS frequently fall short of providing enough information on flood disasters, which reduces their ability to mitigate local-level effects and impedes attempts to save lives. Assessing the effectiveness of information systems (IS) within this particular setting is a noteworthy obstacle for scholars, professionals, and administrators. The objective of this research is to tackle this difficulty by exploring the factors that lead to the success of FEWRS. This involves incorporating risk knowledge and response capabilities into the standard IS success model. The present study employs the DeLone and McLean (D&M) models due to their efficaciousness in meeting the designated requirements that are essential for mitigating the impact of flooding disasters.

Green Building Techniques: Under The Umbrella of the Climate Framework Agreement

Wed, 10 Jan 2024 00:00:00 +0000

Various green building rating systems have been devised to assess the sustainability levels of buildings, offering a standardized approach to evaluate their environmental impact. However, adapting these existing methods to diverse regions requires addressing additional considerations, such as distinct climatic conditions and regional variations. This study delves into a comprehensive exploration of widely utilized environmental building assessment methodologies, including BREEAM, LEED, SB-Tool, CASBEE, GRIHA, and Eco-housing. A new building environmental assessment scheme tailored to the global landscape is needed due to limitations of existing assessment schemes. A framework based on principal component analysis is introduced to develop this new scheme. PCA applied to a dataset of many responses on building sustainability revealed nine key components, including site selection, environmental impact, building resources and re-use, building services and management, innovative construction techniques, environmental health and safety, mechanical systems, indoor air quality, and economic considerations. A framework for sustainable building development in world is proposed. The study provides insights for designers and developers in developing countries, offering a roadmap for achieving green development. The framework prioritizes key components for a nuanced evaluation of sustainability in building projects, contributing to the global discourse on environmentally responsible construction practices.

Enhancing Energy Efficiency With Smart Building Energy Management System Using Machine Learning and IOT

Tue, 11 Jun 2024 00:00:00 +0000

The energy management system designed on the networking platform has been interfaced with controller to control the electrical device using the Wireless communication has been used as the most reliable and efficient technology in short-range communication. In this method IoT-based energy management could significantly contribute to energy conservation of home appliances device. This model analyses an IoT-based smart energy meter that automatically tracks residential energy consumption using current and voltage sensors. Input values senses unit that detects and controls the electrical devices used for daily actions. The ESP32 is used due to its built-in Wi-Fi facility, allowing data collection and exchange from electronic hardware to a cloud platform. The virtual android app displays the value of voltage, current, power, and unit consumed on a mobile screen, enhancing the efficiency of the system. The developed coding system to enhance system performance and provide more accurate results and ESP32 controller to interface non-invasive CT and voltage sensors, delivering data to a Blynk server over the internet. Model show the system accurately records voltage, current, dynamic power, and increasing power consumption and outcome accordingly, the home concerned person can turn ON/OFF the device based on such information if customer based user information.

Object Detection Using Capsule Neural Network: An Overview

Eman Turki Mahdi, Shokhan M. Al-Barzinji , Waleed Kareem Awad — Wed, 02 Oct 2024 00:00:00 +0000

Appropriate remedies for tasks like language translation, object identification, object segmentation, picture recognition, and natural language processing are needed for today's computer vision tasks. This paper explores the use of Capsule neural Networks (Caps Nets) in object identification and offers a thorough analysis of their developments and uses. It presents an analysis of the transformative impact of Caps Nets on object identification tasks. This distinctive the architecture of neural network is presented as an alternative to convolutional neural networks (CNNs). The paper highlights that how Caps Nets are unique in capturing spatial correlations and hierarchical patterns in visualization data by analyzing the fundamental ideas of the technology. Additionally, it demonstrates how Caps net out perform CNNs in terms of generalization, interpretability, and last in the resistance to spatial distortions, confirming their goodness at object detection. By integrating the Caps networks with the latest scientific findings and advances, the paper shows the current status and potential future paths for object detection methods that use this leading neural network architecture.