In this current world, keep hearing about heart disease problems every day and about the deaths due to them, making heart disease a major contributor to the crucial mortality rate worldwide. According to the World Health Organization (WHO), an estimated 17.9 million individuals die from cardiovascular diseases (CVDs) each year. The identification of cardiovascular disease states, including cardiac arrhythmia and coronary heart disease, based on traditional clinical data analysis is still a big challenge. The early diagnosis of cardiac disease can enable timely medical treatment and save many lives. The use of machine learning (ML) algorithms enables intelligent decision-making and accurate disease prediction by identifying complex patterns in healthcare data. This study adopted the following preprocessing methods for the UCI Heart Disease dataset: missing-value treatment, duplicate removal, noise reduction, one-hot encoding, Z-score normalization, and SMOTE data balancing. The proposed XGBoost model was developed for heart disease risk assessment and evaluated using accuracy, precision, recall, and F1-score. The proposed model achieved 99.8% accuracy, 99.7% precision, 99.9% recall, and 99.6% F1-score, demonstrating its effectiveness and reliability for accurate heart disease prediction.
Madhav Sharma· International Journal of Int...· 0 citations
New attacks are getting smarter and more sophisticated, so the old signature-based intrusion detection and prevention systems can't find them. This work proposes a machine learning approach to build a cybersecurity threat intelligence framework for effective multiclass intrusion detection, in which the Decision Tree classifier is used. The CICIDS2017 benchmark dataset, which contains both benign network traffic and several types of cyberattacks, is used to build and test the suggested model. The goal of the preparation process is to enhance classification performance by cleaning and separating data, utilizing Standard Scaler to scale features, and SMOTE to balance classes. Metrics like as recall, accuracy, precision, F1-score, confusion matrix, and ROC curve are used to test the Decision Tree model. An impressive 99.91% accuracy (ACC) rate, 97.79% precision (PRE), 97.08% recall (REC), 97.41% F1-score (F1), and 0.99 AUC were revealed by the experiment's outcomes. The suggested method beats state-of-the-art deep learning and ML approaches in terms of performance, execution time, and computational complexity. The results show that the suggested design is a reliable, efficient, and lightweight way to find cyber security threats and IDR apps with intelligence.
Madhav Sharma· International Journal of Cyb...· 0 citations
Security researchers rely heavily on Network Intrusion Detection Systems (NIDS) to keep an eye on network traffic and notify administrators of any suspicious activities. The purpose of this paper is to offer a comprehensive overview of intrusion detection systems (IDS), including the following topics: fundamentals, kinds of IDS, methods for detecting intrusions in NIDS, the architecture of IDS, data pre-processing, and examples of ML techniques used in NIDS. This covers several detection methods, including signature-based, anomaly-based, specification-based, and behavior-based approaches, as well as their advantages and disadvantages in recognizing both existing and new cyber threats. The review also covers the architecture of NIDS which consists of network sensors, preprocessors, network traffic analysis, alert generation and security analysis. A variety of ML techniques, including supervised, unsupervised, semi-supervised, ensemble, and deep learning (DL) approaches, are being explored to improve the accuracy and adaptability of intrusion detection systems (IDS). Other applications such as DoS/DDoS attack detection, Malware detection, Botnets, Brute force attacks, Insider compromise, IoT compromise and Critical infrastructure threats are also shown. Despite all the challenges in terms of false positives, scalability, computational complexity, data quality, and novel attack styles, the features that ML can provide for intelligent, adaptive, and accurate intrusion detection systems are appealing.
Madhav Sharma· International Journal of Cyb...· 0 citations
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