Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users’ and systems’ sensitive information during the training and testing phases.

The banking and financial sector has always been a prime target for cyber threats due to the critical nature of the information they handle. With the increasing dependence on technology and digital transformation, the sector is facing more complex and sophisticated threats from cybercriminals. The banking sector serves as the backbone of a country’s economy, interlinked with various other sectors like petroleum, mining, health, and industry. Any significant damage to the banking sector can send shockwaves through the entire economic landscape. Therefore, cyber threat classifications play a crucial role in risk management and provide a valuable framework for understanding and responding to cyber threats. Understanding the potential impact of a cyber-attack helps organisations assess the risks they face and develop appropriate risk mitigation strategies. The purpose of this research paper is to provide a comprehensive analysis of cyber threats in the banking and financial sectors, including identifying common threats, their nature and character to help in classification. One of the significant contributions of this research paper is to classify cyber threats to the banking and financial sectors based on their severity and technicality. This classification helps to identify the appropriate countermeasures required to mitigate the risks of each type of threat. Furthermore, the paper explores the technical, non-technical, organizational countermeasures and the legal and regulatory measures used to protect financial transactions from cyber threats. This research work delves into the challenges and limitations of cyber threat classifications, focusing specifically on those confronting the banking and financial sector in their pursuit of robust cybersecurity. Additionally, it analyses recent trends and developments in the field, highlighting the evolving nature of cyber threats to banking. The most significant challenge is the rapidly evolving nature of cyber threats, making it challenging to keep up with the latest trends and technologies.

Diabetes is a common chronic illness or absence of sugar in the blood. The early detection of this disease decreases the serious risk factor. Nowadays, Machine Learning based cloud environment acts as a vital role in disease detection. The people who belong to the rural areas are not getting the proper health care treatments. So, this research work proposed an automated eHealth cloud system for detecting diabetes in the earlier stage to decrease the mortality rate and provides health treatment facilities to rural peoples. Extreme Learning Machine (ELM) is a type of Artificial Neural Network (ANN) that has a lot of potential for solving classification challenges. This research work is consisting of several activities like feature normalization, feature selection and classification. We have employed principal component analysis (PCA) for feature selection and extreme learning machine (ELM) for classification. Finally, a cloud computing-based environment with three numbers of virtual machines (vCPU-4, vCPU-8, and vCPU-16), is used for the detection of diabetes. The efficacy of the proposed model has been evaluated with the PIMA dataset in both standalone and cloud environments and achieved 90.57 % accuracy, 82.24 % sensitivity, 73.23 % specificity, and 75.03 % F-1 score with the virtual machine vCPU-16. The experimental results define the proposed model as superior to other state-of-art models with better classification accuracy and less number of features.

Ensuring the dependability of software before its public release is of utmost importance. Many software issues arise due to human errors made throughout the development process, highlighting the importance of addressing these errors early. It is crucial to incorporate testing resources at the beginning of development to minimize potential issues. Utilizing an approach that identifies modules susceptible to errors helps potential problems. With an understanding of the significance of precisely anticipating module failures, multiple automated solutions are already emerging. This work presents a refined software defect prediction model that utilizes a meta-heuristic optimization technique. Our methodology integrates NASA’s data collection procedure, which includes data cleansing, reducing the dimensionality of features, and predicting defects. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to decrease the feature dimensionality, while an Extreme Learning Machine (ELM) is utilized for forecasting defects. The ELM parameters, such as weight and biases, are ideally chosen using the suggested improved JAYA optimization (IMJAYA) method. The model’s validation involves assessing its accuracy, sensitivity, specificity, F1 score, and MCC metrics using a 10x5 cross-validation. The model is verified using NASA datasets that consist of several classes, such as CM1, KC2, KC3, MC1, PC1, and JM1. The PCA-LDA+ IMJAYA-ELM model yields defect prediction accuracies of 95.73%, 98.08%, 94.87%, 96.23%, 97.10%, and 97.46% for the CM1, KC2, KC3, MC1, PC1, and JM1 datasets, respectively. The research outcomes show encouraging outcomes when using a meta-heuristic optimization technique with smaller feature sets for studies on predicting software defects.

This study presents a novel Similarity-Based Hybrid API Malware Detection Model (HAPI-MDM) aiming to enhance the accuracy of malware detection by leveraging the combined strengths of static and dynamic analysis of API calls. Faced with the pervasive challenge of obfuscation techniques used by malware authors, the conventional detection models often struggle to maintain robust performance. Our proposed model addresses this issue by deploying a two-stage learning approach where the XGBoost algorithm acts as a feature extractor feeding into an Artificial Neural Network (ANN). The key innovation of HAPI-MDM is the similarity-based feature, which further enhances the detection accuracy of the dynamic analysis, ensuring reliable detection even in the presence of obfuscation. The model was evaluated using seven machine learning techniques with 10 K-fold cross-validation. Experimental results demonstrated HAPI-MDM’s superior performance, achieving an overall accuracy of 97.91% and the lowest false-positive and false-negative rates compared to related works. The findings suggest that integrating dynamic and static API-based features and utilizing a similarity-based feature significantly improves malware detection performance, thereby offering an effective tool to fortify cybersecurity measures against escalating malware threats.

A key challenge in developing Machine Learning (ML) models for predicting or diagnosing Cardiovascular Disease (CVD), is selecting suitable algorithms and fine-tuning their parameters. In this study, we employed three ML techniques, namely Auto-WEKA, Decision Table/Naive Bayes (DTNB), and Multiobjective Evolutionary (MOE) fuzzy classifier to create diagnostic models using the Heart Disease Dataset from IEEE Dataport. Auto-WEKA generated a highly accurate model with a 100% success rate through optimal classifier selection and hyperparameter configuration. The DTNB classifier yielded a satisfactory 85.63% prediction accuracy concerning patients' risk levels. Further refinements, though, could help reduce possible misclassifications. Finally, the MOE fuzzy classifier achieved approximately 81.6% accuracy, indicating the potential for enhancing precision and recall values by adjusting classifier settings. Our findings underscore the promise of ML tools in CVD diagnosis and suggest further optimization of classifier parameters for superior performance.