The main objective of this study is to conduct a comprehensive analysis of cyber risks in cloud computing, including classifying threats, vulnerabilities, impacts, and countermeasures. This classification helps to identify suitable security controls to mitigate cyber risks for each type of threat. Additionally, this study aims to explore the main vulnerabilities in terms of infrastructure, service and platform in cloud computing. This study uses the content analysis technique to collect, analyze, and classify data in terms of types of threats, vulnerabilities, and countermeasures. The methodology comprises four primary stages: (1) identifying key components, (2) threat identification, (3) vulnerability identification, and (4) countermeasure identification. The results indicate that DoS attacks and account hijacking attacks were the most prevalent infrastructure vulnerabilities in cloud computing, each accounting for 14% and 10% of incidents. The results found that unpatched software and weak access controls were classified as the most critical threats in the service level in cloud computing, comprising 17% and 12% of incidents, respectively. The results also indicated that encryption methods, access controls mechanisms and firewall malware protection are the most significant and effective countermeasures for protecting the infrastructure, service and platform in cloud computing environment. The findings of this study provides valuable recommendations for academic research in classifying the different types of cyber threats and understanding the significant security controls against cyber-attacks in cloud computing.

The rapid evolution of cyberattacks, and in particular DDoS attacks, has outpaced many intrusion detection systems (IDSs) that fail to be interpretable or transparent, which subsequently hinders wider adoption in real-world high-stake environments. To our knowledge, this is the first joint utilization of CNNs with both SHAP and LIME for XAI-IDS tasks, particularly based on comparative evaluations across a wide range of different intrusions and a combination of XAI methods. The proposed method starts with data acquisition and data preprocessing of NSL-KDD dataset as only DDoS labeled records output are chosen and this is followed by data cleaning through data normalization, feature selection and label encoding to get accurate results. The dataset is divided into training and testing sets, a 1D CNN is trained on the dataset to differentiate DDoS attacks from normal traffic using the hyperparameters with optimized values and early stopping. It yields high predictive performance with 94% accuracy, 93% precision, 95% recall, and 94% F1-score. For these results, it demonstrates strong classification ability with very few false positive and false negative. Stack SHAP for global feature importance and LIME for individual predictions to give us human-understandable explainable results of the model. Not only does this dual explainability cultivate trust and accountability, it also enhances auditing and compliance in sensitive industries. Overall, XAI-IDS shows that the combination of deep learning and post-hoc interpretability is a promising approach to the design of trustworthy systems for cybersecurity. Future works will be conducted toward real-time deployment and multi-class detection in federated and edge learning frameworks.

In this paper, a new representation of Farsi words is proposed to present the keyword spotting problems in Farsi document image retrieval. In this regard, we define a signature for each Farsi word based on the word connected component layout. The mentioned signature is shown as boxes, and then, by sketching vertical and horizontal lines, we construct a grid of each word to provide a new descriptor. One of the advantages of this method is that it can be used for both handwritten and machine-printed texts. Finally, to evaluate the performance of our system in comparison to other methods, a database that contains 19,582 printed Farsi words is examined, and after applying this approach, a recall rate of 98.1% and a precision rate of 94.3% are obtained.

Stock price prediction holds paramount significance for individual investors, guiding crucial decisions in financial planning and investment strategies. This research delves into the methodology of Monte Carlo simulation, a versatile tool in financial modeling, to assess its advantages and disadvantages in the context of predicting stock prices. The study employs Python code to demonstrate the step-by-step implementation of Monte Carlo simulations, emphasizing the mathematical optimization of parameters for enhanced accuracy. Results showcase a characteristic bell curve, offering a probabilistic perspective on potential outcomes. Comparative analyses with other forecasting models, such as graphic analysis, underscore the superior reliability of Monte Carlo simulation in evaluating risks and rewards. Furthermore, the paper explores the application of Monte Carlo simulation in real-world scenarios, such as portfolio optimization and retirement planning, highlighting its pragmatic value for individual investors navigating the complexities of the stock market. The research concludes by acknowledging the limitations of the approach and advocating for a comprehensive consideration of all relevant factors in financial decision-making. This exploration serves as a valuable resource for individual investors seeking informed insights into probabilistic forecasting methods for effective stock price predictions.