Detecting botnet and malware cyber-attacks is a critical task in ensuring the security of computer networks. Traditional methods for identifying such attacks often involve static rules and signatures, which can be easily evaded by attackers. Dl is a subdivision of ML, has shown promise in enhancing the accuracy of detecting botnets and malware by analyzing large amounts of network traffic data and identifying patterns that are difficult to detect with traditional methods. In order to identify abnormal traffic patterns that can be a sign of botnet or malware activity, deep learning models can be taught to learn the intricate interactions and correlations between various network traffic parameters, such as packet size, time intervals, and protocol headers. The models can also be trained to detect anomalies in network traffic, which could indicate the presence of unknown malware. The threat of malware and botnet assaults has increased in frequency with the growth of the IoT. In this research, we offer a unique LSTM and GAN-based method for identifying such attacks. We utilise our model to categorise incoming traffic as either benign or malicious using a dataset of network traffic data from various IoT devices. Our findings show how well our method works by attaining high accuracy in identifying botnet and malware cyberattacks in IoT networks. This study makes a contribution to the creation of stronger and more effective security systems for shielding IoT devices from online dangers. One of the major advantages of using deep learning for botnet and malware detection is its ability to adapt to new and previously unknown attack patterns, making it a useful tool in the fight against constantly evolving cyber threats. However, DL models require large quantity of labeled data for training, and their performance can be affected by the quality and quantity of the data used. Deep learning holds great potential for improving the accuracy and effectiveness of botnet and malware detection, and its continued development and application could lead to significant advancements in the field of cybersecurity.

The commercial adoption of artificial intelligence in business analytics tools across multiple industries is being driven by the rising volume and complexity of company data. Business organizations are being helped by the widespread application of artificial intelligence and machine learning in business intelligence to glean meaningful insights from sizable and complicated datasets and provide business recommendations that are clear to any business user. Within the industry of information technology, the business analytics is used to refer the usage of computing to gain the insights from data. Such data can be acquired from the internal sources of company like from its enterprise resource planning application, warehouse and mart data, providers of third-party data, or from other public sources. Sample of 198 respondents from different business sectors were to know the role, application and impact of artificial intelligence in business analytics. It is found that there is a significant role of artificial intelligence in business analytics. This paper provides a rigorous examination of the literature in an effort to illustrate the value-creating procedures and to elucidate how organizations may employ AI technologies in their operations. In this study, the forms of AI use in the organizational setting, first- and second-order impacts, and usage typologies are highlighted along with the significant enablers and inhibitors of AI adoption and use. Our analysis synthesizes the current literature.

Timely detection and treatment are crucial in managing brain tumors, a severe medical condition. MRI is a commonly used diagnostic tool to detect brain tumors. However, because of the complex structure of the brain and the wide range of tumors sizes and forms, MRI scan interpretation can be time-consuming and error-prone. The automated detection and segmentation of brain tumors has shown encouraging results with to recent developments in DL techniques. We suggest a CNN-RNNs and GANs based DL technique for brain tumor identification in this paper. Transfer learning and data augmentation techniques are used in the suggested method to train the CNN on a sizable dataset of MRI images labelled with tumor areas. The suggested strategy, according to experimental findings, is more accurate than the most advanced techniques now available for finding brain tumors. The suggested strategy has the potential to help radiologists identify brain tumors quickly and reliably, improving patient outcomes.

Multiscale adaptive object detection is a powerful computer vision technique that holds great potential for customer behavior analysis in various domains. By accurately detecting and tracking objects of interest, such as customers or products, at different scales, this approach enables detailed analysis of customer behavior. It allows businesses to track customer movements, interactions with products, and dwell times, providing valuable insights into shopping patterns and preferences. The application of multiscale adaptive object detection in customer behavior analysis offers businesses the opportunity to optimize store layouts, product placements, and marketing strategies, leading to enhanced customer experiences and improved business performance. In this paper, we introduce an innovative technique for object detection that leverages contrastive feature learning to augment the efficacy of multiscale object detection. Our methodology incorporates a contrastive loss function to extract discriminative features that exhibit resilience to scale and perspective disparities. This empowers our model to precisely detect objects across a broad range of sizes and viewpoints, even in arduous scenarios encompassing partial occlusion or low contrast against the background. Through comprehensive experiments conducted on benchmark datasets, we demonstrate that our approach surpasses state-of-the-art methodologies in terms of both accuracy and efficiency.

This research work is conducted to make the analysis of digital technology is one of the most admired and effective technologies that has been applied in the global context for faster data management. Starting from business management to connectivity, everywhere the application of IoT and digital technology is undeniable. Besides the advancement of the data management, cyber security is also important to prevent the data stealing or accessing from the unauthorized data. In this context the IoT security technology focusing on the safeguarding the IoT devices connected with internet. Different technologies are taken under the consideration for developing the IoT based cyber security such as Device authentication, Secure on boarding, data encryption and creation of the bootstrap server. All of these technologies are effective to its ground for protecting the digital data. In order to prevent cyber threats and hacking activities like SQL injection, Phishing, and DoS, this research paper has proposed a newer technique of the encryption process by using the python codes and also shown the difference between typical conventional system and proposed system for understanding both the system in a better way.

Phishing attacks have become one of the most prominent cyber threats in recent times, which poses a significant risk to the security of organizations and individuals. Therefore, detecting such Cyber attacks has become crucial to ensure a secure digital environment. In this regard, deep learning techniques have shown promising results for the detection of phishing attacks due to their ability to learn and extract features from raw data. In this study, we propose a deep learning-based approach to detecting phishing attacks by using a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our proposed model extracts features from the URL and email content to detect phishing attempts. We evaluate the proposed approach on a real-world dataset and achieve an accuracy of over 95%. The results indicate that the proposed approach can effectively detect phishing attacks and can be utilized in real-world applications to ensure a secure digital environment.