Numerous applications in human-computer interaction, healthcare, and other fields have been made possible by the growth of mobile devices, which has created new opportunities for real-time emotion recognition. This research introduces a novel method for mobile device-based deep facial expression analysis for real-time emotion recognition. In order to execute accurate and efficient emotion recognition directly on the device, our technology makes use of the computational capacity of contemporary smartphones. This eliminates the need for cloud-based processing and ensures user privacy. For mobile platforms, we use a deep learning architecture that is optimised for both speed and accuracy. A lightweight convolutional neural network (CNN) for facial feature extraction and a recurrent neural network (RNN) for temporal emotion modelling are the main elements of our system. Our system accurately detects and categorises a variety of emotions, including joy, sadness, rage, surprise, and more by processing video frames from the device's camera feed in real-time. We carried out extensive trials on a variety of datasets to assess our methodology, attaining state-of-the-art accuracy with minimal processing cost. Through a variety of applications, including emotion-aware virtual assistants, mental health tracking tools, and immersive gaming experiences, we show how useful our technology is. This paper makes a contribution to the burgeoning field of mobile-based emotion recognition by providing a strong and effective solution that enables researchers and developers to produce ground-breaking software that can better comprehend and react to human emotions while protecting data privacy and guaranteeing real-time performance on mobile devices.

Online platforms' integrity and security are seriously threatened by the growth of automated bot attacks, necessitating the development of effective methods for telling harmful bots apart from legitimate users. In order to successfully combat automated bot attacks, this project investigates the creation and application of CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) methods utilising deep learning techniques. In this study, we employ deep learning to create and apply complex CAPTCHA-problems that help us distinguish between real users and automated bots. Convolutional neural networks and recurrent neural networks are employed to build dynamic, adjustable CAPTCHAs in order to keep up with the bots' evolving approaches. Our research focuses on creating CAPTCHAs that are simple to use for humans but difficult to understand by robots, which creates a favourable user experience for those who are actually human. Character skewing, background noise injection, and image obfuscation are some of the methods we use to safeguard our CAPTCHAs while maintaining their usability. Furthermore, we carry out exhaustive trials in the real world to assess the effectiveness of our methods based on in-depth CAPTCHA learning. We evaluate their resistance to a variety of attack methods, such as counterattacks and machine learning-based bot attacks, in order to confirm that they are resilient. The findings of our study show that utilising deep learning in CAPTCHA methods to thwart automated bot attacks is both feasible and effective. Our method makes the internet environment safer and more user-friendly while also enhancing security and reducing user annoyance. This work is an important step in the fight against the growing menace of automated bot attacks in the digital sphere.

The need for robust and efficient Distributed Denial of Service (DDoS) attack detection methods has become increasingly evident in today's networked world. Existing approaches, while commendable, often exhibit limitations that hinder their effectiveness. This paper introduces an innovative approach that leverages Local Outlier Factor (LOF) in conjunction with Principal Component Analysis (PCA) for unsupervised DDoS attack detection. Existing methods often fall short in addressing the evolving nature of DDoS attacks, struggling to maintain precision, accuracy, and recall rates. Our proposed model addresses these limitations by harnessing the power of LOF and PCA, offering a more adaptive and dynamic detection framework. LOF enables the identification of outliers in network traffic patterns, while PCA reduces the dimensionality of the data, enhancing the model's efficiency and speed. The advantages of our approach are manifold. It not only achieves superior performance in terms of precision, accuracy, recall, speed, and Area Under the ROC Curve (AUC) when compared to existing methods but also ensures a higher level of adaptability in detecting emerging DDoS attack vectors & sets. This adaptability is critical in today's ever-evolving threat landscape. The impact of this work extends beyond the realm of academia. In practical terms, our model offers network administrators a potent tool for safeguarding their infrastructures against DDoS attacks. By improving detection rates and reducing false positives, it contributes to the overall security posture of networks, ensuring uninterrupted services and enhanced user experience levels.

In cybersecurity, protecting the digital boundaries is very important to avoid data leaks and illegal access. In order to keep data safe, intrusion detection systems (IDS) are very important for keeping an eye on things and finding strange behavior. This paper suggests a new way to make IDS better at protecting the digital border. Propsoed method uses complex encryption algorithms to make sure that data is safer within the digital borders. When you protect private data, it stays safe even if someone breaks into your network and gets to the data without the decoding key. In addition, our system uses machine learning techniques to constantly look at network traffic trends and find strange things that could mean an attack. First, it gives you tracking and reports in real time, so you can quickly deal with security risks. The second reason is that encryption protects the privacy, security, and validity of data. Third, adding machine learning improves the accuracy of IDS by lowering the number of false alarms and finding new risks. The paper discuss strong data protection in cybersecurity, the digital boundaries must be protected with advanced encryption methods and IDS that are based on machine learning. This method improves network security, keeps private data safe, and lowers the risk of hacking.

Using predictive analytics is a key part of making the computer supply chain safer because it helps companies find and stop threats before they happen. Predictive analytics uses complex algorithms and machine learning to look through huge amounts of data for trends and outliers that could point to cyber dangers. Businesses can stay ahead of cyber attackers and keep their digital assets safe with this method. In the online supply chain, one of the best things about prediction analytics is that it can find new threats before they become full-blown attacks. Predictive analytics looks at past data, current trends, and other factors to be able to guess possible computer dangers and weak spots. In turn, this lets businesses take strategic steps to lower these risks, like fixing security holes or adding more protections. Predictive analytics can also improve the cyber supply chain's ability to respond to incidents. Predictive analytics looks at data from many places, like network logs, endpoint devices, and security monitors, to help find and rank possible security events. This lets companies react quickly and effectively to cyberattacks to lessen their effects. The prediction analytics is a great way to make the online supply chain safer. Using advanced algorithms and machine learning, businesses can find and stop cyber dangers before they happen, keep their digital assets safe, and boost their total security.