The Internet of Things (IoT) is currently seeing tremendous growth due to new technologies and big data . Research in the field of IoT security is an emerging topic. IoT networks are becoming more vulnerable to new assaults as a result of the growth in devices and the production of massive data. In order to recognize the attacks, an intrusion detection system is required. In this work, we suggested a Deep Learning (DL) model for intrusion detection to categorize various attacks in the dataset. We used a filter-based approach to pick out the most important aspects and limit the number of features, and we built two different deep-learning models for intrusion detection . For model training and testing, we used two publicly accessible datasets, NSL-KDD and UNSW-NB 15. First, we applied the dataset on the Deep neural network (DNN) model and then the same dataset on Convolution Neural Network (CNN) model. For both datasets, the DL model had a better accuracy rate. Because DL models are opaque and challenging to comprehend, we applied the idea of explainable Artificial Intelligence (AI) to provide a model explanation. To increase confidence in the DNN model, we applied the explainable AI (XAI) Local Interpretable Model-agnostic Explanations (LIME ) method, and for better understanding, we also applied Shapley Additive Explanations (SHAP).

The paper examines the complex structure of quan-tum computing and outlines its key elements, including qubits, quantum gates, superposition, and entanglement. Our inves-tigation goes beyond standard analysis, providing a singular synthesis of quantum theoretical foundations with cutting-edge applications like Brain Computer Interface (BCI) technology. We highlight fresh connections between emerging disciplines like quantum machine learning and quantum image processing. Through rigorous examination and innovative methodology, this paper contributes not only an advanced understanding of the fundamentals of quantum computing but also serves as a pioneering compass, directing future research in aligning quantum computational prowess with contemporary technological challenges.

As cloud computing continues to evolve, the security of cloud-based systems remains a paramount concern. This research paper delves into the intricate realm of intrusion detection systems (IDS) within cloud environments, shedding light on their diverse types, associated challenges, and inherent limitations. In parallel, the study dissects the realm of Explainable AI (XAI), unveiling its conceptual essence and its transformative role in illuminating the inner workings of complex AI models. Amidst the dynamic landscape of cybersecurity, this paper unravels the synergistic potential of fusing XAI with intrusion detection, accentuating how XAI can enrich transparency and interpretability in the decision-making processes of AI-driven IDS. The exploration of XAI's promises extends to its capacity to mitigate contemporary challenges faced by traditional IDS, particularly in reducing false positives and false negatives. By fostering an understanding of these challenges and their ram-ifications' this study elucidates the path forward in enhancing cloud-based security mechanisms. Ultimately, the culmination of insights reinforces the imperative role of Explainable AI in fortifying intrusion detection systems, paving the way for a more robust and comprehensible cybersecurity landscape in the cloud.

Software Defined Network (SDN) has changed the perception we used to have towards computer communication. As number of devices grow in the network, management of the inter device communications become a daunting task. SDN offers an easier approach to manage the large scaling networks. Control plane has the global vision of the routing devices. Any kind of network misbehavior can be easily traced and responded in real time. SDN does not come without its challenges. Its architecture exposes many surfaces vulnerable to attacks. Controller being the central device to control the network is the most lucrative point for the attackers. This paper shows how controller as well as data plane are vulnerable to Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) attacks. Although various solutions have been proposed by researchers to detect and mitigate Distributed Denial of Service (DDoS) attacks, but these solutions are limited in applications and scope. We will perform Low-Rate DDoS (LR-DDoS) attacks on controller to eat up the controller resources. Such low-density attacks are effective and most difficult to detect because of their low rate and distributed in nature. We will also focus on how to detect these attacks and possible solutions to mitigate the attacks.