Accurate and efficient lung and colon cancer classification is vital for early detection and treatment planning. Traditional methods require manual effort and expert analysis, leading researchers to explore deep learning models. However, deep learning-based lung and colon cancer classification models face challenges such as generalization, overfitting, gradient vanishing, and hyperparameter tuning. To overcome these challenges, we propose an efficient Deep Attention module and a Residual block-based lung and colon cancer classification Network (DARNet). It comprises three key components such as residual blocks, attention modules, and fully connected layers. Residual blocks (RBs) are utilized to refine the DARNet's ability to learn and capture residual information which allows DARNet to perceive complex patterns and improve accuracy. Attention module (AM) enhances feature extraction and captures useful information in the input data. Finally, to achieve better generalization performance, we employ Bayesian Optimization (BO) to fine-tune the hyperparameters of DARNet. Extensive experimental results indicate that the proposed BO-based DARNet achieved superior performance over competitive models on benchmark lung and colon cancer datasets, with a median accuracy of 98.86% and lower variance.

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.

To provide security to all pairs of nodes in network mobility (NEMO) while executing the handoff between different technologies, a hybrid cryptosystem with a suitable network selection mechanism is proposed. All pairs of nodes, i.e., Mobile Node (MN), Mobile Router (MR), Correspondent Node (CN) and MN, and Home Agent (HA), respectively, are considered. A proper security mechanism is proposed to provide confidentiality to Bound Update (BU) during handoff and conversation between MN, MR, and HA using the elliptic curve cryptography (ECC). In this solution, a network selection mechanism is proposed based on user preference and Received Signal Strength (RSS) in a heterogeneous network. The proposed model can protect the communication using security analysis from all NEMO standard attacks. Whenever NEMO moves, MR intimates to HA about the address change using (BU) and MR receives Binding Acknowledgement (BA) as a reply. During data (frame) exchange and registration between MN, CN, and HA, various security threats arise. In the earlier work, only the security solution is given, and the best network selection algorithm is not provided in a heterogeneous environment. Therefore, in this paper, the best network selection is contributed based on Received Signal Strength (RSS) and user preferences. A comparison of the proposed model is drawn with Return Routability Procedure (RRP). Authentication is provided for communication between MN and CN. The proof is derived using BAN logic. Many standard security attacks have been successfully avoided on all pairs of communications. It has been observed that the proposed model achieves 2.4854% better throughput than the existing models. Also, the proposed model reduces the handoff latency and packet loss by 2.7482% and 3.8274%, respectively.

Proposed is a secure and efficient approach for designing and implementing an enterprise-class cryptographic file system for Linux (ECFS) in kernel-space. It uses stackable file system interface to introduce a layer for encrypting files using symmetric keys, and public-key cryptography for user authentication and file sharing, like other existing enterprise-class cryptographic file systems. It differs itself from existing systems by including all public-key cryptographic operations and public-key infrastructure (PKI) support in kernel-space that protects it from attacks that may take place with a user-space PKI support. It has a narrower domain of trust than existing systems. It uses XTS mode of AES algorithm for file encryption for providing better protection and performance. It also uses kernel-keyring service for improving performance. It stores the cryptographic metadata in file’s access control list (ACL) as extended attributes to ease the task of file sharing. A secure protocol has also been designed and implemented to guard against various possible attacks, when its files are accessed remotely over an untrusted network.