Cloud computing services provide a wide range of resource pool for maintaining a large amount of data. Cloud services are commonly used as the private or public data forum based on the demand, and the increase in usage has lead to security concerns. The information in the cloud comes under threat due to hackers, and the most common attack on the cloud data is considered as the Distributed Denial of Service (DDoS) attack. This work has concentrated on detecting the DDoS attack by developing the deep learning-based classifier. The service request from the users is collected and grouped as the log information. From the log file, some important features are selected for the classification using the Bhattacharya distance measure to reduce the training time of the classifier. Here, Taylor-Elephant Herd Optimisation based Deep Belief Network (TEHO-DBN), is developed by modifying the Elephant Herd Optimisation (EHO) with the Taylor series and the algorithm thus developed is adopted to train the Deep Belief Network (DBN) for the DDoS attack detection. From the simulation results, it can be concluded that the proposed TEHO based DBN classifier has improved performance with a maximum accuracy of 0.830.

The present trend of automation anddata interchange in industrial technology is known as Industry 4.0. Industry 4.0 is altering its next generation of distribution networks by making them more responsive and efficient. There are various issues when implementing Internet of Thing (IoT) devices in an Industry 4.0 scenario, mainly due to reduced IoT nodes or devices with insufficient infrastructure to operate security solutions. As a result, securing the environment will require a lightweight and effective security solution. The authors of this study offer a lightweight and flexible authentication method as part of an access control strategy for secure data exchange in-vehicle networks. Lightweight operations, hash functions, XOR operations, and concatenation are all used in the proposed protocol. The security of this approach is also evaluated using AVISPA. The authentication process between IoT networks and resilience to various security threats will be demonstrated. The experimental analysis shows that the computational cost attained is 7.96 s, and the communication cost attained is 834 bits. Similarly, the resource utilization for this computation is 11%, and the average verification time is 7 ms. The experimental analysis demonstrates that the proposed method is efficient with lower computational cost, communication costs, verification cost, and resource utilization than the existing state-of-the-art approaches

Blockchain is picking up footing and one of the omnipresent themes these days can be named. Despite the fact that faultfinders are challenging its durability, safety and stability, it has officially changed the way of life of numerous people in a few territories due to its overwhelming impact on projects, moreover, organizations. Giving that the highlights of blockchain innovation ensure more solid and convenient administrations, it is imperative to think about the security furthermore, protection concerns and complications behind the inventive innovation. The range of blockchain applications extends to open and public services including health, financial, automotive, Internet of Things (IoT) and risk management. Many talk about using the blockchain data system for various applications in the spotlight. Nonetheless, a comprehensive review on the point of view of specialized applications and applications has not yet been an expert. In this paper, we seek to perform a thorough study on the technology of blockchain by analyzing its architecture of varying consensus algorithms over and above challenges and opportunities for security and data protection within blockchains.

Nepal, a lower-middle-income country in South Asia, predominantly features smallholder farming communities operating on modest land holdings. These smallholders often adhere to traditional farming methods, relying on familial labor, which has become increasingly inefficient in contemporary agricultural landscapes. To enhance their productivity and efficiency, smallholder farmers require affordable and accessible Internet of Things (IoT)-based systems. However, the prevailing IoT solutions in the market primarily cater to large-scale commercial enterprises, rendering them unsuitable for the specific needs and constraints faced by smallholder farmers. In response to this gap, we have introduced a cost-effective, customizable, scalable, and dependable IoT platform tailored expressly for smallholder farmers. This platform empowers them to visualize, monitor, and control real-time data pertaining to their crops, livestock, and other agricultural assets. To ascertain the efficacy and suitability of our proposed platform, we conducted a comparative analysis with existing counterparts such as Blynk IoT and ThingSpeak IoT, evaluating their respective features and application services against standard requirements. Additionally, we subjected our platform to rigorous server load testing, assessing crucial performance parameters including throughput, response time, user capacity, and data sampling rates. Over an observation period spanning an average of 339 days, our platform successfully processed and stored a substantial volume of data, encompassing 817,633 sensor messages, averaging 2412 messages per day, with a cumulative storage size of 14,238.28 KB. Extrapolating from these results, it is noteworthy that an A0 instance with 20 GB of cloud space can adequately accommodate 200 users at a rate of 100 MB per user, which is adequate for the smallholder needs. Furthermore, the purposed platform was deployed inside a polyhouse to perform off-season grafting of citrus plants. The achieved success rate of 84% closely approached the success rate of 90–95% observed during on-season grafting. These empirical findings, coupled with the extensive data gathered during our research, underscore the reliability and performance of our proposed IoT platform for smallholder farmers.