The regular and continuous request for organic products in the market has increased the need for increase in organic farming. Many of the organic farmers are also small scale and most organic farming can be grown at a small level in homes through Home gardening. The pest infestation in the plants can be prevented and avoided with the usage of organic pesticides. Management of pests in home gardening is an important task. Organic pesticides are eco-friendly and can be applied easily with minimum cost. Organic pesticides can be either homemade or made naturally. The pesticide mixture is a combination of neem, garlic, pepper, milk, and salt solution and all the solutions are kept in separate containers. The management of pesticide includes the accurate amount of pesticide and the time of applying the pesticide and if done automatically, this management system reduces a lot of human effort. An internet of things (IoT) based module for pesticide management automatically controls the valves of the organic pesticide containers. All the data such as environmental conditions, infection in plants at particular seasons and their specific pesticide mixture are to be stored in a database. Prevention of the infections caused by the pathogens and other entities can be effectively done by automatic pesticide dispensing system. This paper analyses the existing systems for organic farming and proposes a framework using an android based application with an in-built mobile module for providing automatic pesticide dispensing.

Phishing is a treacherous effort to steal private data from users like address, aadhar number, PAN card details, credit/debit card details, bank account details, password for online shopping sites, etc. Pinching or phishing of private information on the web has caused havoc on a majority of users due to the lack of internet security. Phishing attacks make use of fake emails or websites, intended to fool users into revealing personal or financial information by posing as the trusted bank/shopping site. The various types of phishing attacks and the recent approaches to prevent the attacks are discussed. A framework to detect and prevent phishing attacks is also proposed. A combination of supervised and unsupervised machine learning techniques is used to detect known and unknown attacks.

The usage of IoT in the medical field, often referred to as IoMT, plays a vital role in facilitating the exchange of sensitive data among medical devices. This capability significantly contributes to enhancing the quality of patient care. However, it comes with privacy issues that compromise the security of the data collected by medical sensors, making them vulnerable to potential cyber threats such as data modification, replay attacks, etc. These attacks can lead to significant data loss or unauthorized alterations. Machine learning, particularly in cyber-attack detection systems, is crucial for identifying and classifying such attacks. Yet, the main challenge lies in adapting to the dynamic and unpredictable nature of malicious attacks and creating scalable solutions to combat them. The objective of this paper is to detect cybersecurity threats, with a particular focus on man-in-the-middle attacks that occur within the IoMT communication network. The study utilizes principal component analysis (PCA) for feature reduction and employs multi-layer perceptron to classify unforeseen cyber-attack IoT-based healthcare devices. The study evaluates the effectiveness of this proposed strategy using real-time data from the St. Louis Enhanced Healthcare Monitoring System (WUSTL-EHMS). The findings indicate that the multi-layer perceptron outperforms other tested classifiers, achieving an accuracy score of 96.39%, while also improving the performance by reducing the time complexity.

Decentralization in every walk of life has resulted in the development of Global Grid networking. Data sharing and access depends on their availability, capability, cost and user requirements. One of the needs for a secure Grid Environment is a strong authentication for users. Since Authentication is the entry point into every network, a novel smart card based authentication scheme has been proposed. The proposed authentication scheme utilizes the biometric data embedded in a smart card along with the ID and password of the user. The Time efficient performance of the proposed scheme in comparison with the existing Secure Socket Layer based authentication scheme is discussed. The attacks which the proposed scheme is able to withstand are also discussed.

The emerging 5G network has been revolutionizing the data transfer across the networks. Recently, advanced technologies transform the healthcare industry. With the emergence of 5G technology, smart and ubiquitous healthcare systems are interconnected with high speed and low latency. As ubiquitous healthcare applications require stringent network requirements, managing network traffic and resources becomes difficult. Internet of Medical Things (IoMT) devices generates a large volume and variety of data, including physiological signals, images, and videos. As many medical applications require real-time communication and data transfer, this puts high demands on the network, which needs handle low latency, high availability, and high reliability. Achieving consistent QoS is challenging due to the inherent limitations of conventional methods which are susceptible to network congestion and latency issues. The next generation of Software Defined Networking (SDN) in IoMT can play a vital role in supporting traffic management. Reliable classification and forecasting will enable the SDN controller to make optimal routing decisions dynamically. Therefore in this paper, a novel Medical Traffic Forecasting framework based on Weighted Multivariate Singular Spectrum Analysis (MTF-WMSSA) is proposed for an SDN-enabled IoMT healthcare network to analyze and forecast network traffic and ensure accurate real-time medical data transfer. The evaluation was conducted on the EHMS dataset to study the performance of the proposed method. The comparison of the proposed MTF-WMSSA is made with classical methods such as SVR and LSTM. The result shows that the proposed MTF-WMSSA exhibits improved classification accuracy of 93%. The network parameters that are forecasted are average traffic load, average packet arrival rate and average jitter. The MAPE of the multi-step ahead forecast is 13.64 and 11.41 for short and long intervals respectively. The forecasting algorithm proposed in this paper can efficiently determine future flow parameters which help to implement adaptive traffic engineering. The evaluation was conducted on the dataset to study the performance of the proposed method. The result shows that the proposed MTF-WMSSA exhibits better accuracy for multi-step ahead forecast and outperforms the classical methods.

A Tenant Virtual Machine (TVM) user in the cloud may misuse its computing power to launch malware attack against other tenant VMs, Host OS, Hypervisor, or any other computing devices/resources inside the cloud environment of a Cloud Service Provider. The security solutions deployed within the TVM may not be reliable, as malware can disable them or remain undetected due to its hidden nature. Therefore, security solutions deployed outside the virtual machine are necessary. This research proposes deploying an Intrusion Detection System (IDS) at the Hypervisor layer, utilizing time series system call data and employing a Convolutional Neural Network (CNN) model to accurately detect the presence of malicious (malware) computer programs within virtual machines. The raw VMM system call traces are transformed into novel Time Series System Call patterns and utilized by a deep learning algorithm for training and building the classifier model. A deep learning model, CNN, is used to build the classifier model for detecting intrusions with high accuracy. It is capable of detecting both known and unknown malware. The CNN model is compared with machine learning algorithms for the results and discussions, and it outperforms ML algorithms in terms of intrusion detection accuracy when utilizing novel time series system call data..