E-Learning is a sought-after option for learners during pandemic situations. In e-Learning platforms, there are many courses available, and the user needs to select the best option for them. Thus, recommender systems play an important role to provide better automation services to users in making course choices. It makes recommendations for users in selecting the desired option based on their preferences. This system can use machine intelligence (MI)-based techniques to carry out the recommendation mechanism. Based on the preferences and history, this system is able to know what the users like most. In this work, a recommender system is proposed using the collaborative filtering mechanism for e-Learning course recommendation. This work is focused on MI-based models such as K-nearest neighbor (KNN), Singular Value Decomposition (SVD) and neural network–based collaborative filtering (NCF) models. Here, one lakh of Coursera’s course review dataset is taken from Kaggle for analysis. The proposed work can help learners to select the e-Learning courses as per their preferences. This work is implemented using Python language. The performance of these models is evaluated using performance metrics such as hit rate (HR), average reciprocal hit ranking (ARHR) and mean absolute error (MAE). From the results, it is observed that KNN is able to perform better in terms of higher HR and ARHR and lower MAE values as compared to other models.

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.

Today, power systems have transformed considerably and taken a new shape of geographically distributed systems from the locally centralized systems thereby leading to a new infrastructure in the framework of networked control cyber-physical system (CPS). Among the different important operations to be performed for smooth generation, transmission, and distribution of power, maintaining the scheduled frequency, against any perturbations, is an important one. The load frequency control (LFC) operation actually governs this frequency regulation activity after the primary control. Due to CPS nature, the LFC operation is vulnerable to attacks, both from physical and cyber standpoints. The cyber-attack strategies ranges from a variety of attacks such as jamming the network communication, time-delay attack, and false data injection. Motivated by these perspectives, this paper studies the cybersecurity issues of the power systems during the LFC operation, and a survey is conducted on the security analysis of LFC. Various cyber-attack strategies, their mathematical models, and vulnerability assessments are performed to understand the possible threats and sources causing failure of frequency regulation. The LFC operation of two-area power systems is considered as a tutorial example to quantify the vulnerabilities. Mitigation strategies through control theoretic approaches are then reviewed and highlighted for LFC operation under cyber-attack.