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Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system's security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively. [ABSTRACT FROM AUTHOR]

With the increasing growth in the network and latest technologies by which people communicates via voice or data and modifies the radio devices easily and cost effectively. Software defined radio brings the flexibility, power and efficiency including cloud and big data, control and management of the traditional networks has raised the challenges for the development of multimedia applications. Multimedia applications require to handle the large amount of data at the servers which has increased the load on them. To resolve this issue, Software Defined Networking (SDN) came into existence which makes the management of the network more conformable. To satisfy the constraints of Quality of Service (QoS) and Quality of Experience (QoE) with the limited network availability, one of the keynotes that have been taken into consideration is the load balancing. Therefore, many servers can be used with the load balancers which behave as the front end. The present paper aims to reflect impact on the efficiency of the usage of software-defined networks service in various multimedia applications. A genetic load balancing algorithm (GLBA) is proposed and is implemented on POX controller with mininet emulator in python language to compute its effectiveness and efficiency. Validation of GLBA for 100 to 600 users over server load, weighted round robin, round robin, dynamic server and LBBSRT algorithms with parameters, throughput, response time, memory and CPU utilization has proved the significance of proposed algorithm. [ABSTRACT FROM AUTHOR]