This paper describes the design, implementation, and results of an NSF funded Summer Academy from 2016 to 2018, which engaged, on an annual basis, 30 to 60 rising 10th and 11th grade high school science students in an innovative, technology-enriched Project Based Learning (PBL) environment. This Academy emphasized how tech gadgets work and the impact that technology can have on improving communities by immersing students in the exploration of one such device that is a growing phenomenon, the "aerial drone." In this Academy, the students learned various operations of the drone through Python programming language, and some cybersecurity issues and solutions. The student teams, under the guidance of diverse mentors, comprehensively fortified their STEM problem-solving skills and critical thinking. Both formative and summative evaluations for this Academy showed that it helped students improve their critical thinking ability and motivated them to pursue careers in STEM-related disciplines, specifically in information technology and cybersecurity areas.

In the present world, it is difficult to realize any computing application working on a standalone computing device without connecting it to the network. A large amount of data is transferred over the network from one device to another. As networking is expanding, security is becoming a major concern. Therefore, it has become important to maintain a high level of security to ensure that a safe and secure connection is established among the devices. An intrusion detection system (IDS) is therefore used to differentiate between the legitimate and illegitimate activities on the system. There are different techniques are used for detecting intrusions in the intrusion detection system. This paper presents the different clustering techniques that have been implemented by different researchers in their relevant articles. This survey was carried out on 30 papers and it presents what different datasets were used by different researchers and what evaluation metrics were used to evaluate the performance of IDS. This paper also highlights the pros and cons of each clustering technique used for IDS, which can be used as a basis for future work.

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Internet of Things (IoT) refers to heterogeneous systems and devices (often referred to as smart objects) that connect to the internet, and is an emerging and active area of research with tremendous technological, social, and economical value for a hyper-connected world. In this paper, we will discuss how billions of these internet connected devices and machines will change the future in which we shall live, communicate and do the business. The devices, which would be connected to the internet, could vary from simple systems on chip (SOC) without any Operating System (OS) to highly powerful processor with intelligent OS with widely varying processing capability and diverse protocol support. Many of these devices can also communicate with each other directly in a dynamic manner. A key challenge is: how to manage such a diverse set of devices of such massive scale in a secured and effective manner without breaching privacy. In this paper, we will discuss various management issues and challenges related to different communication protocol support and models, device management, security, privacy, scalability, availability and analytic support, etc., in managing IoT. The key contribution of this paper is proposal of a reference management system architecture based on cloud technology in addressing various issues related to management of IoThaving billions of smart objects.

Clustering is a process by which an object space is partitioned into different classes such that some optimization criteria are satisfied. The exact solution to wide variety of problems in clustering takes an exponential amount of time. Therefore researchers have developed several heuristics to solve the clustering problems. Some of these heuristics use stochastic techniques such as genetic algorithm, simulated annealing and simulated evolution to obtain near-optimal solutions with a reduced time complexity. In this paper, we present an algorithm involving a combination of genetic algorithm (GA) and simulated evolution. Each string in the GA's population is a solution state and consists of a number of clusters. The global clustering procedure is based on principles of evolution, while within each population the individuals are generated through a combination of genetic crossover operator and a further constructive step. Experimental evaluation shows that the proposed strategy produces better results than that of the best greeti heuristics known to us and has a potential for further improvement.