VPN Usage in Higher Education: A Study to Mitigate Risk Related to Public Wi-Fi Usage

Higher education settings have unique security risks in that they traditionally do not have robust security programs. Higher education network infrastructure can be outdated and resources for information security limited. These challenges are met with frequent faculty and staff travel for conferences and recruiting events in other states and countries. Frequent travel can leave university and faculty information and assets at risk; particularly when faculty and staff use public Wi-Fi in areas such as hotels, conference venues, coffee shops, and airports. Data in higher education institutions can be leaked when sent over channels that are not secure. This emergent research explores virtual private network (VPN) usage in a university setting using employer-issued devices. Emphasis is placed on VPN usage when using public Wi-Fi. The research focuses on the importance of risk to the university and its researchers as an influence on VPN usage behavior

Sensitivity Study of a Class of Fuzzy Control Systems

The paper performs the sensitivity study with respect to the parametric variations of the controlled plant in case of a class of fuzzy control systems dedicated to servo systems. The presentation is particularized to fuzzy control systems to solve the tracking control problem in case of wheeled mobile robots of tricycle type with two degrees of freedom. There is proposed a new development method for Takagi-Sugeno PI-fuzzy controllers based on the application of the Extended Symmetrical Optimum method to the basic linear PI controllers in a cascaded control system structure. There are derived sensitivity models, validated by considering a case study concerning the speed control of a servo system with DC motor as actuator in mobile robot control. Experimental results validate the development method for Takagi-Sugeno PI-fuzzy controllers

Q-LEARNING, POLICY ITERATION AND ACTOR-CRITIC REINFORCEMENT LEARNING COMBINED WITH METAHEURISTIC ALGORITHMS IN SERVO SYSTEM CONTROL

This paper carries out the performance analysis of three control system structures and approaches, which combine Reinforcement Learning (RL) and Metaheuristic Algorithms (MAs) as representative optimization algorithms. In the first approach, the Gravitational Search Algorithm (GSA) is employed to initialize the parameters (weights and biases) of the Neural Networks (NNs) involved in Deep Q-Learning by replacing the traditional way of initializing the NNs based on random generated values. In the second approach, the Grey Wolf Optimizer (GWO) algorithm is employed to train the policy NN in Policy Iteration RL-based control. In the third approach, the GWO algorithm is employed as a critic in an Actor-Critic framework, and used to evaluate the performance of the actor NN. The goal of this paper is to analyze all three RL-based control approaches, aiming to determine which one represents the best fit for solving the proposed control optimization problem. The performance analysis is based on non-parametric statistical tests conducted on the data obtained from real-time experimental results specific to nonlinear servo system position control

Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

This paper proposes two applications of Grey Wolf Optimizer (GWO) algorithms to a path planning (PaPl) problem and a Proportional-Integral (PI)-fuzzy controller tuning problem. Both optimization problems solved by GWO algorithms are explained in detail. An off-line GWO-based PaPl approach for Nonholonomic Wheeled Mobile Robots (NWMRs) in static environments is proposed. Once the PaPl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a GWO-based approach to tune cost-effective PI-fuzzy controllers in tracking control problem for NWMRs. The experimental results are demonstrated through simple multiagent settings conducted on the nRobotic platform developed at the Politehnica University of Timisoara, Romania, and they prove both the effectiveness of the two GWO-based approaches and major performance improvement

A way of decrypting particular malware payloads found in MZPE files

Back in the 90s when the notion of malware first appeared, it was clear that the behaviour and purpose of such software should be closely analysed, such that systems all over the world should be patched, secured and ready to prevent other malicious activities to be happening in the future. Thus, malware analysis was born. In recent years, the rise of malware of all types, for example trojan, ransowmare, adware, spyware and so on, implies that deeper understanding of operating systems, attention to the details and perseverance are just some of the traits any malware analyst should have in their bag. With Windows being the worldwide go-to operating system, Windows\u27 executable files represent the perfect way in which malware can be disguised to later be loaded and produce damage. In this paper we highlight how ciphers like Vigen\`ere cipher or Caesar cipher can be extended to more complex classes, such that, when later broken, ways of decrypting malware payloads, that are disguised in Windows executable files, are found. Alongside the theoretical information present in this paper, based on a dataset provided by our team at Bitdefender, we describe our implementation on how the key to decryption of such payloads can be found, what techniques are present in our approach, how optimization can be done, what are the pitfalls of this implementation and, lastly, open a discussion on how to tackle these pitfalls

RESULTS AND CHALLENGES OF ARTIFICIAL NEURAL NETWORKS USED FOR DECISION-MAKING AND CONTROL IN MEDICAL APPLICATIONS

The aim of this paper is to present several approaches by which technology can assist medical decision-making. This is an essential, but also a difficult activity, which implies a large number of medical and technical aspects. But, more important, it involves humans: on the one hand, the patient, who has a medical problem and who requires the best solution; on the other hand, the physician, who should be able to provide, in any circumstances, a decision or a prediction regarding the current and the future medical status of the patient. The technology, in general, and particularly the Artificial Intelligence (AI) tools could help both of them, and it is assisted by appropriate theory regarding modeling tools. One of the most powerful mechanisms that can be used in this field is the Artificial Neural Networks (ANNs). This paper presents some of the results obtained by the Process Control group of the Politehnica University Timisoara, Romania, in the field of ANNs applied to modeling, prediction and decision-making related to medical systems. An Iterative Learning Control-based approach to batch training a feedforward ANN architecture is given. The paper includes authors’ concerns in this domain and emphasizes that these intelligent models, even if they are artificial, are able to make decisions, being useful tools for prevention, early detection and personalized healthcare

DISCRETE-TIME MODEL-BASED SLIDING MODE CONTROLLERS FOR TOWER CRANE SYSTEMS

This paper applies three classical and very popular discrete-time model-based sliding mode controllers, namely the Furuta controller, the Gao controller, and the quasi-relay controller due to Milosavljević, to the position control of tower crane systems. Three single input-single output (SISO) control systems are considered, for cart position control, arm angular position control and payload position control, and separate SISO controllers are designed in each control system. Experimental results are included to support the comparison of the three plus three plus three sliding mode controllers

Data-Driven Model-Free Sliding Mode and Fuzzy Control with Experimental Validation

The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are built upon a model-free intelligent Proportional-Integral (iPI) control system structure, where an augmented control signal is inserted in the iPI control law to deal with the error dynamics in terms of sliding mode control. The data-driven model-free fuzzy control structure is developed by fuzzifying the PI component of the continuous-time iPI control law. The design approaches of the data-driven model-free control algorithms are offered. The data-driven model-free control algorithms are validated as controllers by real-time experiments conducted on 3D crane system laboratory equipment