Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Gestión de la actividad no presencial y autoevaluación de competencias = Management of student learning outside the classroom and the self-evaluation of competences

La asignatura troncal “Evaluación Psicológica” de los estudios de Psicología y del estudio de grado “Desarrollo humano en la sociedad de la información” de la Universidad de Girona consta de 12 créditos según la Ley Orgánica de Universidades. Hasta el año académico 2004-05 el trabajo no presencial del alumno consistía en la realización de una evaluación psicológica que se entregaba por escrito a final de curso y de la cual el estudiante obtenía una calificación y revisión si se solicitaba. En el camino hacia el Espacio Europeo de Educación Superior, esta asignatura consta de 9 créditos que equivalen a un total de 255 horas de trabajo presencial y no presencial del estudiante. En los años académicos 2005-06 y 2006-07 se ha creado una guía de trabajo para la gestión de la actividad no presencial con el objetivo de alcanzar aprendizajes a nivel de aplicación y solución de problemas/pensamiento crítico (Bloom, 1975) siguiendo las recomendaciones de la Agencia para la Calidad del Sistema Universitario de Cataluña (2005). La guía incorpora: los objetivos de aprendizaje, los criterios de evaluación, la descripción de las actividades, el cronograma semanal de trabajos para todo el curso, la especificación de las tutorías programadas para la revisión de los diversos pasos del proceso de evaluación psicológica y el uso del foro para el conocimiento, análisis y crítica constructiva de las evaluaciones realizadas por los compañeros

Learning objects management and evaluation in an e-learning environment. 'Objetos de gesti??n de aprendizaje y evaluaci??n en un entorno de e-learning'

Monogr??fico con el t??tulo: 'Estado actual de los sistemas e-learning'. Resumen basado en el de la publicaci??n

Power Electronics course: Analysis and evaluation of the educational software and the environment learning

This paper presents the analysis and evaluation of the Power Electronics course at So Paulo State University-UNESP-Campus of Ilha Solteira(SP)-Brazil, which includes the usage of interactive Java simulations tools and an educational software to aid the teaching of power electronic converters. This platform serves as an oriented course for the lectures and supplementary support for laboratory experiments in the power electronics courses. The simulation tools provide an interactive and dynamic way to visualize the power electronics converters behavior together with the educational software, which contemplates the theory and a list of subjects for circuit simulations. In order to verify the performance and the effectiveness of the proposed interactive educational platform, it is presented a statistical analysis considering the last three years. © 2011 IEEE.

Learning strategies evaluation in students with attention deficit hyperactivity disorder

This study aim to verify the use of learning strategies in students of the elementary level presenting interdisciplinary diagnosis of attention dei cit hyperactivity disorder (ADHD). Nine students, male gender, attending 3rd to 9th grade level of the elementary level, average age 10 years and 7 months, presenting interdisciplinary diagnosis of attention dei cit hyperactivity disorder (ADHD). h e students were submitted to the application of the Evaluation of Learning Strategies from elementary level – EAVAP-EF – scale, which aimed to evaluate the strategies reported and used by students in situation of study and learning, as follows: cognitive strategies, metacognitive strategies and absence of dysfunctional metacognitive strategies. h e general result at EAVAP-EF scale, showed that students with ADHD reached the percentile 25%, considered as low performance in the use of the learning strategies. For the variable absence of dysfunctional metacognitive strategies, the students presented percentile 30%, percentile 25% for cognitive strategies and 55% for metacognitive strategies. h e results showed that ADHD students do not use ef ectively the learning cognitive and metacognitive strategies and present the use of dysfunctional metacognitive strategies. h ese alterations match with the framework of ADHD because the entry of information, either visual or auditory, showed alterations, derived from inattention, which af ected the learning in classroom situation.

Auditory evoked potential: a proposal for further evaluation in children with learning disabilities

The information presented in this paper demonstrates the author's experience in previews cross-sectional studies conducted in Brazil, in comparison with the current literature. Over the last 10 years, auditory evoked potential (AEP) has been used in children with learning disabilities. This method is critical to analyze the quality of the processing in time and indicates the specific neural demands and circuits of the sensorial and cognitive process in this clinical population. Some studies with children with dyslexia and learning disabilities were shown here to illustrate the use of AEP in this population.

The mini-clinical evaluation exercise during medical clerkships: are learning needs and learning goals aligned?

OBJECTIVES The generation of learning goals (LGs) that are aligned with learning needs (LNs) is one of the main purposes of formative workplace-based assessment. In this study, we aimed to analyse how often trainer–student pairs identified corresponding LNs in mini-clinical evaluation exercise (mini-CEX) encounters and to what degree these LNs aligned with recorded LGs, taking into account the social environment (e.g. clinic size) in which the mini-CEX was conducted. METHODS Retrospective analyses of adapted mini-CEX forms (trainers’ and students’ assessments) completed by all Year 4 medical students during clerkships were performed. Learning needs were defined by the lowest score(s) assigned to one or more of the mini-CEX domains. Learning goals were categorised qualitatively according to their correspondence with the six mini-CEX domains (e.g. history taking, professionalism). Following descriptive analyses of LNs and LGs, multi-level logistic regression models were used to predict LGs by identified LNs and social context variables. RESULTS A total of 512 trainers and 165 students conducted 1783 mini-CEXs (98% completion rate). Concordantly, trainer–student pairs most often identified LNs in the domains of ‘clinical reasoning’ (23% of 1167 complete forms), ‘organisation/efficiency’ (20%) and ‘physical examination’ (20%). At least one ‘defined’ LG was noted on 313 student forms (18% of 1710). Of the 446 LGs noted in total, the most frequently noted were ‘physical examination’ (49%) and ‘history taking’ (21%). Corresponding LNs as well as social context factors (e.g. clinic size) were found to be predictors of these LGs. CONCLUSIONS Although trainer–student pairs often agreed in the LNs they identified, many assessments did not result in aligned LGs. The sparseness of LGs, their dependency on social context and their partial non-alignment with students’ LNs raise questions about how the full potential of the mini-CEX as not only a ‘diagnostic’ but also an ‘educational’ tool can be exploited.