SAMPCAN: A NOVEL CACHING TECHNIQUE FOR CLIENT-SERVER INTERACTION MODEL IN LARGE AD HOC NETWORKS USING RESAMPLING METHODS

ABSTRAC

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Cyber Security of Critical Infrastructures

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods

Simple and flexible random key pre-distribution schemes for wireless sensor networks using deployment knowledge

Sensor nodes are tiny, low-power and battery constrained electromechanical devices that are usually deployed for sensing some type of data in different types of areas. Because of their memory and computational restrictions, public key cryptography (PKC) systems are not suited for sensor nodes to provide security. Instead, private key cryptography is preferred to be used with sensor networks and there has been considerable work in this area, but there still exist problems with private key cryptography because of memory restrictions of sensor nodes. Number of keys that can be deployed into a sensor node is determined by the available memory of that node which is limited even private key cryptographic techniques are applied. So, new key distribution mechanisms are required to decrease number of pairwise keys that are deployed into a sensor node. Random key pre-distribution mechanisms have been proposed to overcome memory restrictions of sensor nodes. These mechanisms are widely accepted for sensor network security. Simply, these schemes try do decrease the number of keys to be deployed in each sensor node in a sensor network and provide reasonable security for the sensor network. Random key pre-distribution schemes proposed until now have some deficiencies. Some of these schemes are too complicated and too difficult to be applied. Schemes that seem deployable involve unrealistic assumptions when real world scenarios are considered. In this thesis, we propose random key pre-distribution mechanisms that are simple and easily deployable. In this thesis, we first developed a generalized random key pre-distribution scheme. Then we proposed three random key pre-distribution mechanisms based on this generalized scheme and we provided their simulation results and their comparison to well-known random key pre-distribution schemes in the literature. Our generalized scheme allows different systems to be derived according to deployment needs. It offers simple, easily deployable distribution mechanisms and provides reasonable connectivity and resiliency with respect to its simplicity

Proceedings

Proceedings of the NODALIDA 2009 workshop Nordic Perspectives on the CLARIN Infrastructure of Language Resources. Editors: Rickard Domeij, Kimmo Koskenniemi, Steven Krauwer, Bente Maegaard, Eiríkur Rögnvaldsson and Koenraad de Smedt. NEALT Proceedings Series, Vol. 5 (2009), v+45 pp. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9207