Developing an Algorithm for Securing the Biometric Data Template in the Database

This research article published by the International Journal of Advanced Computer Science and Applications, Vol. 10, No. 10, 2019In the current technology advancement, biometric template provides a dependable solution to the problem of user verification in an identity control system. The template is saved in the database during the enrollment and compared with query information in the verification stage. Serious security and privacy concerns can arise, if raw, unprotected data template is saved in the database. An attacker can hack the template information in the database to gain illicit access. A novel approach of encryption-decryption algorithm utilizing a design pattern of Model View Template (MVT) is developed to secure the biometric data template. The model manages information logically, the view shows the visualization of the data, and the template addresses the data migration into pattern object. The established algorithm is based on the cryptographic module of the Fernet key instance. The Fernet keys are combined to generate a multiFernet key to produce two encrypted files (byte and text file). These files are incorporated with Twilio message and securely preserved in the database. In the event where an attacker tries to access the biometric data template in the database, the system alerts the user and stops the attacker from unauthorized access, and cross-verify the impersonator based on the validation of the ownership. Thus, helps inform the users and the authority of, how secure the individual biometric data template is, and provided a high level of the security pertaining the individual data privac

Design and Analysis of Decoy Systems for Computer Security

This dissertation is aimed at defending against a range of internal threats, including eaves-dropping on network taps, placement of malware to capture sensitive information, and general insider threats to exfiltrate sensitive information. Although the threats and adversaries may vary, in each context where a system is threatened, decoys can be used to deny critical information to adversaries making it harder for them to achieve their target goal. The approach leverages deception and the use of decoy technologies to deceive adversaries and trap nefarious acts. This dissertation proposes a novel set of properties for decoys to serve as design goals in the development of decoy-based infrastructures. To demonstrate their applicability, we designed and prototyped network and host-based decoy systems. These systems are used to evaluate the hypothesis that network and host decoys can be used to detect inside attackers and malware. We introduce a novel, large-scale automated creation and management system for deploying decoys. Decoys may be created in various forms including bogus documents with embedded beacons, credentials for various web and email accounts, and bogus financial in- formation that is monitored for misuse. The decoy management system supplies decoys for the network and host-based decoy systems. We conjecture that the utility of the decoys depends on the believability of the bogus information; we demonstrate the believability through experimentation with human judges. For the network decoys, we developed a novel trap-based architecture for enterprise networks that detects "silent" attackers who are eavesdropping network traffic. The primary contributions of this system is the ease of injecting, automatically, large amounts of believable bait, and the integration of various detection mechanisms in the back-end. We demonstrate our methodology in a prototype platform that uses our decoy injection API to dynamically create and dispense network traps on a subset of our campus wireless network. We present results of a user study that demonstrates the believability of our automatically generated decoy traffic. We present results from a statistical and information theoretic analysis to show the believability of the traffic when automated tools are used. For host-based decoys, we introduce BotSwindler, a novel host-based bait injection sys- tem designed to delude and detect crimeware by forcing it to reveal itself during the ex- ploitation of monitored information. Our implementation of BotSwindler relies upon an out-of-host software agent to drive user-like interactions in a virtual machine, seeking to convince malware residing within the guest OS that it has captured legitimate credentials. To aid in the accuracy and realism of the simulations, we introduce a novel, low overhead approach, called virtual machine verification, for verifying whether the guest OS is in one of a predefined set of states. We provide empirical evidence to show that BotSwindler can be used to induce malware into performing observable actions and demonstrate how this approach is superior to that used in other tools. We present results from a user to study to illustrate the believability of the simulations and show that financial bait infor- mation can be used to effectively detect compromises through experimentation with real credential-collecting malware. We present results from a statistical and information theo- retic analysis to show the believability of simulated keystrokes when automated tools are used to distinguish them. Finally, we introduce and demonstrate an expanded role for decoys in educating users and measuring organizational security through experiments with approximately 4000 university students and staff

Emerging Technologies

This monograph investigates a multitude of emerging technologies including 3D printing, 5G, blockchain, and many more to assess their potential for use to further humanity’s shared goal of sustainable development. Through case studies detailing how these technologies are already being used at companies worldwide, author Sinan Küfeoğlu explores how emerging technologies can be used to enhance progress toward each of the seventeen United Nations Sustainable Development Goals and to guarantee economic growth even in the face of challenges such as climate change. To assemble this book, the author explored the business models of 650 companies in order to demonstrate how innovations can be converted into value to support sustainable development. To ensure practical application, only technologies currently on the market and in use actual companies were investigated. This volume will be of great use to academics, policymakers, innovators at the forefront of green business, and anyone else who is interested in novel and innovative business models and how they could help to achieve the Sustainable Development Goals. This is an open access book

Journal of Telecommunications and Information Technology, 2017, nr 4

kwartalni

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

Tematski zbornik radova međunarodnog značaja. Tom 3 / Međunarodni naučni skup “Dani Arčibalda Rajsa”, Beograd, 3-4. mart 2015.

In front of you is the Thematic Collection of Papers presented at the International Scientific Confer-ence “Archibald Reiss Days”, which was organized by the Academy of Criminalistic and Police Studies in Belgrade, in co-operation with the Ministry of Interior and the Ministry of Education, Science and Techno-logical Development of the Republic of Serbia, National Police University of China, Lviv State University of Internal Affairs, Volgograd Academy of the Russian Internal Affairs Ministry, Faculty of Security in Skopje, Faculty of Criminal Justice and Security in Ljubljana, Police Academy “Alexandru Ioan Cuza“ in Bucharest, Academy of Police Force in Bratislava and Police College in Banjaluka, and held at the Academy of Crimi-nalistic and Police Studies, on 3 and 4 March 2015.International Scientific Conference “Archibald Reiss Days” is organized for the fifth time in a row, in memory of the founder and director of the first modern higher police school in Serbia, Rodolphe Archibald Reiss, PhD, after whom the Conference was named.The Thematic Collection of Papers contains 168 papers written by eminent scholars in the field of law, security, criminalistics, police studies, forensics, informatics, as well as members of national security system participating in education of the police, army and other security services from Spain, Russia, Ukraine, Bela-rus, China, Poland, Armenia, Portugal, Turkey, Austria, Slovakia, Hungary, Slovenia, Macedonia, Croatia, Montenegro, Bosnia and Herzegovina, Republic of Srpska and Serbia. Each paper has been reviewed by two reviewers, international experts competent for the field to which the paper is related, and the Thematic Conference Proceedings in whole has been reviewed by five competent international reviewers.The papers published in the Thematic Collection of Papers contain the overview of contemporary trends in the development of police education system, development of the police and contemporary secu-rity, criminalistic and forensic concepts. Furthermore, they provide us with the analysis of the rule of law activities in crime suppression, situation and trends in the above-mentioned fields, as well as suggestions on how to systematically deal with these issues. The Collection of Papers represents a significant contribution to the existing fund of scientific and expert knowledge in the field of criminalistic, security, penal and legal theory and practice. Publication of this Collection contributes to improving of mutual cooperation between educational, scientific and expert institutions at national, regional and international level

2021 Student Symposium Research and Creative Activity Book of Abstracts

The UMaine Student Symposium (UMSS) is an annual event that celebrates undergraduate and graduate student research and creative work. Students from a variety of disciplines present their achievements with video presentations. It’s the ideal occasion for the community to see how UMaine students’ work impacts locally – and beyond. The 2021 Student Symposium Research and Creative Activity Book of Abstracts includes a complete list of student presenters as well as abstracts related to their works

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications