Enhancing service quality and reliability in intelligent traffic system

Intelligent Traffic Systems (ITS) can manage on-road traffic efficiently based on real-time traffic conditions, reduce delay at the intersections, and maintain the safety of the road users. However, emergency vehicles still struggle to meet their targeted response time, and an ITS is vulnerable to various types of attacks, including cyberattacks. To address these issues, in this dissertation, we introduce three techniques that enhance the service quality and reliability of an ITS. First, an innovative Emergency Vehicle Priority System (EVPS) is presented to assist an Emergency Vehicle (EV) in attending the incident place faster. Our proposed EVPS determines the proper priority codes of EV based on the type of incidents. After priority code generation, EVPS selects the number of traffic signals needed to be turned green considering the impact on other vehicles gathered in the relevant adjacent cells. Second, for improving reliability, an Intrusion Detection System for traffic signals is proposed for the first time, which leverages traffic and signal characteristics such as the flow rate, vehicle speed, and signal phase time. Shannon’s entropy is used to calculate the uncertainty associated with the likelihood of particular evidence and Dempster-Shafer (DS) decision theory is used to fuse the evidential information. Finally, to improve the reliability of a future ITS, we introduce a model that assesses the trust level of four major On-Board Units (OBU) of a self-driving car along with Global Positioning System (GPS) data and safety messages. Both subjective logic (DS theory) and CertainLogic are used to develop the theoretical underpinning for estimating the trust value of a self-driving car by fusing the trust value of four OBU components, GPS data and safety messages. For evaluation and validation purposes, a popular and widely used traffic simulation package, namely Simulation of Urban Mobility (SUMO), is used to develop the simulation platform using a real map of Melbourne CBD. The relevant historical real data taken from the VicRoads website were used to inject the traffic flow and density in the simulation model. We evaluated the performance of our proposed techniques considering different traffic and signal characteristics such as occupancy rate, flow rate, phase time, and vehicle speed under many realistic scenarios. The simulation result shows the potential efficacy of our proposed techniques for all selected scenarios.Doctor of Philosoph

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings

An examination of the Asus WL-HDD 2.5 as a nepenthes malware collector

The Linksys WRT54g has been used as a host for network forensics tools for instance Snort for a long period of time. Whilst large corporations are already utilising network forensic tools, this paper demonstrates that it is quite feasible for a non-security specialist to track and capture malicious network traffic. This paper introduces the Asus Wireless Hard disk as a replacement for the popular Linksys WRT54g. Firstly, the Linksys router will be introduced detailing some of the research that was undertaken on the device over the years amongst the security community. It then briefly discusses malicious software and the impact this may have for a home user. The paper then outlines the trivial steps in setting up Nepenthes 0.1.7 (a malware collector) for the Asus WL-HDD 2.5 according to the Nepenthes and tests the feasibility of running the malware collector on the selected device. The paper then concludes on discussing the limitations of the device when attempting to execute Nepenthes

Cross channel fraud detection framework in financial services using recurrent neural networks

The reliability and performance of real time fraud detection techniques has been a major concern for the financial institutions as traditional fraud detection models couldn’t cope with the emerging new and innovative attacks that deceive banks. The problems are further exacerbated with evolving customer behaviour as existing fraud detection models unable to cope with class imbalance problem and longer feedback loop. This thesis looks at the holistic view of fraud detection and proposes a conceptual fraud detection framework that can detect anomalous transaction quickly and accurately, as well as dynamically evolve to maintain the efficiency with minimum input from subject matter expert. The framework is used to analyse Internet Banking (IB) transactions and contextual information to reduce the false positives and improve fraud detection rates. Based on the proposed framework, Long Short-Term Memory (LSTM) based Recurrent Neural Network model for detecting fraud in remote banking is implemented and performance is evaluated against Support Vector Machine (SVM) and Markov models. The main research element is to model events as state vectors so that sequence-based learning can be applied, followed by a weak classifier to deal with noise. Firstly, the study focuses on Feature Engineering where along raw attributes such as IP Address, Amount and other, two novel features for remote banking fraud are evaluated, i.e., the time spend on a page and the time between page transition. The second focus is on modelling which is performed on an anonymised real-life dataset, provided by a large financial institution in Europe. The results of the modelling demonstrate that given the labelled dataset all models can detect payment fraud with acceptable accuracy. Various tests proved that the LSTM model achieves a F1 score of 97.7% whereas the SVM and Markov model achieve 93.5% and 95.0% respectively. As the time elapsed, the LSTM model performance significantly improves as the sequence of events became larger. As the dataset increases that time it takes to train traditional models becomes a bottleneck. This proves the hypothesis that the events across banking channels can be modelled as time series data and then sequence-based learners such as Recurrent Neural Network (RNN) can be applied to improve or reduce the False Positive Rate (FPR) and False Negative Rate (FNR)

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

Compilation of thesis abstracts, September 2009

NPS Class of September 2009This quarter’s Compilation of Abstracts summarizes cutting-edge, security-related research conducted by NPS students and presented as theses, dissertations, and capstone reports. Each expands knowledge in its field.http://archive.org/details/compilationofsis109452751

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well