Towards Real-Time Anomaly Detection within X-ray Security Imagery: Self-Supervised Adversarial Training Approach

Automatic threat detection is an increasingly important area in X-ray security imaging since it is critical to aid screening operators to identify concealed threats. Due to the cluttered and occluded nature of X-ray baggage imagery and limited dataset availability, few studies in the literature have systematically evaluated the automated X-ray security screening. This thesis provides an exhaustive evaluation of the use of deep Convolutional Neural Networks (CNN) for the image classification and detection problems posed within the field. The use of transfer learning overcomes the limited availability of the object of interest data examples. A thorough evaluation reveals the superiority of the CNN features over conventional hand-crafted features. Further experimentation also demonstrates the capability of the supervised deep object detection techniques as object localization strategies within cluttered X-ray security imagery. By addressing the limitations of the current X-ray datasets such as annotation and class-imbalance, the thesis subsequently transitions the scope to- wards deep unsupervised techniques for the detection of anomalies based on the training on normal (benign) X-ray samples only. The proposed anomaly detection models within the thesis employ a conditional encoder-decoder generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space — minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution — an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches. Based on the current approaches and open problems in deep learning, the thesis finally provides discussion and future directions for X-ray security imagery

Air Force Institute of Technology Research Report 2007

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Tier 1 Highway Security Sensitive Material Dynamic Risk Management

Each year, over 2 billion tons of hazardous materials are shipped in the United States, with over half of that being moved on commercial vehicles. Given their relatively poor or nonexistent defenses and inconspicuousness, commercial vehicles transporting hazardous materials are an easy target for terrorists. Before carriers or security agencies recognize that something is amiss, their contents could be detonated or released. From 2006 to 2015, the U.S. Department of Transportation’s Pipeline and Hazardous Materials Safety Administration (PHMSA) recorded 144,643 incidents involving a release of hazardous materials. Although there were no known instances of terrorism being the cause, accidental releases involving trucks carrying hazardous materials are not an uncommon occurrence. At this time, no systems have been developed and operationalized to monitor the movement of vehicles transporting hazardous materials. The purpose of this dissertation is to propose a comprehensive risk management system for monitoring Tier 1 Highway Security Sensitive Materials (HSSMs) which are shipped aboard commercial vehicles in the U.S. Chapter 2 examines the history and current state of hazardous materials transportation. Since the late 19th century, the federal government often introduced new regulations in response to hazardous materials incidents. However, over the past 15 years few binding policies or legislation have been enacted. This demonstrates that government agencies and the U.S. Congress are not inclined to introduce new laws and rules that could hamper business. In 2003, the Federal Motor Carrier Safety Administration (FMCSA) and other agencies led efforts to develop a prototype hazardous materials tracking system (PHTS) that mapped the location of hazardous materials shipments and quantified the level of risk associated with each one. The second half of this chapter uses an in-­‐depth gap analysis to identify deficiencies and demonstrate in what areas the prototype system does not comply with government specifications. Chapter 3 addresses the lack of customized risk equations for Tier 1 HSSMs and develops a new set of risk equations that can be used to dynamically evaluate the level of risk associated with individual hazardous materials shipments. This chapter also discusses the results of a survey that was administered to public and private industry stakeholders. Its purpose was to understand the current state of hazardous materials regulations, the likelihood of hazardous materials release scenarios, what precautionary measures can be used, and what influence social variables may have on the aggregate consequences of a hazardous materials release. The risk equation developed in this paper takes into account the survey responses as well as those risk structures already in place. The overriding goal is to preserve analytical tractability, implement a form that is usable by federal agencies, and provide stakeholders with accurate information about the risk profiles of different vehicles. Due to congressional inaction on hazardous 3 materials transportation issues, securing support from carriers and other industry stakeholders is the most viable solution to bolstering hazardous materials security. Chapter 4 presents the system architecture for The Dynamic Hazardous Materials Risk Assessment Framework (DHMRA), a GIS-­‐based environment in which hazardous materials shipments can be monitored in real time. A case study is used to demonstrate the proposed risk equation; it simulates a hazardous materials shipment traveling from Ashland, Kentucky to Philadelphia, Pennsylvania. The DHMRA maps risk data, affording security personnel and other stakeholders the opportunity to evaluate how and why risk profiles vary across time and space. DHRMA’s geo-­‐fencing capabilities also trigger automatic warnings. This framework, once fully implemented, can inform more targeted policies to enhance the security of hazardous materials. It will contribute to maintaining secure and efficient supply chains while protecting the communities that live nearest to the most heavily trafficked routes. Continuously monitoring hazardous materials provides a viable way to understand the risks presented by a shipment at a given moment and enables better, more coordinated responses in the event of a release. Implementation of DHRMA will be challenging because it requires material and procedural changes that could disrupt agency operations or business practices — at least temporarily. Nevertheless, DHRMA stands ready for implementation, and to make the shipment of hazardous materials a more secure, safe, and certain process. Although DHMRA was designed primarily with terrorism in mind, it is also useful for examining the impacts of accidental hazardous materials releases. Future iterations of DHMRA could expand on its capabilities by incorporating modeling data on the release and dispersion of toxic gases, liquids, and other substances

The impact of the application of international air cargo security regulations in South Africa

Text in EnglishThis research project, within the context of security risk management in general and aviation security in particular, aimed to explore the impact of the application of international and local air cargo security regulations on South Africa, with specific reference to the regulations of the International Civil Aviation Organisation (ICAO), as well as the European Union (EU) and the United States of America (USA). In South Africa, since the early 2000s, the South African Civil Aviation Authority (SACAA) has been the lead agency for dealing with and managing the needs for air cargo security. This oversight by SACAA culminated in 2009 with the promulgation of the SACAA Regulation commonly known as Part 108. Accordingly the primary research focus was on the impact Part 108 has had on the air cargo industry in South Africa. In addition, it compared the South African regulations with those of the USA and EU regulations; explored the compliance of the various roleplayers; sought to understand the enforcement of the regulations; and examined the effectiveness of the available security and screening methods. Furthermore, the research attempted to determine whether these regulations had any effect on preventing or deterring crime in the air cargo sector.Criminology and Security ScienceM. Tech. (Security Management

Towards Managing and Understanding the Risk of Underwater Terrorism

This dissertation proposes a methodology to manage and understand the risk of underwater terrorism to critical infrastructures utilizing the parameters of the risk equation. Current methods frequently rely on statistical methods, which suffer from a lack of appropriate historical data to produce distributions and do not integrate epistemic uncertainty. Other methods rely on locating subject matter experts who can provide judgment and then undertaking an associated validation of these judgments. Using experimentation, data from unclassified successful, or near successful, underwater attacks are analyzed and instantiated as a network graph with the key characteristics of the risk of terrorism represented as nodes and the relationship between the key characteristics forming the edges. The values of the key characteristics, instantiated as the length of the edges, are defaulted to absolute uncertainty, the state where there is no information for, or against, a particular causal factor. To facilitate obtaining the value of the nodes, the Malice spectrum is formally defined which provides a dimensionless, methodology independent model to determine the value of any given parameter. The methodology produces a meta-model constructed from the relationships between the parameters of the risk equation, which determines a relative risk value

Compilation of Abstracts, December 2013

NPS Class of December 2013This publication, Compilation of Abstracts, contains abstracts of unrestricted dissertations, theses, and capstone project reports submitted for the doctor of philosophy, astronautical engineer, master of arts, master of business administration, and master of science degrees for the Naval Postgraduate School’s December 2013 graduating class.http://archive.org/details/compilationofabs109456086

Air Force Institute of Technology Research Report 2006

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Automated Analysis of X-ray Images for Cargo Security

Customs and border officers are overwhelmed by the hundreds of millions of cargo containers that constitute the backbone of the global supply chain, any one of which could contain a security- or customs-related threat. Searching for these threats is akin to searching for needles in an ever-growing field of haystacks. This thesis considers novel automated image analysis methods to automate or assist elements of cargo inspection. The four main contributions of this thesis are as follows. Methods are proposed for the measurement and correction of detector wobble in large-scale transmission radiography using Beam Position Detectors (BPDs). Wobble is estimated from BPD measurements using a Random Regression Forest (RRF) model, Bayesian fused with a prior estimate from an Auto-Regression (AR). Next, a series of image corrections are derived, and it is shown that 87% of image error due to wobble can be corrected. This is the first proposed method for correction of wobble in large-scale transmission radiography. A Threat Image Projection (TIP) framework is proposed, for training, probing and evaluating Automated Threat Detection (ATD) algorithms. The TIP method is validated experimentally, and a method is proposed to test whether algorithms can learn to exploit TIP artefacts. A system for Empty Container Verification (ECV) is proposed. The system, trained using TIP, is based on Random Forest (RF) classification of image patches according to fixed geometric features and container location. The method outperforms previous reported results, and is able to detect very small amounts of synthetically concealed smuggled contraband. Finally, a method for ATD is proposed, based on a deep Convolutional Neural Network (CNN), trained from scratch using TIP, and exploits the material information encoded within dual-energy X-ray images to suppress false alarms. The system offers a 100-fold improvement in the false positive rate over prior work