Robotic Searching for Stationary, Unknown and Transient Radio Sources

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments

Privacy of encrypted Voice Over Internet Protocol

In this research, we present a investigative study on how timing-based traffic analysis attacks can be used for recovery of the speech from a Voice Over Internet Protocol (VOIP) conversation by taking advantage of the reduction or suppression of the generation of traffic whenever the sender detects a voice inactivity period. We use the simple Bayesian classifier and the complex HMM (Hidden Markov Models) classier to evaluate the performance of our attack. Then we describe the usage of acoustic features in our attack to improve the performance. We conclude by presenting a number of problems that need in-depth study in order to be effective in carrying out silence detection based attacks on VOIP systems

Sound Archive: Acadia National Park

The purpose of this project was to collect audio recordings of Acadia National Park. With these recordings, the team designed an archive that makes the recordings accessible to the general public. The audio recordings include important sounds that are signature to Acadia. These “sound marks” illustrate the unique attributes of the park to the general public. This project is of importance to groups that do not have direct access to the park. Researchers and artists who are looking for sound clips to capture and display the history of Acadia can find use in this archive. It will preserve and present the natural sounds and environment of Acadia National Park for many generations to come

Timely processing of big data in collaborative large-scale distributed systems

Today’s Big Data phenomenon, characterized by huge volumes of data produced at very high rates by heterogeneous and geographically dispersed sources, is fostering the employment of large-scale distributed systems in order to leverage parallelism, fault tolerance and locality awareness with the aim of delivering suitable performances. Among the several areas where Big Data is gaining increasing significance, the protection of Critical Infrastructure is one of the most strategic since it impacts on the stability and safety of entire countries. Intrusion detection mechanisms can benefit a lot from novel Big Data technologies because these allow to exploit much more information in order to sharpen the accuracy of threats discovery. A key aspect for increasing even more the amount of data at disposal for detection purposes is the collaboration (meant as information sharing) among distinct actors that share the common goal of maximizing the chances to recognize malicious activities earlier. Indeed, if an agreement can be found to share their data, they all have the possibility to definitely improve their cyber defenses. The abstraction of Semantic Room (SR) allows interested parties to form trusted and contractually regulated federations, the Semantic Rooms, for the sake of secure information sharing and processing. Another crucial point for the effectiveness of cyber protection mechanisms is the timeliness of the detection, because the sooner a threat is identified, the faster proper countermeasures can be put in place so as to confine any damage. Within this context, the contributions reported in this thesis are threefold * As a case study to show how collaboration can enhance the efficacy of security tools, we developed a novel algorithm for the detection of stealthy port scans, named R-SYN (Ranked SYN port scan detection). We implemented it in three distinct technologies, all of them integrated within an SR-compliant architecture that allows for collaboration through information sharing: (i) in a centralized Complex Event Processing (CEP) engine (Esper), (ii) in a framework for distributed event processing (Storm) and (iii) in Agilis, a novel platform for batch-oriented processing which leverages the Hadoop framework and a RAM-based storage for fast data access. Regardless of the employed technology, all the evaluations have shown that increasing the number of participants (that is, increasing the amount of input data at disposal), allows to improve the detection accuracy. The experiments made clear that a distributed approach allows for lower detection latency and for keeping up with higher input throughput, compared with a centralized one. * Distributing the computation over a set of physical nodes introduces the issue of improving the way available resources are assigned to the elaboration tasks to execute, with the aim of minimizing the time the computation takes to complete. We investigated this aspect in Storm by developing two distinct scheduling algorithms, both aimed at decreasing the average elaboration time of the single input event by decreasing the inter-node traffic. Experimental evaluations showed that these two algorithms can improve the performance up to 30%. * Computations in online processing platforms (like Esper and Storm) are run continuously, and the need of refining running computations or adding new computations, together with the need to cope with the variability of the input, requires the possibility to adapt the resource allocation at runtime, which entails a set of additional problems. Among them, the most relevant concern how to cope with incoming data and processing state while the topology is being reconfigured, and the issue of temporary reduced performance. At this aim, we also explored the alternative approach of running the computation periodically on batches of input data: although it involves a performance penalty on the elaboration latency, it allows to eliminate the great complexity of dynamic reconfigurations. We chose Hadoop as batch-oriented processing framework and we developed some strategies specific for dealing with computations based on time windows, which are very likely to be used for pattern recognition purposes, like in the case of intrusion detection. Our evaluations provided a comparison of these strategies and made evident the kind of performance that this approach can provide

Numerical Analysis for Relevant Features in Intrusion Detection (NARFid)

Identification of cyber attacks and network services is a robust field of study in the machine learning community. Less effort has been focused on understanding the domain space of real network data in identifying important features for cyber attack and network service classification. Motivations for such work allow for anomaly detection systems with less requirements on data “sniffed” off the network, extraction of features from the traffic, reduced learning time of algorithms, and ideally increased classification performance of anomalous behavior. This thesis evaluates the usefulness of a good feature subset for the general classification task of identifying cyber attacks and network services. The generality of the selected features elucidates the relevance or irrelevance of the feature set for the classification task of intrusion detection. Additionally, the thesis provides an extension to the Bhattacharyya method, which selects features by means of inter-class separability (Bhattacharyya coefficient). The extension for multiple class problems selects a minimal set of features with the best separability across all class pairs. Several feature selection algorithms (e.g., accuracy rate with genetic algorithm, RELIEF-F, GRLVQI, median Bhattacharyya and minimum surface Bhattacharyya methods) create feature subsets that describe the decision boundary for intrusion detection problems. The selected feature subsets maintain or improve the classification performance for at least three out of the four anomaly detectors (i.e., classifiers) under test. The feature subsets, which illustrate generality for the intrusion detection problem, range in size from 12 to 27 features. The original feature set consists of 248 features. Of the feature subsets demonstrating generality, the extension to the Bhattacharyya method generates the second smallest feature subset. This thesis quantitatively demonstrates that a relatively small feature set may be used for intrusion detection with machine learning classifiers

Robotic Searching for Stationary, Unknown and Transient Radio Sources

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments

Autism Spectrum Disorder Policymaking in New Mexico: An Ethnographic Case Study

ABSTRACT Understanding how ASD policy is made at the state level is important to the various institutional and individual stakeholders who make, apply, and are governed by it. Critical disability theory was applied to this qualitative study of ASD policymaking in New Mexico. This study examined how policymakers and stakeholders brought their identities, knowledge, values, and beliefs to policymaking in New Mexico. The study was guided by the question, “How is ASD policy in New Mexico constructed?” The research used the following methods: (a) individual interviews of policy stakeholders, (b) observations of public policy meetings, (c) document review. Six major themes emerged: Tension in the Discursive Field, Dividing Practices, Reifying Autism, The Use of Force, The Government of Autism, and Autism Tsunami Policy Paradigm Shift. Analysis also uncovered related sub-themes. The study findings addressed interactions among governmentalities, discourses, violence, and resistance that, together with outside influences, may produce a paradigm shift in ASD policy in New Mexico

Suspect Until Proven Guilty, a Problematization of State Dossier Systems via Two Case Studies: The United States and China

This dissertation problematizes the state dossier system (SDS): the production and accumulation of personal information on citizen subjects exceeding the reasonable bounds of risk management. SDS - comprising interconnecting subsystems of records and identification - damage individual autonomy and self-determination, impacting not only human rights, but also the viability of the social system. The research, a hybrid of case-study and cross-national comparison, was guided in part by a theoretical model of four primary SDS driving forces: technology, political economy, law and public sentiment. Data sources included government documents, academic texts, investigative journalism, NGO reports and industry white papers. The primary analytical instrument was the juxtaposition of two individual cases: the U.S. and China. Research found that constraints on the extent of the U.S. SDS today may not be significantly different from China\u27s, a system undergoing significant change amidst growing public interest in privacy and anonymity. Much activity within the U.S., such as the practice of suspicious activity reporting, is taking place outside the domain of federal privacy laws, while ID systems appear to advance and expand despite clear public opposition. Momentum for increasingly comprehensive SDS appears to be growing, in part because the harms may not be immediately evident to the data subjects. The future of SDS globally will depend on an informed and active public; law and policy will need to adjust to better regulate the production and storage of personal information. To that end, the dissertation offers a general model and linguistic toolkit for the further analysis of SDS

Attacks on self-driving cars and their countermeasures : a survey

Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-Attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-Attack. We also provide further research directions to improve the security issues associated with self-driving cars. © 2013 IEEE