The Android Smartphone as an Inexpensive Sentry Ground Sensor

Proc. SPIE Conf. on Unattended Ground, Sea, and Air Sensor Technologies and Applications XIV, Baltimore, MD, April 2012A key challenge of sentry and monitoring duties is detection of approaching people in areas of little human traffic. We are exploring smartphones as easily available, easily portable, and less expensive alternatives to traditional military sensors for this task, where the sensors are already integrated into the package. We developed an application program for the Android smartphone that uses its sensors to detect people passing nearby; it takes their pictures for subsequent transmission to a central monitoring station. We experimented with the microphone, light sensor, vibration sensor, proximity sensor, orientation sensor, and magnetic sensor of the Android. We got best results with the microphone (looking for footsteps) and light sensor (looking for abrupt changes in light), and sometimes good results with the vibration sensor. We ran a variety of tests with subjects walking at various distances from the phone under different environmental conditions to measure limits on acceptable detection. We got best results by combining average loudness over a 200 millisecond period with a brightness threshold adjusted to the background brightness, and we set our phones to trigger pictures no more than twice a second. Subjects needed to be within ten feet of the phone for reliable triggering, and some surfaces gave poorer results. We primarily tested using the Motorola Atrix 4G (Android 2.3.4) and HTC Evo 4G (Android 2.3.3) and found only a few differences in performance running the same program, which we attribute to differences in the hardware. We also tested two older Android phones that had problems with crashing when running our program. Our results provide good guidance for when and where to use this approach to inexpensive sensing

Effects of the Factory Reset on Mobile Devices

Mobile devices usually provide a “factory-reset” tool to erase user-specific data from the main secondary storage. 9 Apple iPhones, 10 Android devices, and 2 BlackBerry devices were tested in the first systematic evaluation of the effectiveness of factory resets. Tests used the Cellebrite UME-36 Pro with the UFED Physical Analyzer, the Bulk Extractor open-source tool, and our own programs for extracting metadata, classifying file paths, and comparing them between images. Two phones were subjected to more detailed analysis. Results showed that many kinds of data were removed by the resets, but much user-specific configuration data was left. Android devices did poorly at removing user documents and media, and occasional surprising user data was left on all devices including photo images, audio, documents, phone numbers, email addresses, geolocation data, configuration data, and keys. A conclusion is that reset devices can still provide some useful information to a forensic investigation

Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem

NPS NRP Executive SummaryThe objective is to review requirements created within the DoD Requirements process and identify those that create excessive cost growth, and rank programs with significant cost growth. The research questions are: ' What are common elements of requirements that create excessive cost growth in Navy systems? ' Assuming the elements are identified, determine the risk (likelihood and magnitude) of cost growth from common elements for both procurement and sustainment costs. We propose structured and unstructured data sciences and business intelligence to address the research questions: ' Apply text analyses to the DoD programs requirements data from the operational requirements documents and previous processes. Locate the cost growth risks (likelihood and magnitude) in terms of characteristics including capability requirements (unstructured), key performance parameters (structured data), key systems attributes (structured data), keywords, themes, and entities. Tools include lexical link analysis, spaCy (https://spacy.io/), Orange, and https://prodi.gy/ (for classification). ' Apply Network/graph tools: visualize the risks and capabilities in terms of relations. Prioritize capability, program, system, or product using centrality analysis and correlate with the cost growth risk. ' Apply the integrated deep analytics of leveraging AI for learning, optimize, and wargame (LAILOW) framework, derived from the ONR funded projects. Patterns are learned from big data (if any) and used for the optimization of what if analysis. New operation and capability requirements anticipate uncertainty, unknowns, and unexpected situations when there is no or rare data. This motivates using wargame simulations to coevolve risks and capabilities using coevolutionary algorithms of selection, mutation, and crossover. The tasks include scoping the data and demonstrating the proposed methods. The deliverables include reports, a demonstration, and a paper approved by the sponsor.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem

NPS NRP Technical ReportThe objective is to review requirements created within the DoD Requirements process and identify those that create excessive cost growth, and rank programs with significant cost growth. The research questions are: ' What are common elements of requirements that create excessive cost growth in Navy systems? ' Assuming the elements are identified, determine the risk (likelihood and magnitude) of cost growth from common elements for both procurement and sustainment costs. We propose structured and unstructured data sciences and business intelligence to address the research questions: ' Apply text analyses to the DoD programs requirements data from the operational requirements documents and previous processes. Locate the cost growth risks (likelihood and magnitude) in terms of characteristics including capability requirements (unstructured), key performance parameters (structured data), key systems attributes (structured data), keywords, themes, and entities. Tools include lexical link analysis, spaCy (https://spacy.io/), Orange, and https://prodi.gy/ (for classification). ' Apply Network/graph tools: visualize the risks and capabilities in terms of relations. Prioritize capability, program, system, or product using centrality analysis and correlate with the cost growth risk. ' Apply the integrated deep analytics of leveraging AI for learning, optimize, and wargame (LAILOW) framework, derived from the ONR funded projects. Patterns are learned from big data (if any) and used for the optimization of what if analysis. New operation and capability requirements anticipate uncertainty, unknowns, and unexpected situations when there is no or rare data. This motivates using wargame simulations to coevolve risks and capabilities using coevolutionary algorithms of selection, mutation, and crossover. The tasks include scoping the data and demonstrating the proposed methods. The deliverables include reports, a demonstration, and a paper approved by the sponsor.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem

NPS NRP Project PosterThe objective is to review requirements created within the DoD Requirements process and identify those that create excessive cost growth, and rank programs with significant cost growth. The research questions are: ' What are common elements of requirements that create excessive cost growth in Navy systems? ' Assuming the elements are identified, determine the risk (likelihood and magnitude) of cost growth from common elements for both procurement and sustainment costs. We propose structured and unstructured data sciences and business intelligence to address the research questions: ' Apply text analyses to the DoD programs requirements data from the operational requirements documents and previous processes. Locate the cost growth risks (likelihood and magnitude) in terms of characteristics including capability requirements (unstructured), key performance parameters (structured data), key systems attributes (structured data), keywords, themes, and entities. Tools include lexical link analysis, spaCy (https://spacy.io/), Orange, and https://prodi.gy/ (for classification). ' Apply Network/graph tools: visualize the risks and capabilities in terms of relations. Prioritize capability, program, system, or product using centrality analysis and correlate with the cost growth risk. ' Apply the integrated deep analytics of leveraging AI for learning, optimize, and wargame (LAILOW) framework, derived from the ONR funded projects. Patterns are learned from big data (if any) and used for the optimization of what if analysis. New operation and capability requirements anticipate uncertainty, unknowns, and unexpected situations when there is no or rare data. This motivates using wargame simulations to coevolve risks and capabilities using coevolutionary algorithms of selection, mutation, and crossover. The tasks include scoping the data and demonstrating the proposed methods. The deliverables include reports, a demonstration, and a paper approved by the sponsor.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Making Sense of Email Addresses on Drives

Drives found during investigations often have useful information in the form of email addresses, which can be acquired by search in the raw drive data independent of the file system. Using these data, we can build a picture of the social networks in which a drive owner participated, even perhaps better than investigating their online profiles maintained by social-networking services, because drives contain much data that users have not approved for public display. However, many addresses found on drives are not forensically interesting, such as sales and support links. We developed a program to filter these out using a Naïve Bayes classifier and eliminated 73.3% of the addresses from a representative corpus. We show that the byte-offset proximity of the remaining addresses found on a drive, their word similarity, and their number of co-occurrences over a corpus are good measures of association of addresses, and we built graphs using this data of the interconnections both between addresses and between drives. Results provided several new insights into our test data

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Technical ReportThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Executive SummaryThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Project PosterThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Making Sense of Email Addresses on Drives

Drives found during investigations often have useful information in the form of email addresses which can be acquired by search in the raw drive data independent of the file system. Using this data we can build a picture of the social networks that a drive owner participated in, even perhaps better than investigating their online profiles maintained by social-networking services because drives contain much data that users have not approved for public display. However, many addresses found on drives are not forensically interesting, such as sales and support links. We developed a program to filter these out using a Naïve Bayes classifier and eliminated 73.3% of the addresses from a representative corpus. We show that the byte-offset proximity of the remaining addresses found on a drive, their word similarity, and their number of co-occurrences over a corpus are good measures of association of addresses, and we built graphs using this data of the interconnections both between addresses and between drives. Results provided several new insights into our test data