The document proposes a smartphone-based behavioral authentication system called SenSec. It collects sensor data to build user behavior models. Features are extracted from the sensor data and used to build risk analysis trees to detect anomalies. When anomalies are detected, a certainty score is broadcast and can trigger authentication for sensitive applications. The system was tested on a dataset of 25 users, achieving over 98% accuracy in user identification. Extensions and integrations with other systems are discussed to enhance security, privacy, and energy efficiency.
Behaviometrics: Behavior Modeling from Heterogeneous Sensory Time-SeriesJiang Zhu
Over the decades, we have seen tremendous success in biometrics technologies being used in all types of applications based on the physical attributes of the individual such as face, fingerprints, voice and iris. Inspired by this, we introduce a new concept Mobile Behaviometrics, which uses algorithms and models to measure and quantify unique human behavioral patterns in place of human bio-attributes. Behaviometrics algorithms take multiple data from various sensors as input and fuse them to build behavioral models which are capable of producing application specific quantitative analysis on the unique individuals that were the originators of the data.
TaintDroid is a system that provides dynamic taint tracking and analysis for Android. It tracks privacy sensitive information like location, contacts etc. at variable, message, method and file levels with 14% overhead. Testing 30 apps found 20 shared information unexpectedly, like sending device IDs or location to ad servers. TaintDroid effectively demonstrates the need for stronger mobile privacy but has limitations like requiring OS modifications and false positives. Future work aims to reduce false positives, integrate crowdsourcing and detect privacy information leakage attempts.
We introduce a new mobile system framework, SenSec, which uses passive sensory data to ensure the security of applications and data on mobile devices.
SenSec constantly collects sensory data from accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user uses the device.
SenSec calculates the sureness that the mobile device is being used by its owner.
Based on the sureness score, mobile devices can dynamically request the user to provide active authentication (such as a strong password), or disable certain features of the mobile devices to protect user's privacy and information security.
In this paper, we model such gesture patterns through a continuous n-gram language model using a set of features constructed from these sensors. We built mobile application prototype based on this model and use it to perform both user classification and user authentication experiments. User studies show that SenSec can achieve 75 accuracy in identifying the users and 71.3 accuracy in detecting the non-owners with only 13.1 false alarms.
The document discusses using mobility traces and context information to detect loss or theft of mobile devices. It proposes converting traces and context into "behavior text" representations, then building an n-gram language model to establish a baseline for normal behavior. The model can detect anomalies indicating potential loss or theft events by flagging sequences with unexpectedly low probabilities. The approach aims to discover such events early for notification and recovery efforts.
Guest Lecture: SenSec - Mobile Security through BehavioMetrics Jiang Zhu
This document summarizes research on using mobile sensor data and behavioral biometrics for user authentication and activity recognition. It describes collecting data from accelerometers, GPS, WiFi and applications to build language models of user behavior. Scores are calculated to determine the likelihood a behavior belongs to a user or activity class. Authentication is triggered based on thresholds. The system was tested to identify users from single key presses and detect anomalies with days of training data at 80% accuracy. Future work involves expanded data collection, improved models, integration with security frameworks, and ensuring user privacy.
The penetration of mobile devices equipped with various embedded sensors also make it possible to capture the physical and virtual context of the user and surrounding environment. Further, the modeling of human behaviors based on those data becomes very important due to the increasing popularity of context-aware computing and people-centric applications, which utilize users' behavior pattern to improve the existing services or enable new services. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion.
With the increasing in the number of anti-social activates that have been taking place, security has been given utmost importance lately. Many Organizations have installed CCTVs for constant Monitoring of people and their interactions. For a developed Country with a population of 64 million, every person is captured by a camera 30 times a day. A lot of video data generated and stored for a certain time duration. A 704x576 resolution image recorded at 25fps will generate roughly 20GB per day. Constant Monitoring of data by humans to judge if the events are abnormal is near impossible task as requires a workforce and their constant attention. This creates a need to automate the same. Also , there is need to show in which frame and which part of it contain the unusual activity which aid the faster judgment of the unusual activity being abnormal. This is done by converting video into frames and analyzing the persons and their activates from the processed frame .Machine learning and Deep Learning Algorithms and techniques support us in a wide accept to make Possible.
Behaviometrics: Behavior Modeling from Heterogeneous Sensory Time-SeriesJiang Zhu
Over the decades, we have seen tremendous success in biometrics technologies being used in all types of applications based on the physical attributes of the individual such as face, fingerprints, voice and iris. Inspired by this, we introduce a new concept Mobile Behaviometrics, which uses algorithms and models to measure and quantify unique human behavioral patterns in place of human bio-attributes. Behaviometrics algorithms take multiple data from various sensors as input and fuse them to build behavioral models which are capable of producing application specific quantitative analysis on the unique individuals that were the originators of the data.
TaintDroid is a system that provides dynamic taint tracking and analysis for Android. It tracks privacy sensitive information like location, contacts etc. at variable, message, method and file levels with 14% overhead. Testing 30 apps found 20 shared information unexpectedly, like sending device IDs or location to ad servers. TaintDroid effectively demonstrates the need for stronger mobile privacy but has limitations like requiring OS modifications and false positives. Future work aims to reduce false positives, integrate crowdsourcing and detect privacy information leakage attempts.
We introduce a new mobile system framework, SenSec, which uses passive sensory data to ensure the security of applications and data on mobile devices.
SenSec constantly collects sensory data from accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user uses the device.
SenSec calculates the sureness that the mobile device is being used by its owner.
Based on the sureness score, mobile devices can dynamically request the user to provide active authentication (such as a strong password), or disable certain features of the mobile devices to protect user's privacy and information security.
In this paper, we model such gesture patterns through a continuous n-gram language model using a set of features constructed from these sensors. We built mobile application prototype based on this model and use it to perform both user classification and user authentication experiments. User studies show that SenSec can achieve 75 accuracy in identifying the users and 71.3 accuracy in detecting the non-owners with only 13.1 false alarms.
The document discusses using mobility traces and context information to detect loss or theft of mobile devices. It proposes converting traces and context into "behavior text" representations, then building an n-gram language model to establish a baseline for normal behavior. The model can detect anomalies indicating potential loss or theft events by flagging sequences with unexpectedly low probabilities. The approach aims to discover such events early for notification and recovery efforts.
Guest Lecture: SenSec - Mobile Security through BehavioMetrics Jiang Zhu
This document summarizes research on using mobile sensor data and behavioral biometrics for user authentication and activity recognition. It describes collecting data from accelerometers, GPS, WiFi and applications to build language models of user behavior. Scores are calculated to determine the likelihood a behavior belongs to a user or activity class. Authentication is triggered based on thresholds. The system was tested to identify users from single key presses and detect anomalies with days of training data at 80% accuracy. Future work involves expanded data collection, improved models, integration with security frameworks, and ensuring user privacy.
The penetration of mobile devices equipped with various embedded sensors also make it possible to capture the physical and virtual context of the user and surrounding environment. Further, the modeling of human behaviors based on those data becomes very important due to the increasing popularity of context-aware computing and people-centric applications, which utilize users' behavior pattern to improve the existing services or enable new services. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion.
With the increasing in the number of anti-social activates that have been taking place, security has been given utmost importance lately. Many Organizations have installed CCTVs for constant Monitoring of people and their interactions. For a developed Country with a population of 64 million, every person is captured by a camera 30 times a day. A lot of video data generated and stored for a certain time duration. A 704x576 resolution image recorded at 25fps will generate roughly 20GB per day. Constant Monitoring of data by humans to judge if the events are abnormal is near impossible task as requires a workforce and their constant attention. This creates a need to automate the same. Also , there is need to show in which frame and which part of it contain the unusual activity which aid the faster judgment of the unusual activity being abnormal. This is done by converting video into frames and analyzing the persons and their activates from the processed frame .Machine learning and Deep Learning Algorithms and techniques support us in a wide accept to make Possible.
Wearable Computing - Part III: The Activity Recognition Chain (ARC)Daniel Roggen
This document discusses activity recognition from sensor data. It describes how simple binary sensors can provide some information but full activity detection requires interpreting multiple correlated sensor streams using techniques like signal processing, machine learning and reasoning. Key steps in activity recognition systems are preprocessing, segmentation, feature extraction, and classification of sensor data. Challenges include continuous recognition, dealing with variable executions of activities, and separating activities from non-activities.
Activity recognition based on a multi-sensor meta-classifierOresti Banos
Ensuring ubiquity, robustness and continuity of monitoring
is of key importance in activity recognition. To that end, multiple sensor congurations and fusion techniques are ever more used. In this paper we present a multi-sensor meta-classier that aggregates the knowledge of several sensor-based decision entities to provide a unique and reliable activity classication. This model introduces a new weighting scheme which improves the rating of the impact that each entity has on the decision fusion process. Sensitivity and specicity are particularly considered as insertion and rejection weighting metrics instead of the overall accuracy classication performance proposed in a previous work. For the sake of comparison, both new and previous weighting models together with feature fusion models are tested on an extensive activity recognition
benchmark dataset. The results demonstrate that the new weighting scheme enhances the decision aggregation thus leading to an improved recognition system.
This presentation illustrates part of the work described in the following articles:
* Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B. & Valenzuela, O.
Human activity recognition based on a sensor weighting hierarchical classifier.
Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer Berlin / Heidelberg, vol. 17, pp. 333-343 (2013)
* Banos, O., Damas, M., Pomares, H., Rojas, I.: Activity recognition based on a multi-sensor meta-classifier. In: Proceedings of the 2013 International Work Conference on Neural Networks (IWANN 2013), Tenerife, Spain, June 12-14, (2013)
Wearable technologies: what's brewing in the lab?Daniel Roggen
Wearable technologies are being developed for a variety of applications in both research labs and commercial settings. Some key areas of focus include flexible and stretchable electronics; custom wearables for specific sensing needs; activity recognition for tasks like healthcare monitoring and sports analysis; and developing wearables as "smart assistants" that can augment users by constantly sensing their context. Research challenges include miniaturizing components, developing low-power sensing and recognition, and enabling wearables to self-adapt over time through techniques like online user adaptation.
This document discusses different types of sensors that can be used for wearable computing applications. It describes sensors for measuring physical context like location, activity, and environment as well as internal states like emotions and cognition. Both software sensors from data on devices and hardware sensors are covered. Specific sensor technologies discussed include accelerometers, gyroscopes, inertial measurement units, GPS, radio fingerprints, capacitive sensing, electrooculography, and skin conductance sensors. Examples are given of how sensor data can be fused and analyzed to infer higher level context and activities. Challenges of using sensors on the body are also addressed.
Sherlock: Monitoring sensor broadcasted data to optimize mobile environmentijsrd.com
Sherlock is a framework that uses sensors in smartphones to optimize the micro-environment around the phone. It runs as a daemon process and provides finer-grained environmental information to applications through APIs. The goal is to save battery by adapting the phone's behavior based on accurate context, such as dimming the screen when in a pocket or bag. It covers major usage scenarios and can detect if the phone is in the hand, on a desk, etc. using sensors like proximity, accelerometer, gyroscope. This allows applications to provide customized services based on the user's situation.
Sensors, threats, responses and challenges - Dr Emil Lupu (Imperial College L...Comit Projects Ltd
Presentation by Dr Emil Lupu (Imperial College London)at COMIT 2016: Digitally Building Britain, September 2016
More information: http://www.comit.org.uk/liveblog
NFC allows short-range wireless data transfer between devices that are within 10 cm of each other. It works using magnetic field induction and can operate in either active or passive mode. NFC has applications in areas like contactless payment, ticketing, and data sharing. Major phone manufacturers have incorporated NFC in recent devices and its use is expected to grow as more popular mobile platforms adopt the technology.
Assessment Test Framework for Collecting and Evaluating Fall - Related Data u...Martin Ebner
This document presents a framework for collecting and evaluating fall-related data using mobile devices. The framework includes a test client application for mobile devices that allows clinical mobility tests to be performed and acceleration data to be recorded. A proof-of-concept was developed using an iPhone to record acceleration data during mobility tests. The recorded data was then analyzed to evaluate the potential for fall detection using mobile devices. The framework is intended to support integration of various sensor-enabled devices and allow collected data to be accessed for further analysis.
The DemaWare Service-Oriented AAL Platform for People with DementiaYiannis Kompatsiaris
This work presents DemaWare, an Ambient Intelligence platform that targets Ambient Assisted Living for people with Dementia. DemaWare seamlessly integrates diverse hardware (wearable and ambient sensors), as well as soft- ware components (semantic interpretation, reasoning), involved in such context. It also enables both online and offline processes, including sensor analysis and storage of context semantics in a Knowledge Base. Consequently, it orchestrates semantic interpretation which incorporated defeasible logics for uncertainty handling. Overall, the underlying functionality aids clinicians and carers to timely assess and diagnose patients in the context of lab trials, homes or nursing homes.
For the IoTweek 2019 conference in Aarhus Denmark 8 concepts were presented to an audience of Industry and higher education, which demonstrate the capabilities of the IoTCrawler and their potential to generate an impact within different domains.
Read more about the partners and test-beds presented at: https://iotcrawler.eu/index.php/partners/
NoxEye is an AI-based threat detection system for real-time video surveillance. It uses Faster RCNN models running on fog nodes to detect and label possible crime events and objects in motion-captured images from edge nodes. When a crime is detected, the images are saved and notifications are sent to police. This provides efficient, real-time crime detection while distributing processing load across the IoT/fog/cloud architecture. The system aims to prevent crimes with high prediction accuracy and fast response times through an easy-to-use interface.
This document discusses the development of an Android application for physical activity recognition using the accelerometer sensor. It provides background on the Android operating system and its open development environment. It then summarizes relevant research papers on activity recognition using mobile sensors. The document outlines the process of collecting and labeling accelerometer data from smartphone sensors during different physical activities. Features are extracted from the sensor data and several machine learning classifiers are evaluated for activity recognition. The application will recognize activities and track metrics like calories burned, distance traveled, and implement fall detection and medical reminders.
[DSC Europe 23] Mihailo Ilic - Scalable and Interoperable Data Flow Managemen...DataScienceConferenc1
In recent years, there has been a significant increase in the use of Smart Farming Technologies (SFTs), which are seen as key enablers in farm management for crop monitoring and reduction of chemical use. This presentation will cover a key component for the advancement of such systems – a data infrastructure which offers semantic and syntactic interoperability. Through the utilization of ontologies and smart data models in the agricultural domain, this kind of infrastructure can support actionable digital twins and advance farming capabilities.
From Context-awareness to Human Behavior PatternsVille Antila
Ville Antila discusses using smartphones to detect daily routines and human behavior patterns through continuous context logging. Smartphones can sense context through built-in sensors and log location, device usage, physical activity, and Bluetooth snapshots. This data is interpreted to estimate routines like locations visited and detect changes. Example applications include context-adaptive feedback that considers situation suitability, and context-based user interface migration between devices. Challenges include ensuring quality, user awareness of adaptive behavior, and testing context-aware applications in real-world use.
The document discusses sensemaking from distributed mobile sensing data from a middleware perspective. It notes that the proliferation of smartphones and their various sensors enables crowdsensing for applications like emergency response, personal health monitoring, and spatial field sensing. However, developing collaborative mobile apps for sensemaking is challenging due to barriers like lack of standardized APIs and scalability issues. The document proposes a distributed middleware framework to address this by providing APIs and libraries for collaboration, virtual sensing, computational offloading, and cloud integration to ease app development and ensure scalability. It discusses some example middleware platforms and techniques used for sensemaking.
Bringing Wireless Sensing to its full potentialAdrian Hornsby
This document discusses bringing wireless sensing to its full potential through the use of standards. It outlines how wireless sensor networks can be integrated with the Internet using standards like 6LoWPAN to allow IP connectivity for low power devices. It also discusses using semantic standards to annotate sensor data for improved discovery and interoperability through frameworks like the Sensor Web Enablement. Finally, it discusses how efficient XML formats like EXI can be used to compress XML data exchange in bandwidth constrained wireless sensor networks.
Detecting and Improving Distorted Fingerprints using rectification techniques.sandipan paul
In this detection and improving distorted fingerprint using rectification techniques like SVM, PCA classifier etc.
In this ppt a distorted fingerprint is taken and improve that distorted fingerprint into normal one.
Mobile fraud detection using neural networksVidhya Moorthy
This document discusses using neural networks for mobile fraud detection. It begins by defining fraud and how it impacts mobile network operators. It then classifies different types of fraud and indicators used for detection. Current detection methods like rule-based and differential analysis are described along with their limitations. Neural networks are proposed as an improved method for both existing and new fraud detection by training on relevant data only. The document concludes neural networks can help reduce false alarms while still detecting stolen phones but recommends adding a password verification process.
1) The document discusses a context-aware mobile social web, where mobile applications can access and use contextual information about users, such as their location, activity, and device characteristics.
2) Telecom Italia has developed platforms and applications to enable this context-aware mobile social web, including tools for collecting, representing, and analyzing context data, as well as applications that provide location-based recommendations and allow users to tag content with context.
3) Lessons from deploying these applications include the importance of openness to popular social networks, using context accurately, promoting high-quality content, and ensuring user privacy and comfort with sharing personal information. Standardization of context representation could help address challenges involving context certification and sharing
Wearable Computing - Part III: The Activity Recognition Chain (ARC)Daniel Roggen
This document discusses activity recognition from sensor data. It describes how simple binary sensors can provide some information but full activity detection requires interpreting multiple correlated sensor streams using techniques like signal processing, machine learning and reasoning. Key steps in activity recognition systems are preprocessing, segmentation, feature extraction, and classification of sensor data. Challenges include continuous recognition, dealing with variable executions of activities, and separating activities from non-activities.
Activity recognition based on a multi-sensor meta-classifierOresti Banos
Ensuring ubiquity, robustness and continuity of monitoring
is of key importance in activity recognition. To that end, multiple sensor congurations and fusion techniques are ever more used. In this paper we present a multi-sensor meta-classier that aggregates the knowledge of several sensor-based decision entities to provide a unique and reliable activity classication. This model introduces a new weighting scheme which improves the rating of the impact that each entity has on the decision fusion process. Sensitivity and specicity are particularly considered as insertion and rejection weighting metrics instead of the overall accuracy classication performance proposed in a previous work. For the sake of comparison, both new and previous weighting models together with feature fusion models are tested on an extensive activity recognition
benchmark dataset. The results demonstrate that the new weighting scheme enhances the decision aggregation thus leading to an improved recognition system.
This presentation illustrates part of the work described in the following articles:
* Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B. & Valenzuela, O.
Human activity recognition based on a sensor weighting hierarchical classifier.
Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer Berlin / Heidelberg, vol. 17, pp. 333-343 (2013)
* Banos, O., Damas, M., Pomares, H., Rojas, I.: Activity recognition based on a multi-sensor meta-classifier. In: Proceedings of the 2013 International Work Conference on Neural Networks (IWANN 2013), Tenerife, Spain, June 12-14, (2013)
Wearable technologies: what's brewing in the lab?Daniel Roggen
Wearable technologies are being developed for a variety of applications in both research labs and commercial settings. Some key areas of focus include flexible and stretchable electronics; custom wearables for specific sensing needs; activity recognition for tasks like healthcare monitoring and sports analysis; and developing wearables as "smart assistants" that can augment users by constantly sensing their context. Research challenges include miniaturizing components, developing low-power sensing and recognition, and enabling wearables to self-adapt over time through techniques like online user adaptation.
This document discusses different types of sensors that can be used for wearable computing applications. It describes sensors for measuring physical context like location, activity, and environment as well as internal states like emotions and cognition. Both software sensors from data on devices and hardware sensors are covered. Specific sensor technologies discussed include accelerometers, gyroscopes, inertial measurement units, GPS, radio fingerprints, capacitive sensing, electrooculography, and skin conductance sensors. Examples are given of how sensor data can be fused and analyzed to infer higher level context and activities. Challenges of using sensors on the body are also addressed.
Sherlock: Monitoring sensor broadcasted data to optimize mobile environmentijsrd.com
Sherlock is a framework that uses sensors in smartphones to optimize the micro-environment around the phone. It runs as a daemon process and provides finer-grained environmental information to applications through APIs. The goal is to save battery by adapting the phone's behavior based on accurate context, such as dimming the screen when in a pocket or bag. It covers major usage scenarios and can detect if the phone is in the hand, on a desk, etc. using sensors like proximity, accelerometer, gyroscope. This allows applications to provide customized services based on the user's situation.
Sensors, threats, responses and challenges - Dr Emil Lupu (Imperial College L...Comit Projects Ltd
Presentation by Dr Emil Lupu (Imperial College London)at COMIT 2016: Digitally Building Britain, September 2016
More information: http://www.comit.org.uk/liveblog
NFC allows short-range wireless data transfer between devices that are within 10 cm of each other. It works using magnetic field induction and can operate in either active or passive mode. NFC has applications in areas like contactless payment, ticketing, and data sharing. Major phone manufacturers have incorporated NFC in recent devices and its use is expected to grow as more popular mobile platforms adopt the technology.
Assessment Test Framework for Collecting and Evaluating Fall - Related Data u...Martin Ebner
This document presents a framework for collecting and evaluating fall-related data using mobile devices. The framework includes a test client application for mobile devices that allows clinical mobility tests to be performed and acceleration data to be recorded. A proof-of-concept was developed using an iPhone to record acceleration data during mobility tests. The recorded data was then analyzed to evaluate the potential for fall detection using mobile devices. The framework is intended to support integration of various sensor-enabled devices and allow collected data to be accessed for further analysis.
The DemaWare Service-Oriented AAL Platform for People with DementiaYiannis Kompatsiaris
This work presents DemaWare, an Ambient Intelligence platform that targets Ambient Assisted Living for people with Dementia. DemaWare seamlessly integrates diverse hardware (wearable and ambient sensors), as well as soft- ware components (semantic interpretation, reasoning), involved in such context. It also enables both online and offline processes, including sensor analysis and storage of context semantics in a Knowledge Base. Consequently, it orchestrates semantic interpretation which incorporated defeasible logics for uncertainty handling. Overall, the underlying functionality aids clinicians and carers to timely assess and diagnose patients in the context of lab trials, homes or nursing homes.
For the IoTweek 2019 conference in Aarhus Denmark 8 concepts were presented to an audience of Industry and higher education, which demonstrate the capabilities of the IoTCrawler and their potential to generate an impact within different domains.
Read more about the partners and test-beds presented at: https://iotcrawler.eu/index.php/partners/
NoxEye is an AI-based threat detection system for real-time video surveillance. It uses Faster RCNN models running on fog nodes to detect and label possible crime events and objects in motion-captured images from edge nodes. When a crime is detected, the images are saved and notifications are sent to police. This provides efficient, real-time crime detection while distributing processing load across the IoT/fog/cloud architecture. The system aims to prevent crimes with high prediction accuracy and fast response times through an easy-to-use interface.
This document discusses the development of an Android application for physical activity recognition using the accelerometer sensor. It provides background on the Android operating system and its open development environment. It then summarizes relevant research papers on activity recognition using mobile sensors. The document outlines the process of collecting and labeling accelerometer data from smartphone sensors during different physical activities. Features are extracted from the sensor data and several machine learning classifiers are evaluated for activity recognition. The application will recognize activities and track metrics like calories burned, distance traveled, and implement fall detection and medical reminders.
[DSC Europe 23] Mihailo Ilic - Scalable and Interoperable Data Flow Managemen...DataScienceConferenc1
In recent years, there has been a significant increase in the use of Smart Farming Technologies (SFTs), which are seen as key enablers in farm management for crop monitoring and reduction of chemical use. This presentation will cover a key component for the advancement of such systems – a data infrastructure which offers semantic and syntactic interoperability. Through the utilization of ontologies and smart data models in the agricultural domain, this kind of infrastructure can support actionable digital twins and advance farming capabilities.
From Context-awareness to Human Behavior PatternsVille Antila
Ville Antila discusses using smartphones to detect daily routines and human behavior patterns through continuous context logging. Smartphones can sense context through built-in sensors and log location, device usage, physical activity, and Bluetooth snapshots. This data is interpreted to estimate routines like locations visited and detect changes. Example applications include context-adaptive feedback that considers situation suitability, and context-based user interface migration between devices. Challenges include ensuring quality, user awareness of adaptive behavior, and testing context-aware applications in real-world use.
The document discusses sensemaking from distributed mobile sensing data from a middleware perspective. It notes that the proliferation of smartphones and their various sensors enables crowdsensing for applications like emergency response, personal health monitoring, and spatial field sensing. However, developing collaborative mobile apps for sensemaking is challenging due to barriers like lack of standardized APIs and scalability issues. The document proposes a distributed middleware framework to address this by providing APIs and libraries for collaboration, virtual sensing, computational offloading, and cloud integration to ease app development and ensure scalability. It discusses some example middleware platforms and techniques used for sensemaking.
Bringing Wireless Sensing to its full potentialAdrian Hornsby
This document discusses bringing wireless sensing to its full potential through the use of standards. It outlines how wireless sensor networks can be integrated with the Internet using standards like 6LoWPAN to allow IP connectivity for low power devices. It also discusses using semantic standards to annotate sensor data for improved discovery and interoperability through frameworks like the Sensor Web Enablement. Finally, it discusses how efficient XML formats like EXI can be used to compress XML data exchange in bandwidth constrained wireless sensor networks.
Detecting and Improving Distorted Fingerprints using rectification techniques.sandipan paul
In this detection and improving distorted fingerprint using rectification techniques like SVM, PCA classifier etc.
In this ppt a distorted fingerprint is taken and improve that distorted fingerprint into normal one.
Mobile fraud detection using neural networksVidhya Moorthy
This document discusses using neural networks for mobile fraud detection. It begins by defining fraud and how it impacts mobile network operators. It then classifies different types of fraud and indicators used for detection. Current detection methods like rule-based and differential analysis are described along with their limitations. Neural networks are proposed as an improved method for both existing and new fraud detection by training on relevant data only. The document concludes neural networks can help reduce false alarms while still detecting stolen phones but recommends adding a password verification process.
1) The document discusses a context-aware mobile social web, where mobile applications can access and use contextual information about users, such as their location, activity, and device characteristics.
2) Telecom Italia has developed platforms and applications to enable this context-aware mobile social web, including tools for collecting, representing, and analyzing context data, as well as applications that provide location-based recommendations and allow users to tag content with context.
3) Lessons from deploying these applications include the importance of openness to popular social networks, using context accurately, promoting high-quality content, and ensuring user privacy and comfort with sharing personal information. Standardization of context representation could help address challenges involving context certification and sharing
The document discusses the key features and architecture of the Internet of Things (IoT). It describes IoT as connecting physical devices through sensors and software to collect and exchange data over networks. The key features discussed are artificial intelligence, interconnectivity, distributed processing, heterogeneity, interoperability, scalability, security, and dynamic changes. The basic IoT architecture includes sensor networks, gateways, and communication technologies to connect devices. Sensor networks gather data from various sensors, while gateways act as an interface between sensor networks and cloud/application services. Common wireless technologies enabling IoT device connectivity include RFID, WLAN, and short-range wireless protocols.
Context is King: AR, AI, Salience, and the Constant Next ScenarioClark Dodsworth
Clark Dodsworth’s AREvent talk, Santa Clara, CA June 3, 2010: "Context is King: AR, AI, Saience, and the Constant Next Scenario" Mostly about smartphone AR as a gateway to context aware computing becoming indispensible.
[EUC2014] cODA: An Open-Source Framework to Easily Design Context-Aware Andro...Matteo Ferroni
Mobile devices take an important part in everyday life. They are now cheaper and widespread, but still a lot of time is spent by the users to configure them: users adapt to their own device, not vice versa. Can our smartphones do something smarter? In this work, we propose a framework to support the development of context-aware applications for Android devices: the goal of such applications is to reduce as much as possible the interaction with the user, making use of automatic and intelligent components. Moreover, these components should consume as less power and computational resources as possible, being them part of a mobile ecosystem whose battery and hardware are highly constrained. The work implies the study of a methodology that fits the Android framework and the design of a highly extensible software architecture. An open-source framework based on the proposed methodology is then described. Some use cases are finally presented, analyzing the performances and the limitations of the proposed methodology.
Full paper: http://ieeexplore.ieee.org/abstract/document/6962264
The document discusses the key features and architecture of the Internet of Things (IoT). It describes IoT as connecting physical devices through sensors and software to collect and exchange data over networks. The key features discussed are artificial intelligence, interconnectivity, distributed processing, heterogeneity, interoperability, scalability, security, and dynamic changes. The basic IoT architecture includes sensor networks, gateways, and communication technologies to connect devices. Sensor networks gather data from various sensors, while gateways act as an interface between sensor networks and cloud/application services. Common wireless technologies enabling IoT device connectivity include RFID, WLAN, and short-range wireless protocols.
Sense Networks uses proprietary technology and location analytics expertise to analyze location history and behavior patterns to deliver unique insights and intelligence. They have built a high-capacity platform called MacroSense that can extract information from tens of millions of user locations and points of interest, segmenting users and predicting future behaviors. Their location-based segments have been shown to drive user responses and actions.
Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks,
Fog computing is a term created by Cisco that refers to extending cloud computing to the edge of an enterprise's network.
Cisco introduced its fog computing vision in January 2014 as a way of bringing cloud computing capabilities to the edge of the network .
As the result, closer to the rapidly growing number of connected devices and applications that consume cloud services and generate increasingly massive amounts of data.
SensLAB is a very large scale open wireless sensor network testbed consisting of over 1000 sensor nodes distributed across 4 sites in France. It aims to provide researchers with an experimental platform for developing and validating new sensor network protocols and applications through large-scale deployment and experimentation. The testbed supports heterogeneous sensor nodes and radio technologies and provides tools for remote and automated experimentation, as well as non-intrusive monitoring of experiments.
This document provides an overview and summary of mobile application risks. It begins with defining the mobile threat landscape, including statistics on the prevalence of Android malware. It then discusses the various types of mobile malware threats and behaviors. The document outlines vulnerabilities in mobile applications and ecosystems. It proposes approaches for securing the mobile environment, including static and dynamic behavioral analysis, malware detection, and vulnerability analysis. Finally, it discusses strategic control points for security and some enterprise solutions for mitigating risks of bring your own device policies.
The document discusses intuitive user interfaces and one-touch interactions. It describes a company called IntuitiveUI that aims to simplify device usage through predictive modeling and a one-touch experience. IntuitiveUI uses sensors and logging of user behaviors to build statistical models and predict common actions based on context like time, location and past events. This allows displaying relevant options with a single touch rather than multiple taps through conventional menus. The approach aims to overcome challenges of mobile complexity but challenges include uneven data collection and meeting user expectations.
CYBER INTELLIGENCE & RESPONSE TECHNOLOGYjmical
This document provides an overview of AccessData's Cyber Intelligence Response Technology (CIRT) platform. CIRT offers an integrated suite of digital forensics and incident response capabilities including network forensics, host-based forensics, data auditing, and malware analysis. Key features include an agent that can independently collect and store data from endpoints, a Cerberus module that analyzes files for malicious behaviors without signatures or prior knowledge, and modules for analyzing removable media, volatile memory, and network packet captures. The platform allows multiple teams such as incident response, computer forensics, and compliance to collaborate on investigations.
Similar to SenSec: Mobile Application Security through Passive Sensing (20)
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
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Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
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TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
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Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
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- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
2. • Monitor and track user behavior on smartphones using various
on-device sensors
• Convert sensory traces and other context information to Personal
Behavior Features
• Build Risk Analysis Trees with these features and use it for
calculation of Certainty Scores
• Trigger various Authentication Schemes when certain application
is launched.
2
5. 60% • “The 329 organizations
polled had collectively lost
50% more than 86,000 devices
… with average cost of lost
40% data at $49,246 per device,
30%
worth $2.1 billion or $6.4
million per organization.
20%
10%
"The Billion Dollar Lost-Laptop Study,"
0% conducted by Intel Corporation and the
Ponemon Institute, analyzed the scope
and circumstances of missing laptop
Mobile Device Loss or theft PCs.
Strategy One Survey conducted among a U.S. sample of 3017 adults age 18 years older in September
21-28, 2010, with an oversample in the top 20 cities (based on population).
5
6. Application
Password Different
applications may
have different
A major source of
sensitivities
security vulnerabilities.
Easy to guess, reuse,
forgotten, shared
Usability
Authentication too-often or
sometimes too loose
6
9. • MobiSens app collects sensor data
• Motion sensors
• GPS and WiFi Scanning
• In-use applications and their traffic patterns
• SenSec module build user behavior models
• Unsupervised Activity Segmentation and model the sequence using
Language model
• Building Risk Analysis Tree (DT) to detect anomaly
• Combine above to estimate risk (online): certainty score
• SenSec broadcast certainty score to other applications
• Application Access Control Module uses broadcast receiver
9
10. • Feature vector calculated from a step window represent the
behavior state within a given time window
• surrounding environment: GPS location, WiFi signal
• activity: motions, applications in use
• communication: network traffic
• Using Decision Tree to detect anomaly in behaviors
• Each node represents a feature dimension
• Leaves can be one of the following
• Owner Detection: owner [0,1], 0: Anomaly, 1: Normal
• User Identification: user id [0,1,…. N], user’s identification, i.e. IMEI
• Multiple trees can be built with subset of feature space
• Weighted average
• Voting
10
11. • Convert feature vector series to label streams – dimension reduction
• Using n-gram to model sequence of label stream for each sensory
dimension – current state and transition captured
• Step window with assigned length
A1 A2 A1 A4
G2 G5 G2 G2
W2 W1 W2
P1 P3 P6 P1
A2 G2G5 W1 P1P3 A1A4 G2 W1W2 P1
11
12. • User behavior at time t depends only on the last n-1 behaviors
• Sequence of behaviors can be predicted by n consecutive
location in the past
• Maximum Likelihood Estimation from training data by counting:
• MLE assign zero probability to unseen n-grams
Incorporate smoothing function (Katz)
Discount probability for observed grams
Reserve probability for unseen grams
12
13. • Feed sequence of the past behaviors in a stepping window of size
N to n-gram model for testing
• For a testing sequence of behavior labels
• Estimate the average log probability this sequence is generated
from the n-gram
• If this likelihood drops below a threshold, flag an anomaly alert
13
17. • Total data set size: 4GB
Dataset • Remove 2 heavy users
Numer of users 50
• Remove users with very
Device Android phones limited data duration
• Remove users that don’t
Location Bay area
have application and traffic
Averag period 30 days data due to older MobiSens
version
Number of data
7
types • 25 users with comparable
Finest sampling dataset size
interval (motion 200 ms
sensors) • Data duration: 4 hour ~ 2.5
days
17
18. • Motion Sensors (100)
• Used to summarize
acceleration stream
• Calculated separately for each
dimension [x,y,z,m]
• GPS: location label via density based clustering (1)
• WiFi: (SSIDs, RSSIs) pairs ranked by signal strength (6)
• Applications: Bitmap of well-known applications (60 + 1)
• Application Traffic Pattern: Tx/Rx traffic vectors (120 + 2)
• Step Window Size: 5 seconds
18
19. • User Identification Test and Owner Detection Test for randomly
selected partial data set (4 users) with 1:1 training/test split
• ~ 99% accuracy
• number of leaves: 56 , size of tree: 111
• Using non-motion attributes yields lower accuracy (96%)
• Significant tree size reduction, number of leaves: 3, size of tree: 5
• Cross entropy may be significant to easily distinguish users using some
features.
• Using only motion attributes can distinguish different users
• ~ 98% accuracy
• very large tree, number of leaves: 267, size of tree 533
• may cause performance issues on mobile platform
19
20. • Apply cross-entropy filter to remove users that could be identified
easily using a small set of features
• 12 users with 210k data instances
• User identification : train RAT model on 66% instances and rest
as testing
84.8% 83.5 79.3
100
7649
80
60 Accuracy
40 Size Factor
20 221 35
0
All Non-Motion Motion-Only
20
22. • Experiments to discover anomaly usage with ~80% accuracy with
only days of training data
22
23. • Extended data set for feature construction
TCP, UDP traffic; sound; ambient lighting; battery status, etc.
• Data and Modeling
Gain more insights into the data, features and factorized relationships among
various sensors
Try other classification methods and compare results: LR, SVM, Random
Forest, etc
• Enhanced security of SenSec components
Integration with Android security framework and other applications
• Privacy challenges
Data collection, model training, privacy policy, etc.
• Energy efficiency
23
28. • Data Collection 9.=$(1/6'9.=$;1'
(1/6$/<' 9.=$(1/6'7+"@1/:
• Running app list
!55;$"+#$./ A$21;.<<1,'
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• Per-app traffic pattern 4,.2$;1'!40
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• IPC Interface !"#$%$#&' 4..; 0/#1,2+"1
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(#.,+<1' 718+%$.,'9.:1;$/<'
• Certainty Score 4)68$/<
B1=(1,%$"1' (&6#1* !;<.,$#8*6
3+#+'
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• Offline-Model Push via Data Exchange API
• Risk Analysis Tree can be trained using global data on the MobiSens Server
and pushed back to the mobile device
28
29. • MobiSens Server
• Offline Clustering
• K-means package from Weka Data Mining Toolkit
• Using aggregated data from all users
• Offline RAT training
• Decision Tree package from Weka Data Mining Toolkit
• Construct training data set and design evaluation strategy
• MobiSens Client
• Retrive RAT model from MobiSens Server
• On-device n-gram label sequence construction (n=1,2,3; window size =5s)
• RAT inference using Weka Toolkit on device
• Status bar notification based on certainty value
29
30. • Reactive API to Team Access
API call from Team Access to SenSec to retrieve the current Certainty Score
given the context
getCertaintyScore(SenSecContextType ctx, count)
• Proactive API to Team Acess and other equivalent modules
Broadcast Receiver on Certainty Score
certaintyScore{
CertaintyScoreType scores[];
WindowSizeType window_size;
SenSecContextType ctx;
}
30