ICNC 2013 SenSec Presentation


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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.

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  • As a quick overview of our work, in order to enforce application security, we monitor and track user behavior through the traces collected from the on-device sensors. And then we convert these trace and other context information to behavior features. We adopt a n-gram model to model the user’s behavior and use that to monitor and calculate certainty score. This score will be fed to smartphone’s authentication module to enforce the security of various application and its data on the device.
  • So what’s motivate us to do this project.
  • Mobile applications and devices are becoming ubiquitous and will increasingly interact with different sensors, services and other mobile users. It is crucial for mobile users to privately and securely interact with their environment and data and for mobile services to trust the identity of the user. While mobile devices such as smartphones make our lives convenient in ways that were unimaginable before, applications such as email, web browsing, social network, shopping and online banking know too much about our private lives. Mobility introduces additional security and privacy challenges in being able to provide services in a way that neither compromises the environment of users nor their data. Protecting a user's privacy and ensuring the accountability of mobile applications in a seamless and non-intrusive way poses great challenges to next generation mobile computing platforms.Recently, a new survey* has revealed that 36 percent of consumers in the United States have either lost their mobile phone or had it stolen. Another survey† has also revealed that 329 organizations polled had collectively lost more than 86,000 devices with average cost of lost data at $49,246 per device, worth $2.1 billion or $6.4 million per organization. Given the high loss rate and high cost associated with these losses, accountable schemes are needed to protect the data on the mobile devices.
  • Reliable authentication is an essential requirement for a mobile device and its applications. Today, passwords are the most common form of authentication. This results in two potential problems. First, passwords are also a major source of security vulnerabilities, as they are often easy to guess, re-used, often forgotten, often shared with others, and are susceptible to social engineering attacks. Secondly, to secure the data and applications on a mobile device, the mobile system would prompt user for authentication quite often and this results in series usability issues. We also observe that different applications on a mobile device may have different sensitivities towards the aforementioned threats and data loss. For example, the Angry Bird game on an android is less sensitive than Contact List or Phone Album should the device is operated by unauthorized user. One-thing-for-all approach in authentication schemes may be either too loose for some applications, which expose them to risks, or too tight for others, which cause usability problems.
  • The commoditization of sensor technologies coupled with advances in modeling user behavior offer us new opportunities for simplifying and strengthening authentication. We envision a new mobile system framework, SenSec, which uses passive sensory data to ensure application security.
  • SenSec is constantly collecting sensory data from accelerometer, gyroscope, GPS, WiFi, microphone or even camera. Through analyzing the sensory data, it constructs the context under which the mobile device is used. This includes locations, movements and usage patterns, etc. From the context, the system can calculate the certainty that the system is at risk. Different applications on mobile device are assigned either manually or automatically with a sensitivity value. When user is invoking an application, SenSec compares the certainty with this application’s sensitivity level. If the sensitivity passes the certainty threshold, authentication mechanism would be employed to ensure security policy for that application.
  • Comparing the play to Shakespeare’s known library of works, Track words and phrases patterns in the data P (unknown play| known Shakespeare's work) Greater than threshold? Yeah, we found another Shakespeare's work Or, Plagiarism
  • So building along this line, we use a continousn-gram model to learn the sequence of locations from user’s wifi traces.N-gram model works under the assumptions that the next location in the sequence .. depends on just the last n-1 locations… Once the n-gram model is trained, we can use it to calculate the probability of all possible next locations given the past n-1 locations…. and see which one is the most likely location.To train the model, we use maximum likelihood estimation on the training sequences to estimate these conditional probability … just by counting. As show in this equation, MLE probability of being in location at time i conditioned on the past n-1 history locations is… just the count of all n sequences in the data divided by the count of all these n-1 sequences. There is one small problem with this approach. Let’s say our model come across a location that has not been seen in the training. It just assumes a zero probability. This may push the system to trigger anomaly alert. Luckily, N-gram model is very robust in handling unseen labels if we use smoothing. Smoothing algorithms such as Katz… are to take some probability mass from the seen locations and reserve them for those unseen locations.
  • To illustrate this process, let’s take a look at an example.The blue curve is the log probability we just described. Let’s say anomaly happens at point A. If we set the threshold lower like the red line, the system will detect the anomaly at point B with a reasonable delay. But if we set the threshold too high like the pink line, we will mistakenly flag an anomaly for a sequence of normal behavior text…. Which is counted towards false positives at points C and D. The way to find the right threshold for different applications is to use receiver-operating-characteristic curve or ROC curve. We will look at this in more details later in the talk.
  • So, this complete the whole system architecture. We have the sensing part that produce RSS traces, we have preprocessing part that convert the traces and other context information to behavior text and we have the modeling training and inference part that is used to do anomaly detection with a design parameter “threshold”
  • Training Stage: Each of the participants uses the phone for 24 hours while the \\emph{SenSec} app is collecting sensory information in the background and build the behavioral $n$-gram model.Positive Testing Stage: In this stage, each participants continue uses her phone for 24 hours. This time the \\emph{SenSec} app is switched to testing mode. It collects the sensors reading the same way as in training mode, but also construct behavior text sequence and feed it to the learned n-gram model. A \\emph{sureness} score is calculated as described in Equation \\ref{eqn_ngram_prob}. If it falls below a preset threshold while certain operation is performed, an authentication screen will be pop up asking user to enter a passcode. The \\emph{sureness} score and the authentication decision are recorded for logging and result reporting purpose.Negative Testing Stage: The phones are given to other participants and let them use it for another 24 hours. As in the previous stage, the same operations are performed on the phone and all authentication events will be record for further analysis. % user 2 negative testing, record labels and results ---- (FN, TP)We examined the logs generated by these experiments. At each authentication decision point, the sureness score is recorded. By varying the threshold, we can evaluate how well our \\emph{SenSec} models perform user authentication under different threshold values. For each threshold value, we can calculate False Positive Rate or FPR and True Positive Rate or TPR and plot Receiver Operating Characteristic (ROC) curves.True Positive Rate (TPR) represents the cases that system successfully detect non-owner access, while False Positive Rate (FPR) represents the false alarms when the owner is detected as a non-owner. To ensure the security of the system while still keeping the usability to a certain level, we need to maximize TPR and minimize FPR. The ROC curves provide a guideline on choosing the right thresholds to fulfill such a requirement. For example, the system may have a requirement to effectively detect 70\\% of the unauthorized access, but it may need to keep the false alarm rate below 15\\%. As shown in Figure \\ref{fig_roc}, we can choose a data point from the allowable region on the ROC curve: threshold 0.7 can achieve 71.3\\% TPR and only 13.1\\% FPR. We also examined the detection delay at this threshold by deliberately passing the phone between the two users more often and recording the time. By comparing these recorded time and the timestamps of triggered anomalies from the log, we found \\emph{SenSec} bears an average 4.96 seconds detection delay.
  • So now lets see what we conclude from this work and the future work we plan to do
  • So now lets see what we conclude from this work and the future work we plan to do
  • That brings me to the end of my presentation. Thank you very much for your attention.
  • ICNC 2013 SenSec Presentation

    1. 1. Jiang Zhu, Pang Wu, Xiao Wang, Joy Ying ZhangCarnegie Mellon UniversityJanuary 31st, 2013 1
    2. 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 continuous n-gram model with these features and use it for calculation of Sureness Scores• Trigger various Authentication Schemes when certain application is launched. 2
    3. 3. 3
    4. 4. 60% • “The 329 organizations polled had collectively lost50% more than 86,000 devices … with average cost of lost40% data at $49,246 per device, worth $2.1 billion or $6.430% 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). 4
    5. 5. Application Password Different applications may have differentA major source of sensitivitiessecurity vulnerabilities.Easy toguess, reuse, forgotten, shared Usability Authentication too-often or sometimes too loose 5
    6. 6. 6
    7. 7. Quantization ClusteringRisk Analysis Sensor Fusion Activity Tree and Segmentation Recognition Certainty of Risk Application Sensitivity < Application Access Control Application Access Control 7
    8. 8. • Human behavior/activities share some common properties with natural languages • Meanings are composed from meanings of building blocks • Exists an underlying structure (grammar) • Expressed as a sequence (time-series)• Apply Statistical NLP to mobile sensory data• Information retrieval, machine translation, text categorization, summarization, prediction 8
    9. 9. • Generative language model: P( English sentence) given a model P(“President Obama has signed the Bill of … ”| Politics ) >> P(“President Obama has signed the Bill of … ” | Sports ) LM reflects the n-gram distribution of the training data: domain, genre, topics.• With labeled behavior text data, we can train a LM for each activity type: “walking”-LM, “running”-LM and classify the activity as 9
    10. 10. • 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
    11. 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 13
    12. 12. • Build n-gram models for M users/classes m=1,2,3…M• Given a behavior text L, we estimate L is generated by the model from user m:• The user classification problem formulated as 14
    13. 13. • A binary classification problem: Classifying a user as the owner â=1 or not â=-1.• Given a behavioral n-gram model• And an observation r, evaluate the probability of a given user is the owner â=1 and check if exceeding a threshold θ:• Given a sequence of behavior text L, and a sensitivity threshold θ, validate if L is generated by user m 15
    14. 14. 0. 8 0. 7Aver age Log Pr obabi l i t y 0. 6 0. 5 0. 4 C D A 0. 3 0. 2 Log Probility B Low Threshold High Threshold 0. 1 0 Sl i di ng W ndow Posi t i on i 16
    15. 15. Sensing Preprocessing Modeling N-gram Model Feature Behavior Text Construction Generation User Classifier Classification User Classifier Binary Authentication Threshold Inference 17
    16. 16. 18
    17. 17. • Accelerometer • Used to summarize acceleration stream • Calculated separately for each dimension [x,y,z,m] • Meta features: Total Time, Window Size• GPS: location string from Google Map API and mobility path• WiFi: SSIDs, RSSIs and path• Applications: Bitmap of well-known applications• Application Traffic Pattern: TCP UDP traffic pattern vectors: [ remote host, port, rate ] 19
    18. 18. • Offline data collection (for training and testing) Pick up the device from a desk Unlock the device using the right slide pattern Invoke Email app from the "Home Screen" Lock the device by pressing the "Power" button Put the device back on the desk 20
    19. 19. 21 21
    20. 20. • 71.3% True-Positive Rate with 13.1% False Positive 22
    21. 21. 23
    22. 22. 24
    23. 23. 25
    24. 24. Quantization Clustering Risk Analysis Sensor Fusion Activity Tree and Segmentation Recognition Certainty of Risk Application Sensitivity < Application Access Control• Experiments to discover anomaly usage with ~80%accuracy with only days of training data 26
    25. 25. • Alpha test in Jun 2012, 1st Google Play Store release in Oct 2012• False Positive: 13% FPR still annoying users sometimes• Use adaptive model • Adding the trace data shortly before a false positive to the training data and update the model• Change passcode validation to sliding pattern• A false positive will grant a “free ride” for a configurable duration • Assumption: just authenticated user should control the device for a given period of time• “Free Ride” period will end immediately if abrupt context change is detected.• Newer version is scheduled to be release in Jan 2013. 27
    26. 26. • 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 28
    27. 27. Thank you.