using identity credential usage logs to detect anomalous service accesses daisuke mashima dr....
TRANSCRIPT
Using Identity Credential Usage Logs to Detect Anomalous Service Accesses
Daisuke Mashima Dr. Mustaque Ahamad
College of Computing
Georgia Institute of TechnologyAtlanta, GA, USA
ACM DIM 2009, Chicago, IL, 2009
2
Increasing Risk of Identity Theft
• Variety of online identity credentials– Passwords, certificates, SSN, credit card
number, etc.– Loss and theft are possible and common
• Consequence of online identity theft– Impersonation– Disclosure of sensitive information– Financial loss
3
To counter such threats…
• Online service providers are required to– Analyze huge amount of log records to identify
suspicious service accesses– Investigate identified records extensively
• In reality…– Significant reliance on human experts– Not processed in real-time basis
• Automated mechanism to monitor identity usage (service accesses) is desired.
4
Outline
• Observations from real data sets
• Our approach
• Anomaly-based risk scoring scheme
• Preliminary evaluation
• Conclusion / Future Work
5
Buzzport Access Log
6
Buzzport Access Log
• Contain only– (Anonymized) User ID– Login timestamp– Logout timestamp
380484533347391, 24/08/2007 14:07:05, 24/08/2007 14:18:46380484533347391, 27/08/2007 08:01:14, 27/08/2007 08:02:54380484533347391, 27/08/2007 08:04:36, 27/08/2007 08:16:05380484533347391, 27/08/2007 12:05:36, 27/08/2007 12:18:15380484533347391, 31/08/2007 14:31:43, 31/08/2007 14:38:08
7
Another data set
• Log records of a portal of online trading company
• The following items are available:– User ID– Coarse Action Type (Login / Logout)– Timestamp– IP Address– Organization Name etc.
8
Observations and Considerations
• Available information is quite limited.– Typical fraud detection systems rely on much
richer information
• Data are not labeled.– Supervised techniques are not available.
• Limited types of events can be observed.– Schemes relying on event sequence or state
transition have limited applicability.
9
Our Approach
• Utilize attributes derived from an individual identity usage record– Timestamp (day-of–week etc.), IP address, etc.– Focus on categorical attributes
• Build user profile based on occurrence frequency of each attribute value
• Determine risk scores based on frequency information
10
User Profile Management
• Defined as a frequency distribution of attribute values (categories)– One profile for one attribute– Multiple profiles can be defined per user.
• Day-of-week profile, hour-of-day profile, and so forth…
• Updated upon receipt of each log record– Simply increment occurrence counters corresponding
to the attribute values in the record
• Data aging can be easily implemented– Periodically multiply all counters with some
decay factor
11
Base Score and Weight
• Base score represents how unlikely an observed user’s access is.– BaseScore = -log (RelativeFrequency)
• Score weight quantifies the “effectiveness” of each attribute for profiling.– When an attribute well characterizes user’s
identity usage pattern, the value should be high.
• How can we quantify it?
12
Score Weight
• Use “distance” between the frequency distribution and uniform distribution as weight– Bhattacharyya Distance etc.– Data aging is necessary.
1 3 5 7 9
11 13 15 17 19 21 23
0
0.05
0.1
0.15
0.2
0.25
RelativeFrequency
Hour of Day
13
Score Aggregation
• Sub Score (a product of a base score and the corresponding weight) are computed.– Sub Score is computed for each profile.
• How can we combine Sub Scores?– Pick the MAX of Sub Scores– Weighted sum of Sub Scores– Others?
１０８ １０９ ９
14
Setting of Experiments
• Buzzport data set
• Profiling attributes– Week of month (5 categories)– Day of week (7 categories)– Hour of Day (24 categories)
• Scale Sub Scores in [0, 100)
• Use MAX of 3 Sub Scores as output
15
Trends of Risk Scores
16
Trends of Risk Scores with Data Aging
• Decay Factor = 0.5 is applied monthly.
17
False Positive / True Positive Analysis
• Randomly pick 5 users with different access frequency
• Split each user’s log records into two:– Test data: last 1 month– Training data: Rest of them
• Analyze False Positive rate by using the same user’s training data and test data
• Analyze True Positive rate by using different users’ data sets (a.k.a Cross Profiling)
18
False Positive / True Positive Results
* Each user’s threshold is determined based on the score range of the training data.
19
Time / Storage Cost
• Measured on Linux PC with Intel Core 2 Duo E6600 and 3GM RAM
• Average time per record: 5ms– Good enough for real-time processing
• Storage space per user: 1.4KB– Potential to accommodate a large number of
users
20
Conclusion
• Defined design principles for risk scoring based on identity usage logs
• Proposed a way to compute anomaly-based risk scores in real-time basis
• Presented a prototype system using time stamp information and showed that it has reasonably good accuracy
21
Future Work
• Investigate other attributes (E.g. location)
• Conduct detailed experiments– Evaluate with other data sets– Find the optimum configuration
• Integrate into other security mechanisms
