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K nowledge- b ased Temporal Abstraction Host-based I ntrusion D etection S ystem for Android. KB-IDS. Academic Advisor: Dr. Yuval Elovici Technical Advisor : Asaf Shabtai Team Members : Eliya Rahamim Elad Ankry Uri Kanonov. Background.
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Knowledge-based Temporal Abstraction Host-based Intrusion Detection System for Android KB-IDS
Academic Advisor: Dr. Yuval Elovici Technical Advisor: AsafShabtai Team Members: EliyaRahamim EladAnkry Uri Kanonov
Background • An IDS is used to detect malicious behaviors that indicates a breach in the security of a computer system • The Knowledge-based Temporal-Abstraction (KBTA) method in which a computational mechanism extracts meaningful conclusions from raw time-stamped data and knowledge. • Android is an operating system for mobile devices, based on the Linux kernel, developed by Google. It allows development of applications in Java, controlling the phone via Google-developed Java libraries.
Problem Domain • In the modern age Smartphones as well as the threats they are susceptible to, are a growing trend • This strengthens the need for sophisticated defense mechanisms to protect them
Current Situation • Mobile devices lack the computational strength needed to support PC-like security solutions • Android, being an open source and open platform introduces new potential risks and types of attacks • Android has some inherent security mechanisms that cannot cope with all possible threats • Due to application sandboxing, conventional methods such as AntiVirus are futile. There is a need for a different solution…
Knowledge-basedTemporal Abstraction • Developed by Prof. Yuval Shahar, 1997 • Time-Stamped Raw Data: • - Primitive Parameters • - Events • Higher Level Meaningful Temporal Information: • - Contexts • - Abstractions • - Temporal Patterns Knowledge (KBTA Security ontology) • Four inference mechanisms: • - Temporal Context Forming • - Contemporaneous Abstraction • - Temporal Interpolation • - Temporal Pattern Matching
Internet Connection Mode Context Worm Pattern I2 I1 KBTA – cont. Patterns TCP Packets Sent State = HIGH Abstractions Contexts High Primitives Medium TCP Packets Sent ( ) Low T1 T2 T3 T0 Events Time Wi-Fi Connection Events ( )
Non-Func. Requirements • Gathering a feature batch (maximum 40) by the agent should take less than 10 seconds. • CPU usage by the HIDS should be under 10% • The HIDS should take at most 10MB on the data partition of the device • The HIDS will be developed in Java using the Android SDK • For demo and testing purposes, a real device will be supplied by DT Labs
Collect features, Analyze Data and Weight Assessments • Primary actors: Android • Description: After a time trigger the agent collects the monitored feature values and sends them to all of the local analysis servers. Each of the servers analyzes the data and outputs a threat assessment. The assessments are weighted by the TWU and if a threat is found, an alert along with any associated data, is dispatched to the agent and the Control Center. • Trigger: A time trigger from Android • Pre-conditions: The agent is installed on the device and is running • Post-conditions: If a threat is found, an alert along with any associated data has been dispatched
Risks • Risk: The HIDS consumes too much CPU • Solution: Reducing the quantity of the features collected by the agent and/or decreasing the collection rate • Risk: The HIDS consumes too much memory • Solution: Reducing the time frame for keeping raw data in the KBTA’s memory • Risk: The HIDS consumes too much bandwidth • Solution: Lessening the amount of data transmitted to and from the Control Center
The End And so Android lived happily ever after…