THIS IS A CONTINUATION OF N00014-14-1-0440 Novel Side Channel Attacks against Networked Systems: Automated Discovery and Mitigation Solutions
Abstract
Statement of Work:The PI proposes systematic identification of unintentionally revealed internal system and network state. The new class of side channels attacks defined as Differential Introspective Side Channels are summarized network responses that can leak such internal state. Such side channels in disguise only leak seemingly trivial information. Rather than merely reacting to any newly discovered side channels of this class, the proposed work will enable the automated comprehensive discovery of this particular class of side channels which exist due to the network protocols inherent goal for exposing information which support debugging and management.Objective:The objective for this research is to investigate new class of side channel attacks against networked systems such as network stacks that can lead to significant damage to user privacy, network security, and application integrity, called Differential Introspective Side Channel (DISC) attacks.Approach:In this project propose systematic identification of unintentionally revealed internal system and network state. A new class of side channels defined as Differential Introspective Side Channels are summarized that can leak such internal state. The security analysis of the above problem is performed in a rigorous and comprehensive framework consisting of four key steps:1. Measurement (behavior characterization of a target system).2. Identification of sensitive network and system state.3. Identification of relevant Differential Introspective Side Channels.4. Security analysis by connecting the sensitive network and system state and the relevant Differential Introspective Side Channels.Through these steps, techniques built on side channels are described which can enable a wide range of securityapplications to discover, and analyze both new and existing attacks.Overall Merit and ONR Mission/Relevance:This research project will develop novel methods for comprehensively discover the network protocol vulnerability in the Differential Introspective Side Channels class. Differential Introspective Side Channels have direct impact on the security assurance of both small systems such as mobile devices as well as large network systems such as Navy s network-based command and control. Systematically discovering and mitigating this type of vulnerability is highly relevant to the Navy, and positively impacting the potentialfor success for Navy s missions.Progress:We have published several papers at competitive research conferences in computer science systems/networking and security venues:~ Static Detection of Packet Injection Vulnerabilities -- A Case for Identifying Attacker-controlled Implicit Information Leaks by Qi Alfred Chen, Zhiyun Qian, Yunhan Jack Jia, Yuru Roy Shao, Z. Morley Mao, Proceedings of ACM Conference on Computer and Communications Security (CCS) 2015.~ Peeking into Your App without Actually Seeing it: UI State Inference and Novel Android Attacks by Qi Alfred Chen,Zhiyun Qian, and Z. Morley Mao, Proceedings of Usenix Security Symposium 2014.We have made the transition of the technology to TMobile on the defense solutions to address the VoLTE Data Free-Ride Attack, which is an example of exploiting the unprotected voice channel.We summarize the following key results that we believe will lead to promising technology transfers and practical impact:~ The security of smartphone GUI frameworks remains an important yet under--~scrutinized topic. In this work, we report that on the Android system (and likely other OSes), a weaker form of GUI confidentiality can be breached in the form of UI state (not the pixels) by abackground app without requiring any permissions. Our finding leads to a class of attacks which we name UI state inference attack. The underlying problem is that popular GUI frameworks by design can potentially reveal every UI state change through a newly-~discoveredpublic side channel shared memory. In our evaluation, we show that for 6 out of 7 popular Androi
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2016
- Source ID
- N000141612614
Entities
People
- Zijing Mao
Organizations
- Board of Regents of the University of Michigan
- Office of Naval Research
- United States Navy