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Title: Information Flow: A Unified Basis for Vulnerability Mitigation, Malware Defense and Attack Scenario Reconstruction (Keynote Presentation)
Access control and information flow are the two building blocks in the design of secure software. Of the two, access control seems ubiquitous, being widely used in operating systems, databases, firewalls, servers, web applications, and so on. The successes of information flow seem less obvious, and its benefits and potential underappreciated. Yet, when it comes to defending against malicious code, access control based defenses have proved susceptible to evasion, or they end up being so restrictive as to interfere with legitimate use. In this talk, I will argue that defenses based on information flow can be more discerning, as they utilize not only the operations performed but also their context, e.g., whether malicious actors could be exerting control over these operation or their key arguments. I will then describe successful applications of information flow to defend against every stage of a cyber attack campaign, including: (a) exploit mitigation for a wide range of software vulnerabilities, (b) malware containment across diverse OSes, including Linux, BSD, and Windows XP through Windows 10, and (c) attack campaign reconstruction, where we achieve a five to six orders of magnitude data reduction by applying our techniques.
Authors:
Award ID(s):
1918667
Publication Date:
NSF-PAR ID:
10296445
Journal Name:
ACM FEAST
Page Range or eLocation-ID:
1 to 2
Sponsoring Org:
National Science Foundation
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Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. 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