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Cloud systems are integral for delivering scalable and virtualized resources globally. It also provides security updates and monitoring to keep user data safe. However, the growing complexity of these systems poses significant challenges, particularly in the realm of logging and security. It is difficult to know for users which detail is critical for further security analysis of the resources. Also, external packages used in the cloud system require updates by users to mitigate the vulnerability, but the large number of packages to manage makes them outdated versions. This paper shares the weakness of cloud logging systems we observed, which can be exploited by attackers. We propose a tool that configures alerts automatically when commands that have missing details in logs are executed and updates vulnerable versions of packages. Our tool leverages a list that includes the commands with missing details in logs and packages that need to be updated because of the known vulnerabilities. To make the list, we conduct complete enumerating for 1,279 commands in five major resources of Azure to find logs with missing details and search related communities to find vulnerable packages that require the manual update. We evaluate the proposed tool with eight attack scenarios based on real-world cases and the result shows that our tool prevents them successfully. 

Controller Area Network (CAN) is the de-facto standard in-vehicle network system. Despite its wide adoption by automobile manufacturers, the lack of security design makes it vulnerable to attacks. For instance, broadcasting packets without authentication allows the impersonation of electronic control units (ECUs). Prior mitigations, such as message authentication or intrusion detection systems, fail to address the compatibility requirement with legacy ECUs, stealthy and sporadic malicious messaging, or guaranteed attack detection. We propose a novel authentication system called ShadowAuth that overcomes the aforementioned challenges by offering backward-compatible packet authentication to ECUs without requiring ECU firmware source code. Specifically, our authentication scheme provides transparent CAN packet authentication without modifying existing CAN packet definitions (e.g., J1939) via automatic ECU firmware instrumentation technique to locate CAN packet transmission code, and instrument authentication code based on the CAN packet behavioral transmission patterns. ShadowAuth enables vehicles to detect state-of-the-art CAN attacks, such as bus-off and packet injection, responsively within 60ms without false positives. ShadowAuth provides a sound and deployable solution for real-world ECUs. 

A software update is a critical but complicated part of software security. Its delay poses risks due to vulnerabilities and defects of software. Despite the high demand to shorten the update lag and keep the software up-to-date, software updates involve factors such as human behavior, program configurations, and system policies, adding variety in the updates of software. Investigating these factors in a real environment poses significant challenges such as the knowledge of software release schedules from the software vendors and the deployment times of programs in each user’s machine. Obtaining software release plans requires information from vendors which is not typically available to public. On the users’ side, tracking each software’s exact update installation is required to determine the accurate update delay. Currently, a scalable and systematic approach is missing to analyze these two sides’ views of a comprehensive set of software. We performed a long term system-wide study of update behavior for all software running in an enterprise by translating the operating system logs from enterprise machines into graphs of binary executable updates showing their complex, and individualized updates in the environment. Our comparative analysis locates risky machines and software with belated or dormant updates falling behind others within an enterprise without relying on any third-party or domain knowledge, providing new observations and opportunities for improvement of software updates. Our evaluation analyzes real data from 113,675 unique programs used by 774 computers over 3 years. 

Recent advances in the causal analysis can accelerate incident response time, but only after a causal graph of the attack has been constructed. Unfortunately, existing causal graph generation techniques are mainly offline and may take hours or days to respond to investigator queries, creating greater opportunity for attackers to hide their attack footprint, gain persistency, and propagate to other machines. To address that limitation, we present Swift, a threat investigation system that provides high-throughput causality tracking and real-time causal graph generation capabilities. We design an in-memory graph database that enables space-efficient graph storage and online causality tracking with minimal disk operations. We propose a hierarchical storage system that keeps forensically-relevant part of the causal graph in main memory while evicting rest to disk. To identify the causal graph that is likely to be relevant during the investigation, we design an asynchronous cache eviction policy that calculates the most suspicious part of the causal graph and caches only that part in the main memory. We evaluated Swift on a real-world enterprise to demonstrate how our system scales to process typical event loads and how it responds to forensic queries when security alerts occur. Results show that Swift is scalable, modular, and answers forensic queries in real-time even when analyzing audit logs containing tens of millions of events.