2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)最新文献

{"title":"BigMap: Future-proofing Fuzzers with Efficient Large Maps","authors":"Alif Ahmed, Jason Hiser, A. Nguyen-Tuong, J. Davidson, K. Skadron","doi":"10.1109/DSN48987.2021.00062","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00062","url":null,"abstract":"Coverage-guided fuzzing is a powerful technique for finding security vulnerabilities and latent bugs in software. Such fuzzers usually store the coverage information in a small bitmap. Hash collision within this bitmap is a well-known issue and can reduce fuzzers’ ability to discover potential bugs. Prior works noted that collision mitigation with naïvely enlarging the hash space leads to an unacceptable runtime overhead. This paper describes BigMap, a two-level hashing scheme that enables using an arbitrarily large coverage_bitmap with low overhead. The key observation is that the overhead stems from frequent operations performed on the full bitmap, although only a fraction of the map is actively used. BigMap condenses these scattered active regions on a second bitmap and limits the operations only on that condensed area. We implemented our approach on top of the popular fuzzer AFL and conducted experiments on 19 benchmarks from FuzzBench and OSS-Fuzz. The results indicate that BigMap does not suffer from increased runtime overhead even with large map sizes. Compared to AFL, BigMap achieved an average of 4.5x higher test case generation throughput for a 2MB map and 33.1x for an 8MB map. The throughput gain for the 2MB map increased further to 9.2x with parallel fuzzing sessions, indicating superior scalability of BigMap. More importantly, BigMap’s compatibility with most coverage metrics, along with its efficiency on bigger maps, enabled exploring aggressive compositions of expensive coverage metrics and fuzzing algorithms, uncovering 33% more unique crashes. BigMap makes using large bitmaps practical and enables researchers to explore a wider design space of coverage metrics","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132495289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WazaBee: attacking Zigbee networks by diverting Bluetooth Low Energy chips","authors":"Romain Cayre, Florent Galtier, G. Auriol, V. Nicomette, M. Kaâniche, G. Marconato","doi":"10.1109/DSN48987.2021.00049","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00049","url":null,"abstract":"This paper discusses the security of wireless communication protocols of the Internet of Things (IoT) and presents a new attack targeting these protocols, called WazaBee, which could have a critical impact and be difficult to detect. Specifically, WazaBee is a pivotal attack aimed at hijacking BLE devices, commonly used in IoT networks, in order to communicate with and possibly attack through a different wireless network technology, considering protocols based on 802.15.4, in particular Zigbee. We present the key principles of the attack and describe some real-world experiments that allowed us to demonstrate its practical feasibility. The attack takes advantage of the compatibility that exists between the two modulation techniques used by these two protocols. Finally, the paper briefly discusses possible countermeasures to mitigate the impact of this attack.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130957582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Horus: Non-Intrusive Causal Analysis of Distributed Systems Logs","authors":"Francisco Neves, Nuno Machado, R. Vilaça, J. Pereira","doi":"10.1109/DSN48987.2021.00035","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00035","url":null,"abstract":"Logs are still the primary resource for debugging distributed systems executions. Complexity and heterogeneity of modern distributed systems, however, make log analysis extremely challenging. First, due to the sheer amount of messages, in which the execution paths of distinct system components appear interleaved. Second, due to unsynchronized physical clocks, simply ordering the log messages by timestamp does not suffice to obtain a causal trace of the execution. To address these issues, we present Horus, a system that enables the refinement of distributed system logs in a causally-consistent and scalable fashion. Horus leverages kernel-level probing to capture events for tracking causality between application-level logs from multiple sources. The events are then encoded as a directed acyclic graph and stored in a graph database, thus allowing the use of rich query languages to reason about runtime behavior. Our case study with TrainTicket, a ticket booking application with 40+ microservices, shows that Horus surpasses current widely-adopted log analysis systems in pinpointing the root cause of anomalies in distributed executions. Also, we show that Horus builds a causally-consistent log of a distributed execution with much higher performance (up to 3 orders of magnitude) and scalability than prior state-of-the-art solutions. Finally, we show that Horus’ approach to query causality is up to 30 times faster than graph database built-in traversal algorithms.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123526190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"William C. Carter Award","authors":"","doi":"10.1109/dsn48987.2021.00012","DOIUrl":"https://doi.org/10.1109/dsn48987.2021.00012","url":null,"abstract":"","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125432991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PID-Piper: Recovering Robotic Vehicles from Physical Attacks","authors":"Pritam Dash, Guanpeng Li, Zitao Chen, Mehdi Karimibiuki, K. Pattabiraman","doi":"10.1109/DSN48987.2021.00020","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00020","url":null,"abstract":"Robotic Vehicles (RV) rely extensively on sensor inputs to operate autonomously. Physical attacks such as sensor tampering and spoofing can feed erroneous sensor measurements to deviate RVs from their course and result in mission failures. In this paper, we present PID-Piper, a novel framework for automatically recovering RVs from physical attacks. We use machine learning (ML) to design an attack resilient Feed-Forward Controller (FFC), which runs in tandem with the RV’s primary controller and monitors it. Under attacks, the FFC takes over from the RV’s primary controller to recover the RV, and allows the RV to complete its mission successfully. Our evaluation on 6 RV systems including 3 real RVs shows that PID-Piper achieves high accuracy in emulating the RV’s controller, in the absence of attacks, with no false positives. Further, PID-Piper allows RVs to complete their missions successfully despite attacks in 83% of the cases, while incurring low performance overheads.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134028929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment Analysis based Error Detection for Large-Scale Systems","authors":"K. Alharthi, A. Jhumka, S. Di, F. Cappello, Edward Chuah","doi":"10.1109/DSN48987.2021.00037","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00037","url":null,"abstract":"Today’s large-scale systems such as High Performance Computing (HPC) Systems are designed/utilized towards exascale computing, inevitably decreasing its reliability due to the increasing design complexity. HPC systems conduct extensive logging of their execution behaviour. In this paper, we leverage the inherent meaning behind the log messages and propose a novel sentiment analysis-based approach for the error detection in large-scale systems, by automatically mining the sentiments in the log messages. Our contributions are four-fold. (1) We develop a machine learning (ML) based approach to automatically build a sentiment lexicon, based on the system log message templates. (2) Using the sentiment lexicon, we develop an algorithm to detect system errors. (3) We develop an algorithm to identify the nodes and components with erroneous behaviors, based on sentiment polarity scores. (4) We evaluate our solution vs. other state-of-the-art machine/deep learning algorithms based on three representative supercomputers’ system logs. Experiments show that our error detection algorithm can identify error messages with an average MCC score and f-score of 91% and 96% respectively, while state of the art ML/deep learning model (LSTM) obtains only 67% and 84%. To the best of our knowledge, this is the first work leveraging the sentiments embedded in log entries of large-scale systems for system health analysis.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"3 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123740799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glitching Demystified: Analyzing Control-flow-based Glitching Attacks and Defenses","authors":"Chad Spensky, Aravind Machiry, N. Burow, Hamed Okhravi, Rick Housley, Zhongshu Gu, H. Jamjoom, C. Kruegel, G. Vigna","doi":"10.1109/DSN48987.2021.00051","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00051","url":null,"abstract":"Hardware fault injection, or glitching, attacks can compromise the security of devices even when no software vulnerabilities exist. Attempts to analyze the hardware effects of glitching are subject to the Heisenberg effect and there is typically a disconnect between what people “think” is possible and what is actually possible with respect to these attacks. In this work, we attempt to provide some clarity to the impacts of attacks and defenses for control-flow modification through glitching. First, we introduce a glitching emulation framework, which provides a scalable playground to test the effects of bit flips on specific instruction set architectures (ISAs) (i.e., the fault tolerance of the instruction encoding). Next, we examine real glitching experiments using the ChipWhisperer, a popular microcontroller using open-source glitching hardware. These real-world experiments provide novel insights into how glitching attacks are realized and might be defended against in practice. Finally, we present GLITCHRESISTOR, an open-source, software-based glitching defense tool that can automatically insert glitching defenses into any existing source code, in an architecture-independent way. We evaluated GLITCHRESISTOR, which integrates numerous software-only defenses against powerful and real-world glitching attacks. Our findings indicate that software-only defenses can be implemented with acceptable run-time and size overheads, while completely mitigating some single-glitch attacks, minimizing the likelihood of a successful multi-glitch attack (i.e., a success rate of 0.000306%), and detecting failed glitching attempts at a high rate (between 79.2% and 100%).","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122775454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statically Detecting JavaScript Obfuscation and Minification Techniques in the Wild","authors":"Marvin Moog, M. Demmel, M. Backes, Aurore Fass","doi":"10.1109/DSN48987.2021.00065","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00065","url":null,"abstract":"JavaScript is both a popular client-side programming language and an attack vector. While malware developers transform their JavaScript code to hide its malicious intent and impede detection, well-intentioned developers also transform their code to, e.g., optimize website performance. In this paper, we conduct an in-depth study of code transformations in the wild. Specifically, we perform a static analysis of JavaScript files to build their Abstract Syntax Tree (AST), which we extend with control and data flows. Subsequently, we define two classifiers, benefitting from AST-based features, to detect transformed samples along with specific transformation techniques. Besides malicious samples, we find that transforming code is increasingly popular on Node.js libraries and client-side JavaScript, with, e.g., 90% of Alexa Top 10k websites containing a transformed script. This way, code transformations are no indicator of maliciousness. Finally, we showcase that benign code transformation techniques and their frequency both differ from the prevalent malicious ones.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128507903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decamouflage: A Framework to Detect Image-Scaling Attacks on CNN","authors":"Bedeuro Kim, A. Abuadbba, Yansong Gao, Yifeng Zheng, Muhammad Ejaz Ahmed, S. Nepal, Hyoungshick Kim","doi":"10.1109/DSN48987.2021.00023","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00023","url":null,"abstract":"Image-scaling is a typical operation that processes the input image before feeding it into convolutional neural network models. However, it is vulnerable to the newly revealed image-scaling attack. This work presents an image-scaling attack detection framework, Decamouflage, consisting of three independent detection methods: scaling, filtering, and steganalysis, to detect the attack through examining distinct image characteristics. Decamouflage has a pre-determined detection threshold that is generic. More precisely, as we have validated, the threshold determined from one dataset is also applicable to other different datasets. Extensive experiments show that Decamouflage achieves detection accuracy of 99.9% and 98.5% in the white-box and the black-box settings, respectively. We also measured its running time overhead on a PC with an Intel i5 CPU and 8GB RAM. The experimental results show that image-scaling attacks can be detected in milliseconds. Moreover, Decamouflage is highly robust against adaptive image-scaling attacks (e.g., attack image size variances).","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127001231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Aware Security Monitoring against Structural Integrity Attacks in 3D Printers","authors":"Sriharsha Etigowni, Sizhuang Liang, S. Zonouz, R. Beyah","doi":"10.1109/DSN48987.2021.00060","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00060","url":null,"abstract":"STereoLithography (STL) files describe the geometry of objects to be printed in additive manufacturing. Previous studies have shown that the STL files that describe functional objects can be attacked such that the objects appear normal during inspection, but fail during operation. Such attacks lead to damage to systems that use the objects and possibly loss of life. The detection of any defects caused due to the attacks nowadays is limited to the quality control process after the objects are manufactured.We present a Trusted Integrity Verifier (TIV) to detect such attacks on 3D printed objects in the early stage of the manufacturing process. These type of new attacks cannot be detected by traditional software security mechanisms since they only focus on the printers and do not consider the inputs (STL design files) to the printer. Early detection of attacks prevents from printing malicious objects resulting in saving time, resources and manufacturing efforts. TIV detects malicious STL files using multidisciplinary approaches unlike the traditional integrity verification techniques. TIV develops a void detection module based on computer vision techniques to identify the internal defects such as voids. Some of these features could be from the design and some could be due to the attack. To differentiate the malicious features from the design features, TIV develops safety verification module based on a numerical method. TIV’s safety verification module is used to differentiate the malicious features from the design features by calculating the load bearing mechanical stress on the objects. These mechanical stresses are compared to the safety operational conditions to determine if the printed object will break or fail during its normal operation.To illustrate TIV’s generality and scalability, we conducted a large-scale analysis on 16,000 real-world 3D print STL files. TIV verified the STL files successfully as either safe or malicious with high accuracy of 92% for object classification and 96.5% for void detection.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133634762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}