Proceedings 2019 Network and Distributed System Security Symposium最新文献

{"title":"IoTGuard: Dynamic Enforcement of Security and Safety Policy in Commodity IoT","authors":"Z. Berkay Celik, Gang Tan, P. Mcdaniel","doi":"10.14722/NDSS.2019.23326","DOIUrl":"https://doi.org/10.14722/NDSS.2019.23326","url":null,"abstract":"Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital connectivity has changed the way we live, play, and work. To date, the traditional approach to securing IoT has treated devices individually. However, in practice, it has been recently shown that the interactions among devices are often the real cause of safety and security violations. In this paper, we present IOTGUARD, a dynamic, policy-based enforcement system for IoT, which protects users from unsafe and insecure device states by monitoring the behavior of IoT and triggeraction platform apps. IOTGUARD operates in three phases: (a) implementation of a code instrumentor that adds extra logic to an app’s source code to collect app’s information at runtime, (b) storing the apps’ information in a dynamic model that represents the runtime execution behavior of apps, and (c) identifying IoT safety and security policies, and enforcing relevant policies on the dynamic model of individual apps or sets of interacting apps. We demonstrate IOTGUARD on 20 flawed apps and find that IOTGUARD correctly enforces 12 of the 12 policy violations. In addition, we evaluate IOTGUARD on 35 SmartThings IoT and 30 IFTTT trigger-action platform market apps executed in a simulated smart home. IOTGUARD enforces 11 unique policies and blocks 16 states in six (17.1%) SmartThings and five (16.6%) IFTTT apps. IOTGUARD imposes only 17.3% runtime overhead on an app and 19.8% for five interacting apps. Through this effort, we introduce a rigorously grounded system for enforcing correct operation of IoT devices through systematically identified IoT policies, demonstrating the effectiveness and value of monitoring IoT apps with tools such as IOTGUARD.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89284063","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":"ExSpectre: Hiding Malware in Speculative Execution","authors":"Jack Wampler, Ian Martiny, Eric Wustrow","doi":"10.14722/ndss.2019.23409","DOIUrl":"https://doi.org/10.14722/ndss.2019.23409","url":null,"abstract":"Recently, the Spectre and Meltdown attacks revealed serious vulnerabilities in modern CPU designs, allowing an attacker to exfiltrate data from sensitive programs. These vulnerabilities take advantage of speculative execution to coerce a processor to perform computation that would otherwise not occur, leaking the resulting information via side channels to an attacker. In this paper, we extend these ideas in a different direction, and leverage speculative execution in order to hide malware from both static and dynamic analysis. Using this technique, critical portions of a malicious program’s computation can be shielded from view, such that even a debugger following an instructionlevel trace of the program cannot tell how its results were computed. We introduce ExSpectre, which compiles arbitrary malicious code into a seemingly-benign payload binary. When a separate trigger program runs on the same machine, it mistrains the CPU’s branch predictor, causing the payload program to speculatively execute its malicious payload, which communicates speculative results back to the rest of the payload program to change its real-world behavior. We study the extent and types of execution that can be performed speculatively, and demonstrate several computations that can be performed covertly. In particular, within speculative execution we are able to decrypt memory using AES-NI instructions at over 11 kbps. Building on this, we decrypt and interpret a custom virtual machine language to perform arbitrary computation and system calls in the real world. We demonstrate this with a proof-of-concept dial back shell, which takes only a few milliseconds to execute after the trigger is issued. We also show how our corresponding trigger program can be a preexisting benign application already running on the system, and demonstrate this concept with OpenSSL driven remotely by the attacker as a trigger program. ExSpectre demonstrates a new kind of malware that evades existing reverse engineering and binary analysis techniques. Because its true functionality is contained in seemingly unreachable dead code, and its control flow driven externally by potentially any other program running at the same time, ExSpectre poses a novel threat to state-of-the-art malware analysis techniques.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"117 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79461598","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":"maTLS: How to Make TLS middlebox-aware?","authors":"Hyunwoo Lee, Zach Smith, Junghwan Lim, Gyeongjae Choi, Selin Chun, Taejoong Chung, T. Kwon","doi":"10.14722/ndss.2019.23547","DOIUrl":"https://doi.org/10.14722/ndss.2019.23547","url":null,"abstract":"Middleboxes are widely deployed in order to enhance security and performance in networking. As communication over TLS becomes increasingly common, however, the end-to-end channel model of TLS undermines the efficacy of middleboxes. Existing solutions, such as ‘SplitTLS’, which intercepts TLS sessions, often introduce significant security risks by installing a custom root certificate or sharing a private key. Many studies have confirmed security vulnerabilities when combining TLS with middleboxes, which include certificate validation failures, use of obsolete ciphersuites, and unwanted content modification. To address the above issues, we introduce a middlebox-aware TLS protocol, dubbed maTLS, which allows middleboxes to participate in the TLS session in a visible and auditable fashion. Every participating middlebox now splits a session into two segments with their own security parameters in collaboration with the two endpoints. The maTLS protocol is designed to authenticate the middleboxes to verify the security parameters of segments, and to audit the middleboxes’ write operations. Thus, security of the session is ensured. We prove the security model of maTLS by using Tamarin, a state-of-theart security verification tool. We also carry out testbed-based experiments to show that maTLS achieves the above security goals with marginal overhead.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89891685","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":"Automating Patching of Vulnerable Open-Source Software Versions in Application Binaries","authors":"Ruian Duan, Ashish Bijlani, Yang Ji, Omar Alrawi, Yiyuan Xiong, Moses Ike, Brendan Saltaformaggio, Wenke Lee","doi":"10.14722/ndss.2019.23126","DOIUrl":"https://doi.org/10.14722/ndss.2019.23126","url":null,"abstract":"Mobile application developers rely heavily on opensource software (OSS) to offload common functionalities such as the implementation of protocols and media format playback. Over the past years, several vulnerabilities have been found in popular open-source libraries like OpenSSL and FFmpeg. Mobile applications that include such libraries inherit these flaws, which make them vulnerable. Fortunately, the open-source community is responsive and patches are made available within days. However, mobile application developers are often left unaware of these flaws. The App Security Improvement Program (ASIP) is a commendable effort by Google to notify application developers of these flaws, but recent work has shown that many developers do not act on this information. Our work addresses vulnerable mobile applications through automatic binary patching from source patches provided by the OSS maintainers and without involving the developers. We propose novel techniques to overcome difficult challenges like patching feasibility analysis, source-code-to-binary-code matching, and in-memory patching. Our technique uses a novel variabilityaware approach, which we implement as OSSPATCHER. We evaluated OSSPATCHER with 39 OSS and a collection of 1,000 Android applications using their vulnerable versions. OSSPATCHER generated 675 function-level patches that fixed the affected mobile applications without breaking their binary code. Further, we evaluated 10 vulnerabilities in popular apps such as Chrome with public exploits, which OSSPATCHER was able to mitigate and thwart their exploitation.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88284969","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":"The Crux of Voice (In)Security: A Brain Study of Speaker Legitimacy Detection","authors":"Ajaya Neupane, Nitesh Saxena, Leanne M. Hirshfield, Sarah E. Bratt","doi":"10.14722/ndss.2019.23206","DOIUrl":"https://doi.org/10.14722/ndss.2019.23206","url":null,"abstract":"A new generation of scams has emerged that uses voice impersonation to obtain sensitive information, eavesdrop over voice calls and extort money from unsuspecting human users. Research demonstrates that users are fallible to voice impersonation attacks that exploit the current advancement in speech synthesis. In this paper, we set out to elicit a deeper understanding of such human-centered “voice hacking” based on a neuro-scientific methodology (thereby corroborating and expanding the traditional behavioral-only approach in significant ways). Specifically, we investigate the neural underpinnings of voice security through functional near-infrared spectroscopy (fNIRS), a cutting-edge neuroimaging technique, that captures neural signals in both temporal and spatial domains. We design and conduct an fNIRS study to pursue a thorough investigation of users’ mental processing related to speaker legitimacy detection – whether a voice sample is rendered by a target speaker, a different other human speaker or a synthesizer mimicking the speaker. We analyze the neural activity associated within this task as well as the brain areas that may control such activity. Our key insight is that there may be no statistically significant differences in the way the human brain processes the legitimate speakers vs. synthesized speakers, whereas clear differences are visible when encountering legitimate vs. different other human speakers. This finding may help to explain users’ susceptibility to synthesized attacks, as seen from the behavioral self-reported analysis. That is, the impersonated synthesized voices may seem indistinguishable from the real voices in terms of both behavioral and neural perspectives. In sharp contrast, prior studies showed subconscious neural differences in other real vs. fake artifacts (e.g., paintings and websites), despite users failing to note these differences behaviorally. Overall, our work dissects the fundamental neural patterns underlying voice-based insecurity and reveals users’ susceptibility to voice synthesis attacks at a biological level. We believe that this could be a significant insight for the security community suggesting that the human detection of voice synthesis attacks may not improve over time, especially given that voice synthesis techniques will likely continue to improve, calling for the design of careful machine-assisted techniques to help humans counter these attacks. *Work done while being a student at UAB","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77361276","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":"Countering Malicious Processes with Process-DNS Association","authors":"Suphannee Sivakorn, Kangkook Jee, Yixin Sun, Lauri Korts-Pärn, Zhichun Li, C. Lumezanu, Zhenyu Wu, L. Tang, Ding Li","doi":"10.14722/ndss.2019.23012","DOIUrl":"https://doi.org/10.14722/ndss.2019.23012","url":null,"abstract":"","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87160128","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":"Statistical Privacy for Streaming Traffic","authors":"Xiaokuan Zhang, Jihun Hamm, M. Reiter, Yinqian Zhang","doi":"10.14722/ndss.2019.23210","DOIUrl":"https://doi.org/10.14722/ndss.2019.23210","url":null,"abstract":"Machine learning empowers traffic-analysis attacks that breach users’ privacy from their encrypted traffic. Recent advances in deep learning drastically escalate such threats. One prominent example demonstrated recently is a traffic-analysis attack against video streaming by using convolutional neural networks. In this paper, we explore the adaption of techniques previously used in the domains of adversarial machine learning and differential privacy to mitigate the machine-learning-powered analysis of streaming traffic. Our findings are twofold. First, constructing adversarial samples effectively confounds an adversary with a predetermined classifier but is less effective when the adversary can adapt to the defense by using alternative classifiers or training the classifier with adversarial samples. Second, differential-privacy guarantees are very effective against such statistical-inference-based traffic analysis, while remaining agnostic to the machine learning classifiers used by the adversary. We propose two mechanisms for enforcing differential privacy for encrypted streaming traffic, and evaluate their security and utility. Our empirical implementation and evaluation suggest that the proposed statistical privacy approaches are promising solutions in the underlying scenarios.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91204376","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":"BadBluetooth: Breaking Android Security Mechanisms via Malicious Bluetooth Peripherals","authors":"Fenghao Xu, Wenrui Diao, Zhou Li, Jiongyi Chen, Kehuan Zhang","doi":"10.14722/ndss.2019.23482","DOIUrl":"https://doi.org/10.14722/ndss.2019.23482","url":null,"abstract":"—Bluetooth is a widely used communication tech- nology, especially under the scenarios of mobile computing and Internet of Things. Once paired with a host device, a Bluetooth device then can exchange commands and data, such as voice, keyboard/mouse inputs, network, blood pressure data, and so on, with the host. Due to the sensitivity of such data and commands, some security measures have already been built into the Bluetooth protocol, like authentication, encryption, authorization, etc. However, according to our studies on the Bluetooth protocol as well as its implementation on Android system, we find that there are still some design flaws which could lead to serious security consequences. For example, it is found that the authentication process on Bluetooth profiles is quite inconsistent and coarse- grained: if a paired device changes its profile, it automatically gets trust and users would not be notified. Also, there is no strict verification on the information provided by the Bluetooth device itself, so that a malicious device can deceive a user by changing its name, profile information, and icon to be displayed on the screen. To better understand the problem, we performed a systematic study over the Bluetooth profiles and presented three attacks to demonstrate the feasibility and potential damages of such Bluetooth design flaws. The attacks were implemented on a Raspberry Pi 2 device and evaluated with different Android OS versions ranging from 5.1 to the latest 8.1. The results showed adversaries could bypass existing protections of Android (e.g., permissions, isolations, etc.), launch Man-in-the-Middle attack, control the victim apps and system, steal sensitive information, etc. To mitigate such threats, a new Bluetooth validation mechanism was proposed. We implemented the prototype system based on the AOSP project and deployed it on a Google Pixel 2 phone for evaluation. The experiment showed our solution could effectively prevent the attacks.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"307 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77373917","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":"Quantity vs. Quality: Evaluating User Interest Profiles Using Ad Preference Managers","authors":"M. Bashir, U. Farooq, Maryam Shahid, Muhammad Fareed Zaffar, Christo Wilson","doi":"10.14722/ndss.2019.23392","DOIUrl":"https://doi.org/10.14722/ndss.2019.23392","url":null,"abstract":"—Widely reported privacy issues concerning major online advertising platforms (e.g., Facebook) have heightened concerns among users about the data that is collected about them. However, while we have a comprehensive understanding who collects data on users, as well as how tracking is implemented, there is still a significant gap in our understanding: what information do advertisers actually infer about users, and is this information accurate? In this study, we leverage Ad Preference Managers ( APMs ) as a lens through which to address this gap. APMs are transparency tools offered by some advertising platforms that allow users to see the interest profiles that are constructed about them. We recruited 220 participants to install an IRB approved browser extension that collected their interest profiles from four APMs (Google, Facebook, Oracle BlueKai, and Neilsen eXelate), as well as behavioral and survey data. We use this data to analyze the size and correctness of interest profiles, compare their composition across the four platforms, and investigate the origins of the data underlying these profiles.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76566509","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":"Giving State to the Stateless: Augmenting Trustworthy Computation with Ledgers","authors":"Gabriel Kaptchuk, M. Green, Ian Miers","doi":"10.14722/ndss.2019.23060","DOIUrl":"https://doi.org/10.14722/ndss.2019.23060","url":null,"abstract":"In this work we investigate new computational properties that can be achieved by combining stateless trusted devices with public ledgers. We consider a hybrid paradigm in which a client-side device (such as a co-processor or trusted enclave) performs secure computation, while interacting with a public ledger via a possibly malicious host computer. We explore both the constructive and potentially destructive implications of such systems. We first show that this combination allows for the construction of stateful interactive functionalities (including general computation) even when the device has no persistent storage; this allows us to build sophisticated applications using inexpensive trusted hardware or even pure cryptographic obfuscation techniques. We further show how to use this paradigm to achieve censorship-resistant communication with a network, even when network communications are mediated by a potentially malicious host. Finally we describe a number of practical applications that can be achieved today. These include the synchronization of private smart contracts; rate limited mandatory logging; strong encrypted backups from weak passwords; enforcing fairness in multi-party computation; and destructive applications such as autonomous ransomware, which allows for payments without an online party.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81000259","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}