2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)最新文献_第3页

Detecting Passive Cheats in Online Games via Performance-Skillfulness Inconsistency 通过性能-技巧不一致性检测网络游戏中的被动作弊

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-26 DOI: 10.1109/DSN.2017.20

Daiping Liu, Xing Gao, Mingwei Zhang, Haining Wang, A. Stavrou

{"title":"Detecting Passive Cheats in Online Games via Performance-Skillfulness Inconsistency","authors":"Daiping Liu, Xing Gao, Mingwei Zhang, Haining Wang, A. Stavrou","doi":"10.1109/DSN.2017.20","DOIUrl":"https://doi.org/10.1109/DSN.2017.20","url":null,"abstract":"As the most commonly used bots in first-person shooter (FPS) online games, aimbots are notoriously difficult to detect because they are completely passive and resemble excellent honest players in many aspects. In this paper, we conduct the first field measurement study to understand the status quo of aimbots and how they play in the wild. For data collection purpose, we devise a novel and generic technique called baittarget to accurately capture existing aimbots from the two most popular FPS games. Our measurement reveals that cheaters who use aimbots cannot play as skillful as excellent honest players in all aspects even though aimbots can help them to achieve very high shooting performance. To characterize the unskillful and blatant nature of cheaters, we identify seven features, of which six are novel, and these features cannot be easily mimicked by aimbots. Leveraging this set of features, we propose an accurate and robust server-side aimbot detector called AimDetect. The core of AimDetect is a cascaded classifier that detects the inconsistency between performance and skillfulness of aimbots. We evaluate the efficacy and generality of AimDetect using the real game traces. Our results show that AimDetect can capture almost all of the aimbots with very few false positives and minor overhead.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130562407","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}

引用次数: 8

What Can We Learn from Four Years of Data Center Hardware Failures? 我们能从四年的数据中心硬件故障中学到什么?

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-26 DOI: 10.1109/DSN.2017.26

Guosai Wang, Lifei Zhang, W. Xu

引用次数: 93

IM-Visor: A Pre-IME Guard to Prevent IME Apps from Stealing Sensitive Keystrokes Using TrustZone IM-Visor:一个Pre-IME警卫，以防止IME应用程序窃取使用TrustZone的敏感按键

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-01 DOI: 10.1109/DSN.2017.12

Chen Tian, Yazhe Wang, Peng Liu, Qihui Zhou, Cheng Zhang, Zhen Xu

{"title":"IM-Visor: A Pre-IME Guard to Prevent IME Apps from Stealing Sensitive Keystrokes Using TrustZone","authors":"Chen Tian, Yazhe Wang, Peng Liu, Qihui Zhou, Cheng Zhang, Zhen Xu","doi":"10.1109/DSN.2017.12","DOIUrl":"https://doi.org/10.1109/DSN.2017.12","url":null,"abstract":"Third-party IME (Input Method Editor) apps are often the preference means of interaction for Android users' input. In this paper, we first discuss the insecurity of IME apps, including the Potentially Harmful Apps (PHA) and malicious IME apps, which may leak users' sensitive keystrokes. The current defense system, such as I-BOX, is vulnerable to the prefix-substitution attack and the colluding attack due to the post-IME nature. We provide a deeper understanding that all the designs with the post-IME nature are subject to the prefix-substitution and colluding attacks. To remedy the above post-IME system's flaws, we propose a new idea, pre-IME, which guarantees that \"Is this touch event a sensitive keystroke?\" analysis will always access user touch events prior to the execution of any IME app code. We designed an innovative TrustZone-based framework named IM-Visor which has the pre-IME nature. Specifically, IM-Visor creates the isolation environment named STIE as soon as a user intends to type on a soft keyboard, then the STIE intercepts, translates and analyzes the user's touch input. If the input is sensitive, the translation of keystrokes will be delivered to user apps through a trusted path. Otherwise, IM-Visor replays non-sensitive keystroke touch events for IME apps or replays non-keystroke touch events for other apps. A prototype of IM-Visor has been implemented and tested with several most popular IMEs. The experimental results show that IM-Visor has small runtime overheads.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127992629","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}

引用次数: 6

RL-BLH: Learning-Based Battery Control for Cost Savings and Privacy Preservation for Smart Meters RL-BLH:基于学习的智能电表成本节约和隐私保护电池控制

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-01 DOI: 10.1109/DSN.2017.16

Jinkyu Koo, Xiaojun Lin, S. Bagchi

引用次数: 10

REMAX: Reachability-Maximizing P2P Detection of Erroneous Readings in Wireless Sensor Networks REMAX:可达性-最大限度地P2P检测的错误读数在无线传感器网络

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-01 DOI: 10.1109/DSN.2017.52

V. Krishna, Michael J. Rausch, Benjamin E. Ujcich, Indranil Gupta, W. Sanders

引用次数: 2

Analysing Selfishness Flooding with SEINE 用塞纳河分析自私泛滥

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-01 DOI: 10.1109/DSN.2017.51

Guido Lena Cota, Sonia Ben Mokhtar, G. Gianini, E. Damiani, J. Lawall, Gilles Muller, L. Brunie

{"title":"Analysing Selfishness Flooding with SEINE","authors":"Guido Lena Cota, Sonia Ben Mokhtar, G. Gianini, E. Damiani, J. Lawall, Gilles Muller, L. Brunie","doi":"10.1109/DSN.2017.51","DOIUrl":"https://doi.org/10.1109/DSN.2017.51","url":null,"abstract":"Selfishness is one of the key problems that confronts developers of cooperative distributed systems (e.g., file-sharing networks, voluntary computing). It has the potential to severely degrade system performance and to lead to instability and failures. Current techniques for understanding the impact of selfish behaviours and designing effective countermeasures remain manual and time-consuming, requiring multi-domain expertise. To overcome these difficulties, we propose SEINE, a simulation framework for rapid modelling and evaluation of selfish behaviours in a cooperative system. SEINE relies on a domain-specific language (SEINE-L) for specifying selfishness scenarios, and provides semi-automatic support for their implementation and study in a state-of-the-art simulator. We show in this paper that (1) SEINE-L is expressive enough to specify fifteen selfishness scenarios taken from the literature, (2) SEINE is accurate in predicting the impact of selfishness compared to real experiments, and (3) SEINE substantially reduces the development effort compared to traditional manual approaches.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114688735","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}

引用次数: 5

What You See is Not What You Get! Thwarting Just-in-Time ROP with Chameleon 你看到的不是你得到的!用变色龙挫败准时制ROP

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-01 DOI: 10.1109/DSN.2017.47

Ping Chen, Jun Xu, Zhi-hao Hu, Xinyu Xing, Minghui Zhu, Bing Mao, Peng Liu

{"title":"What You See is Not What You Get! Thwarting Just-in-Time ROP with Chameleon","authors":"Ping Chen, Jun Xu, Zhi-hao Hu, Xinyu Xing, Minghui Zhu, Bing Mao, Peng Liu","doi":"10.1109/DSN.2017.47","DOIUrl":"https://doi.org/10.1109/DSN.2017.47","url":null,"abstract":"Address space randomization has long been used for counteracting code reuse attacks, ranging from conventional ROP to sophisticated Just-in-Time ROP. At the high level, it shuffles program code in memory and thus prevents malicious ROP payload from performing arbitrary operations. While effective in mitigating attacks, existing randomization mechanisms are impractical for real-world applications and systems, especially considering the significant performance overhead and potential program corruption incurred by their implementation. In this paper, we introduce CHAMELEON, a practical defense mechanism that hinders code reuse attacks, particularly Just-in-Time ROP attacks. Technically speaking, CHAMELEON instruments program code, randomly shuffles code page addresses and minimizes the attack surface exposed to adversaries. While this defense mechanism follows in the footprints of address space randomization, our design principle focuses on using randomization to obstruct code page disclosure, making the ensuing attacks infeasible. We implemented a prototype of CHAMELEON on Linux operating system and extensively experimented it in different settings. Our theoretical and empirical evaluation indicates the effectiveness and efficiency of CHAMELEON in thwarting Just-in-Time ROP attacks.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133273985","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}

引用次数: 7

DynaMiner: Leveraging Offline Infection Analytics for On-the-Wire Malware Detection DynaMiner:利用离线感染分析进行在线恶意软件检测

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-01 DOI: 10.1109/DSN.2017.54

Birhanu Eshete, V. Venkatakrishnan

{"title":"DynaMiner: Leveraging Offline Infection Analytics for On-the-Wire Malware Detection","authors":"Birhanu Eshete, V. Venkatakrishnan","doi":"10.1109/DSN.2017.54","DOIUrl":"https://doi.org/10.1109/DSN.2017.54","url":null,"abstract":"Web-borne malware continues to be a major threat on the Web. At the core of malware infection are for-crime toolkits that exploit vulnerabilities in browsers and their extensions. When a victim host gets infected, the infection dynamics is often buried in benign traffic, which makes the task of inferring malicious behavior a non-trivial exercise. In this paper, we leverage web conversation graph analytics to tap into the rich dynamics of the interaction between a victim and malicious host(s) without the need for analyzing exploit payload. Based on insights derived from infection graph analytics, we formulate the malware detection challenge as a graph-analytics based learning problem. The key insight of our approach is the payload-agnostic abstraction and comprehensive analytics of malware infection dynamics pre-, during-, and post-infection. Our technique leverages 3 years of infection intelligence spanning 9 popular exploit kit families. Our approach is implemented in a tool called DynaMiner and evaluated on infection and benign HTTP traffic. DynaMiner achieves a 97.3% true positive rate with false positive rate of 1.5%. Our forensic and live case studies suggest the effectiveness of comprehensive graph abstraction malware infection. In some instances, DynaMiner detected unknown malware 11 days earlier than existing AV engines.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114080667","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}

引用次数: 5

Counting in the Dark: DNS Caches Discovery and Enumeration in the Internet 在黑暗中计数:互联网中的DNS缓存发现和枚举

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-01 DOI: 10.1109/DSN.2017.63

Amit Klein, Haya Schulmann, M. Waidner

引用次数: 14

Fast Atomic Multicast 快速原子组播

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2017-06-01 DOI: 10.1109/DSN.2017.15

Paulo R. Coelho, Nicolas Schiper, F. Pedone

引用次数: 15