Traffic Analysis Attacks to Encrypted Voip Calls
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ON TRAFFIC ANALYSIS ATTACKS TO ENCRYPTED VOIP CALLS YUANCHAO LU Bachelor of Science in Electrical Engineering Beijing University of Posts and Telecommunications July, 2007 submitted in partial fulfillment of the requirements for the degree MASTER OF SCIENCE IN ELECTRICAL ENGINEERING at the CLEVELAND STATE UNIVERSITY November, 2009 This thesis has been approved for the Department of ELECTRICAL AND COMPUTER ENGINEERING and the College of Graduate Studies by Thesis Committee Chairperson, Dr. Ye Zhu Department/Date Dr. Vijaya K. Konangi Department/Date Dr. Pong P. Chu Department/Date To my loved husband Dawei and my entire family... ACKNOWLEDGMENTS I would like to thank the following people: Dr. Ye Zhu for giving me such a challenging opportunity, for his inspiring direction for the research and for his great support in all aspects of my study. Dr. Vijaya K. Konangi, and Dr. Pong P. Chu, who are on my committee, for their time in reviewing and evaluating this dissertation. Dr. Chansu Yu, Dr. Wenbing Zhao, and Dr. Zhiqiang Gao for their elaborately prepared lectures which imparted knowledge to me, and their perspectives of different things that enriched my view. Dr. Dan Simon for my improvement in English writing. Thank you to my friends for their kind help and friendship. ON TRAFFIC ANALYSIS ATTACKS TO ENCRYPTED VOIP CALLS YUANCHAO LU ABSTRACT The increasing popularity of VoIP telephony has brought a lot of attention and concern over security and privacy issues of VoIP communication. This thesis proposes a new class of traffic analysis attacks to encrypted VoIP calls. The goal of these attacks is to detect speaker or speech of encrypted VoIP calls. The proposed traffic analysis attacks exploit silent suppression, an essential feature of VoIP telephony. These attacks are based on application-level features so that the attacks can detect the same speech or the same speaker of different VoIP calls made with different VoIP codecs. We evaluate the proposed attacks by extensive experiments over different type of networks including commercialized anonymity networks and campus networks. The experiments show that the proposed traffic analysis attacks can detect speaker and speech of encrypted VoIP calls with a high detection rate which is a great improvement comparing with random guess. With the help of intersection attacks, the detection rate for speaker detection can be increased. In order to shield the detrimental effect of this proposed attacks, a countermeasure is proposed to mitigate the proposed traffic analysis attacks. v TABLE OF CONTENTS Page ABSTRACT .................................... v LISTOFTABLES ................................. ix LISTOFFIGURES ................................ x ACRONYM..................................... xii CHAPTER I. INTRODUCTION............................... 1 II. RELATEDWORK.............................. 4 2.1 Low-Latency Anonymity Networks . 4 2.2 TrafficAnalysisAttacks ....................... 5 III. TRAFFIC ANALYSIS ATTACKS ON SIP-BASED ENCRYPTED VOIP CALLS .................................. 6 3.1 Background.............................. 7 3.1.1 SpeechCoding........................ 8 3.1.2 Silence Suppression . 8 3.2 ProblemDefinition.......................... 10 3.2.1 NetworkModel........................ 11 3.2.2 ThreatModel ........................ 11 3.3 Detecting Speeches and Speakers of SIP-Based VoIP Calls . 12 3.3.1 Overview........................... 13 3.3.2 FeatureExtraction. .. .. 13 3.3.3 HMMsTraining ....................... 17 3.3.4 SpeechDetectionandSpeaker Detection. 20 vi 3.3.5 IntersectionAttack . .. .. 21 3.4 Empirical Evaluation . 22 3.4.1 ExperimentSetup ...................... 22 3.4.2 Metrics............................ 23 3.4.3 Threshold Tsilence ...................... 24 3.4.4 LengthofTrainingandTestTraces . 27 3.4.5 PoolSize ........................... 29 3.4.6 Cross-CodecDetection . 30 3.4.7 IntersectionAttack . .. .. 32 3.5 DetectingSpeakerwithoutCandidatePools . 32 3.5.1 DetectionApproach . .. .. 33 3.5.2 PerformanceEvaluation . 34 3.6 ACountermeasureandItsPerformance . 34 3.6.1 Overview........................... 36 3.6.2 Performance Evaluation of the Countermeasure . 37 3.7 DiscussionandFutureWork . 39 IV. TRAFFIC ANALYSIS ATTACKS ON SKYPE VOIP CALLS . 40 4.1 ProblemDefinition.......................... 41 4.1.1 NetworkModel........................ 42 4.1.2 ThreatModel ........................ 42 4.2 Detecting Speech and Speaker of Skype-Based VoIP Calls . 43 4.2.1 Overview........................... 44 4.2.2 FeatureExtraction. .. .. 46 4.2.3 HMMTraining........................ 47 4.2.4 SpeechDetectionandSpeaker Detection. 49 4.2.5 IntersectionAttack . .. .. 49 vii 4.3 Empirical Evaluation . 50 4.3.1 ExperimentSetup ...................... 50 4.3.2 Metrics............................ 51 4.3.3 Effect of Parameter T (Length of Sample Interval) . 54 4.3.4 Effect of Parameter Hpacket (Threshold on Packet Size) . 54 4.3.5 LengthofTrainingTracesandTestTraces . 57 4.3.6 PoolSize ........................... 57 4.3.7 IntersectionAttack . .. .. 59 4.4 ACountermeasureandItsPerformance . 60 4.4.1 Overview........................... 60 4.4.2 Performance Evaluation of The Countermeasure . 62 4.5 DiscussionandFutureWork . 62 V. CONCLUSIONS ............................... 65 BIBLIOGRAPHY ................................. 66 viii LIST OF TABLES Table Page I MajorParametersofG.729BSilenceDetector . 9 II CodecInformation ............................ 22 III Performance of Intersection Attacks Combined with Cross-Codec Speaker Detection ................................. 32 ix LIST OF FIGURES Figure Page 1 An Example of Silence Suppression . 9 2 NetworkModel .............................. 11 3 TheProposedAttack........................... 13 4 Experiment Results on the Threshold Tsilence .............. 15 5 MatchRate Rmatch vs. Threshold Tspurt (µlawCodec)......... 16 6 HMM ................................... 18 7 DetectionRatewithDifferentNumberofStates . 20 8 ExperimentSetup............................. 22 9 Speech Detection Performance with Different Threshold Tsilence ... 25 10 Speaker Detection Performance with Different Threshold Tsilence ... 26 11 Detection Performance with Different Length of Training Traces and TestTraces ................................ 28 12 DetectionPerformancewithDifferentPoolSize . 29 13 Cross-Codec Detection Performance with Different Threshold Tsilence 30 14 Cross-Codec Detection Performance with Different Length of Training TraceandTestTraces .......................... 31 15 Cross-Codec Detection Performance with Different Pool Size . ... 31 16 DetectionPerformance .......................... 35 17 Countermeasure: Camouflaging Alice’s VoIP Packets . 36 18 Performance of the Proposed Countermeasure . .. 38 19 NetworkModel .............................. 42 20 AnExample................................ 44 x 21 StepsofTheProposedAttacks . 46 22 Left-rightHiddenMarkovModel . 47 23 DetectionRatewithDifferentNumberofStates . 48 24 ExperimentSetup............................. 50 25 Effect of Parameter T onSpeechDetection. 52 26 Effect of Parameter T onSpeakerDetection . 53 27 Effect of Parameter Hpacket onSpeechDetection . 55 28 Effect of Parameter Hpacket onSpeakerDetection . 56 29 Detection Rate vs Test Time and Training Time on Speech Detection andSpeakerDetection .......................... 58 30 DetectionPerformancewithDifferentPoolSize . 59 31 PerformanceofIntersectionAttack . 61 32 Countermeasure: Camouflaging Alice’s Skype Packets . 61 33 Performance of the Proposed Countermeasure . .. 63 xi ACRONYM VoIP Voice over Internet Protocol PSTN Public Switched Telephone Networks HMM Hidden Markov Model QoS Quality of Service RTP Real-time Transport Protocol CBR Constant Bit Rate VBR Variable Bit Rate xii CHAPTER I INTRODUCTION This thesis addresses on privacy issues of encrypted Voice over Internet Protocol (VoIP) calls. A class of traffic analysis attacks is proposed to compromise the privacy of encrypted VoIP calls. In this thesis, a countermeasure is proposed and studied to the class of traffic analysis attacks. With the rapid growth of broadband Internet access services, the popularity of VoIP calls has grown significantly. As a competitor with traditional phone services provided over Public Switched Telephone Networks (PSTN), VoIP services are known for their lower cost and richer features. The increasing popularity of VoIP telephony has brought a lot of attention and concern over security and privacy issues of VoIP communication. To protect confidentiality of VoIP calls, advanced users encrypt VoIP packets with Zfone [1] or SRTP [2], the secure version of real time transport protocol. To further protect privacy of VoIP calls, advanced users are using anonymity networks to anonymize VoIP calls. For this purpose, low-latency anonymity networks such as Tor [3] and JAP [4] can be used. The common anonymizing technique used 1 in anonymity networks is rerouting which usually routes packets through a random- selected and usually longer path instead of the shortest path. A class of passive traffic analysis attacks is proposed to compromise privacy of encrypted VoIP calls in this thesis. The procedure of proposed attacks is as follows: First, the adversary collects traces of encrypted VoIP calls made by a victim, say Alice. The adversary then extracts application-level features of Alice’s VoIP calls and trains a Hidden Markov Model (HMM) with the extracted features. To test whether a call of interest is made by Alice, the adversary can extract features from the traces of the call and calculate likelihood