Timing Analysis of Keystrokes and Timing Attacks on SSH
`
`David Wagner
`Dawn Xiaodong Song
`University of California, Berkeley
`
`Xuqing Tian
`
`Abstract
`
`1 Introduction
`
`SSH is designed to provide a secure channel between
`two hosts. Despite the encryption and authentication
`mechanisms it uses, SSH has two weakness: First, the
`transmitted packets are padded only to an eight-byte
`boundary (if a block cipher is in use), which reveals the
`approximate size of the original data. Second, in inter-
`active mode, every individual keystroke that a user types
`is sent to the remote machine in a separate IP packet im-
`mediately after the key is pressed, which leaks the inter-
`keystroke timing information of users’ typing. In this
`paper, we show how these seemingly minor weaknesses
`result in serious security risks.
`
`First we show that even very simple statistical tech-
`niques suffice to reveal sensitive information such as the
`length of users’ passwords or even root passwords. More
`importantly, we further show that by using more ad-
`vanced statistical techniques on timing information col-
`lected from the network, the eavesdropper can learn sig-
`nificant information about what users type in SSH ses-
`sions.
`In particular, we perform a statistical study of
`users’ typing patterns and show that these patterns re-
`veal information about the keys typed. By developing a
`Hidden Markov Model and our key sequence prediction
`algorithm, we can predict key sequences from the inter-
`keystroke timings. We further develop an attacker sys-
`tem, Herbivore , which tries to learn users’ passwords by
`monitoring SSH sessions. By collecting timing informa-
`tion on the network, Herbivore can speed up exhaustive
`search for passwords by a factor of 50. We also propose
`some countermeasures.
`
`In general our results apply not only to SSH, but also
`to a general class of protocols for encrypting interactive
`traffic. We show that timing leaks open a new set of
`security risks, and hence caution must be taken when
`designing this type of protocol.
`
` This research was supported in part by the Defense Advanced Re-
`search Projects Agency under DARPA contract N6601-99-28913 (un-
`der supervision of the Space and Naval Warfare Systems Center San
`Diego) and by the National Science foundation under grants FD99-
`79852 and CCR-0093337.
`
`Just a few years ago, people commonly used astonish-
`ingly insecure networking applications such as tel-
`net, rlogin, or ftp, which simply pass all confi-
`dential information, including users’ passwords, in the
`clear over the network. This situation was aggravated
`through broadcast-based networks that were commonly
`used (e.g., Ethernet) which allowed a malicious user to
`eavesdrop on the network and to collect all communi-
`cated information [CB94, GS96].
`
`Fortunately, many users and system administrators have
`become aware of this issue and have taken counter-
`measures. To curb eavesdroppers, security researchers
`designed the Secure Shell (SSH), which offers an en-
`crypted channel between the two hosts and strong au-
`thentication of both the remote host and the user [Yl¨o96,
`SSL01, YKS 00b]. Today, SSH is quite popular, and it
`has largely replaced telnet and rlogin.
`
`Many users believe that they are secure against eaves-
`droppers if they use SSH. Unfortunately, in this paper
`we show that despite state-of-the-art encryption tech-
`niques and advanced password authentication protocols
`[YKS 00a], SSH connections can still leak significant
`information about sensitive data such as users’ pass-
`words. This problem is particularly serious because it
`means users may have a false confidence of security
`when they use SSH.
`
`In particular we identify that two seemingly minor weak-
`nesses of SSH lead to serious security risks. First, the
`transmitted packets are padded only to an eight-byte
`boundary (if a block cipher is in use). Therefore an
`eavesdropper can easily learn the approximate length of
`the original data. Second, in interactive mode, every
`individual keystroke that a user types is sent to the re-
`mote machine in a separate IP packet immediately af-
`ter the key is pressed (except for some meta keys such
`Shift or Ctrl). We show in the paper that this prop-
`erty can enable the eavesdropper to learn the exact length
`of users’ passwords. More importantly, as we have veri-
`fied, the time it takes the operating system to send out the
`packet after the key press is in general negligible com-
`paring to the inter-keystroke timing. Hence an eaves-
`
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`dropper can learn the precise inter-keystroke timings of
`users’ typing from the arrival times of packets.
`
`Experience shows that users’ typing follows stable pat-
`terns1. Many researchers have proposed to use the du-
`ration of key strokes and latencies between key strokes
`as a biometric for user authentication [GLPS80, UW85,
`LW88, LWU89, JG90, BSH90, MR97, RLCM98,
`MRW99]. A more challenging question which has not
`yet been addressed in the literature is whether we can
`use timing information about key strokes to infer the key
`sequences being typed. If we can, can we estimate quan-
`titatively how many bits of information are revealed by
`the timing information? Experience seems to indicate
`that the timing information of keystrokes reveals some
`information about the key sequences being typed. For
`example, we might have all experienced that the elapsed
`time between typing the two letters “er” can be much
`smaller than between typing “qz”. This observation is
`particularly relevant to security. Since as we show the
`attacker can get precise inter-keystroke timings of users’
`typing in a SSH session by recording the packet arrival
`times, if the attacker can infer what users type from the
`inter-keystroke timings, then he could learn what users
`type in a SSH session from the packet arrival times.
`
`In this paper we study users’ keyboard dynamics and
`show that the timing information of keystrokes does leak
`information about the key sequences typed. Through
`more detailed analysis we show that the timing informa-
`tion leaks about 1 bit of information about the content
`per keystroke pair. Because the entropy of passwords
`is only 4–8 bits per character, this 1 bit per keystroke
`pair information can reveal significant information about
`the content typed. In order to use inter-keystroke tim-
`ings to infer keystroke sequences, we build a Hidden
`Markov Model and develop a n-Viterbi algorithm for the
`keystroke sequence inference. To evaluate the effective-
`ness of the attack, we further build an attacker system,
`Herbivore, which monitors the network and collects tim-
`ing information about keystrokes of users’ passwords.
`Herbivore then uses our key sequence prediction algo-
`rithm for password prediction. Our experiments show
`that, for passwords that are chosen uniformly at random
`with length of 7 to 8 characters, Herbivore can reduce the
`cost of password cracking by a factor of 50 and hence
`speed up exhaustive search dramatically. We also pro-
`pose some countermeasures to mitigate the problem.
`
`We emphasize that the attacks described in this paper are
`a general issue for any protocol that encrypts interactive
`traffic. For concreteness, we study primarily SSH, but
`these issues affect not only SSH 1 and SSH 2, but also
`
`1In this paper we only consider users who are familiar with key-
`board typing and use touch typing.
`
`any other protocol for encrypting typed data.
`
`In Section 2
`The outline of this paper is as follows.
`we discuss in more details about the vulnerabilities
`of SSH and various simple techniques an attacker can
`use to learn sensitive information such as the length
`of users’ passwords and the inter-keystroke timings of
`users’ passwords typed.
`In Section 3 we present our
`statistical study on users’ typing patterns and show that
`inter-keystroke timings reveal about 1 bit of information
`per keystroke pair. In Section 4 we describe how we can
`infer key sequences using a Hidden Markov Model and
`a n-Viterbi algorithm. In Section 5 we describe the de-
`sign, development and evaluation of an attacker system,
`Herbivore, which learns users’ passwords by monitoring
`SSH sessions. We propose countermeasures to prevent
`these attacks in Section 7, and conclude in Section 8.
`
`2 Eavesdropping SSH
`
`The Secure Shell SSH [SSL01, YKS  00b] is used to en-
`crypt the communication link between a local host and a
`remote machine. Despite the use of strong cryptographic
`algorithms, SSH still leaks information in two ways:
`
` First, the transmitted packets are padded only to an
`eight-byte boundary (if a block cipher is in use),
`which leaks the approximate size of the original
`data.
`
`in interactive mode, every individual
` Second,
`keystroke that a user types is sent to the remote
`machine in a separate IP packet immediately after
`the key is pressed (except for some meta keys such
`Shift or Ctrl). Because the time it takes the op-
`erating system to send out the packet after the key
`press is in general negligible comparing to the inter-
`keystroke timing (as we have verified), this also
`enables an eavesdropper to learn the precise inter-
`keystroke timings of users’ typing from the arrival
`times of packets.
`
`The first weakness poses some obvious security risks.
`For example, when one logs into a remote site R in
`SSH, all the characters of the initial login password
`are batched up, padded to an eight-byte boundary if a
`block cipher is in use, encrypted, and transmitted to R.
`Due to the way padding is done, an eavesdropper can
`learn one bit of information on the initial login pass-
`word, namely, whether it is at least 7 characters long
`or not. The second weakness can lead to some potential
`anonymity risks since, as many researchers have found
`previously, inter-keystroke timings can reveal the iden-
`
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`SSH
`Server B
`
`,I
`
`20
`
`l
`
`20
`
`"Password: "
`j~ ,U
`A
`
`28
`
`jl
`
`~
`
`jl
`
`Prompt
`
`N
`
`time
`
`-
`-
`
`20
`
`"s"
`
`20
`
`'
`
`Client
`Host A
`Figure 1: The traffic signature associated with running SU in a SSH session. The numbers in the figure are the size
`(in bytes) of the corresponding packet payloads.
`
`,,
`
`20
`
`,,
`
`20 20 20 20 20
`
`20
`
`"u"
`
`Return
`
`"J""u""l"
`
`"i"
`
`"a"
`
`Return
`
`,,
`
`time
`
`-
`
`tity of the user [GLPS80, UW85, LW88, LWU89, JG90,
`BSH90, MR97, RLCM98, MRW99].
`
`In this section, we show that several simple and practical
`attacks exploiting these two weaknesses. In particular,
`an attacker can identify which transmitted packets corre-
`spond to keystrokes of sensitive data such as passwords
`in a SSH session. Using this information, the attacker
`can easily find out the exact length of users’ passwords
`and even the precise inter-keystroke timings of the typed
`passwords. Learning the exact length of users’ pass-
`words allows eavesdroppers to target users with short
`passwords. Learning the inter-keystroke timing infor-
`mation of the typed passwords allows eavesdroppers to
`infer the content of the passwords as we will show in
`Section 3 and 4.
`
`Traffic Signature Attack We can often exploit prop-
`erties of applications to identify which packets corre-
`spond to the typing of a password. Consider, for in-
`stance, the SU command. Assume the user has already
`established a SSH connection from local host A to re-
`mote host B. When the user types the command SU
`in the established SSH connection A
`B, we obtain a
`peculiar traffic signature as shown in Figure 1.
`If the
`SSH session uses SSH 1.x2 and a block cipher such
`as DES for the encryption [NBS77, NIS99], as is com-
`mon, then the local host A sends three 20-byte pack-
`ets: “s”, “u”, “Return”. The remote host B echoes the
`“s” and “u” in two 20-byte packets and sends a 28-byte
`packet for the “Password: ” prompt. Then A sends 20-
`byte packets, one for each of the password characters,
`without receiving any echo data packets. B then sends
`some final packets containing the root prompt if SU suc-
`ceeds, otherwise some failure messages. Thus by check-
`ing the traffic against this “su” signature, the attacker
`can identify when the user issues the SU command and
`
`2The attack also works when ssh 2.x is in use. Only the packet
`sizes are slightly different.
`
`hence learn which packets correspond to the password
`keystrokes. Note that similar techniques can be used to
`identify when users type passwords to authenticate to
`other applications such as PGP [Zim95] in a SSH ses-
`sion.
`
`Multi-User Attack Even more powerful attacks exist
`when the attacker also has an account on the remote
`machine where the user is logging into through SSH.
`For example, the process status command ps can list
`all the processes running on a system. This allows the
`attacker to observe each command that any user is run-
`ning. Again, if the user is running any command that re-
`quires a password input (such as su or pgp) the attacker
`can identify the packets corresponding to the password
`keystrokes.
`
`Nested SSH Attack Assume the user has already es-
`tablished a SSH session between the local host A and
`remote host B. Then the user wants to open another SSH
`session from B to another remote host C as shown in Fig-
`ure 2. In this case, the user’s password for C is transmit-
`ted, one keystroke at a time, across the SSH-encrypted
`B from the user to B, even though the SSH
`link A
`client on machine B patiently waits for all characters of
`the password before it sends them all in one packet to
`host C for authentication (as designed in the SSH proto-
`col [YKS 00a]). It is easy to identify such a nested SSH
`connection using techniques developed by Zhang and
`Paxson [ZP00b, ZP00a]. Hence in this case the eaves-
`dropper can easily identify the packets corresponding to
`the user’s password on link A B, and from this learn
`the length and the inter-keystroke timings of the users’
`password on host C.
`
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`Our focus on passwords creates many challenges. Pass-
`words are entered very differently from other text: pass-
`words are typed frequently enough that, for many users,
`the keystroke pattern is memorized and often typed al-
`most without conscious thought. Furthermore, well-
`chosen passwords should be random and have little or
`no structure (for instance, they should not be based on
`dictionary words). As a consequence, naive measure-
`ments of keystroke timings will not be representative of
`how users type passwords unless great care is taken in
`the design of the experimental methodology.
`
`Our experimental methodology is carefully designed to
`address these issues. Due to security and privacy consid-
`erations, we chose not to gather data on real passwords;
`therefore, we have chosen a data collection procedure
`intended to mimic how users type real passwords. A
`conservative method is to pick a random password for
`the user (where each character of the password is cho-
`sen uniformly at random from a set of 10 letter keys and
`5 number keys, independently of all other characters in
`the password), have the user practice typing this pass-
`word many times without collecting any measurements,
`and then measure inter-keystroke timing information on
`this password once the user has had a chance to practice
`it at length.
`
`However, we found that, when the goal is to try to
`identify potentially relevant timing properties (rather
`than verify conjectured properties), this conservative ap-
`proach is inefficient. In particular, users typically type
`passwords in groups of 3–4 characters, with fairly long
`pauses between each group. This distorts the digraph
`statistics for the pair of characters that spans the group
`boundary and artificially inflates the variance of our
`measurements. As a result we would need to collect
`a great deal of data for many random passwords be-
`fore this effect would average out. In addition, it takes
`quite a while for users to become familiar with long ran-
`dom passwords. This makes the conservative approach a
`rather blunt tool for understanding inter-keystroke statis-
`tics.
`
`Fortunately, there is a less costly way to gather inter-
`keystroke timing statistics: we gather training data on
`each pair of characters
`as typed in isolation. We
`ka
`kb
`pick a character pair and ask the user to type this pair 30–
`40 times, returning to the home row each time between
`repetitions. For each user, we repeat this for many pos-
`sible pairs (142 pairs, in our experiments) and we gather
`data on inter-keystroke timings for each such pair. We
`collected the latency of each character pair measurement
`and computed the mean value and the standard devia-
`tion. In our experience, this gives better results.
`
`SSH2
`
`password
`
`B
`
`password
`
`SSH1
`
`A
`
`eavesdrop
`
`C
`
`Adversary
`
`Figure 2: The nested SSH attack.
`
`3 Statistical Analysis of Inter-keystroke
`Timings
`
`As a first study towards inferring key sequences from
`timing information, we develop techniques for statistical
`analysis of the inter-keystroke timings. In this section,
`we first describe how we collect training data and show
`some simple timing characteristics of character pairs.
`We then show how we model the inter-keystroke timing
`of a given character pair as a Gaussian distribution. We
`then describe how to estimate quantitatively the amount
`of information about the character pair that one can learn
`using the inter-keystroke timing information. Denote the
`set of character pairs of interest as Q, and let Q denote
`the cardinality of the set Q.
`
`3.1 Data Collection
`
`The two keystrokes of a pair of characters
`gen-
`kb
`ka
`erates four events: the press of ka, the release of ka, the
`press of kb, and the release of kb. However, because
`only key presses (not key releases) trigger packet trans-
`mission, an eavesdropper can only learn timing informa-
`tion about the key-press events. Since the main focus of
`our study is in the scenario where an adversary learns
`timing information on keystrokes by simply monitoring
`the network, we focus only on key-press events. The
`time difference between two key presses is called the la-
`tency between the two keystrokes. We also use the term
`inter-keystroke timing to refer to the latency between two
`keystrokes.
`
`In order to characterize how much information is leaked
`by inter-keystroke timings, we have performed a number
`of empirical tests to measure the typing patterns of real
`users. Because passwords are probably the most sen-
`sitive data that a user will ever type, we focus only on
`information revealed about passwords (rather than other
`forms of interactive traffic).
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`15
`
`10
`
`5
`
`Frequency
`
`15
`
`10
`
`5
`
`Frequency
`
`0
`
`300
`200
`100
`0
`Inter−keystroke Timing for v−o (milliseconds)
`
`0
`
`300
`200
`100
`0
`Inter−keystroke Timing for v−b (milliseconds)
`
`Figure 3: The distribution of inter-keystroke timings for two sample character pairs.
`
`As an example, Figure 3 shows the latency histogram
`of two sample character pairs. The left model corre-
`v, o
`, and the
`sponds to the latency between the pair
`right model corresponds to
`v, b
`. We can see that the
`latency between
`v, o
`is clearly shorter than the la-
`tency between
`v, b
`, and the latency distributions of
`these two sample character pairs are almost entirely non-
`overlapping.
`
`The optimized data collection approach gives us a more
`efficient way to study fine-grained details of inter-
`keystroke statistics without requiring collecting an enor-
`mous amount of data. We used data collected in this way
`to quickly identify plausible conjectures, develop poten-
`tial attacks, and to train our attack models. As far as
`we are aware, collecting data on keystroke pairs in iso-
`lation does not seem to bias the data in any obvious way.
`Nonetheless, we also validate all our results using the
`conservative measurement method (see Section 5).
`
`3.2 Simple Timing Characteristics
`
`Next, we divide the test character pairs into five cate-
`gories, based on whether they are typed using the same
`hand, the same finger, and whether they involve a num-
`ber key:
`
` Two letter keys typed with alternating hands, i.e.,
`
`one with left hand and one with right hand;
`
` Two characters containing one letter key and one
`number key typed with alternating hands;
`
` Two letter keys, both typed with the same hand but
`with two different fingers;
`
` Two letter keys typed with the same finger of the
`same hand;
`
` Two characters containing one letter key and one
`number key, both typed with the same hand.
`
`Figure 4 shows the histogram of latency distribution of
`character pairs for each category. We split the whole la-
`tency range into six bins as shown in the x-axis. Within
`each category, we put each character pair into the cor-
`responding bin if its mean latency value is within the
`range of the bin. Each bar in the histogram of a cate-
`gory represents the ratio of the number of character pairs
`in the associated bin over the total number of character
`pairs in the category.3 We can see that all the character
`pairs that are typed using two different hands take less
`than 150 milliseconds, while pairs typed using the same
`hand and particularly the same finger take substantially
`longer. Character pairs that alternate between one letter
`key and one number key, but are typed using the same
`3Hence the sum of all bars within one category is 1.
`
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`Histogram of the latency of character pairs
`
`Two letter keys, alternating hands
`A letter and a number, alternating hands
`Two letters, same hand, different fingers
`Two letters, same finger
`A letter and a number, same hand
`
`< 100
`
`100-150
`
`150-200
`
`200-250
`
`250-300
`
`> 300
`
`Latency (milliseconds)
`
`1
`
`0.9
`
`0.8
`
`0.7
`
`0.6
`
`0.5
`
`0.4
`
`0.3
`
`0.2
`
`0.1
`
`0
`
`Ratio of character pairs
`
`Figure 4: Inter-keystroke timings for character pairs in five different categories. Note that some bars at some positions
`disappear because the corresponding height is zero.
`
`hand, take the longest time to type. This is simply be-
`cause two hands offer a certain amount of parallelism,
`while character pairs typed with one hand require a cer-
`tain degree of sequential movements and hence tend to
`take longer. This is especially obvious in the case of one
`letter and one number pairs typed using one hand. They
`in general require more hand movement and hence the
`longest time.4
`
`So, if the attacker observes a character pair typed with
`latency more than 150 milliseconds, he can guess with
`high probability of success that the character pair is not
`typed using two different hands and hence can infer
`about 1 bit of information about the content of the char-
`acter pair. Because the 142 character pairs are formed
`from randomly selected letter keys and number keys,
`they seem likely to form a representative sample of the
`whole keyboard. Hence this simple classification ex-
`tends to the whole keyboard, and already indicates that
`the inter-keystroke timing leaks substantial information
`about what is typed.
`
`The properties described above are unlikely to be ex-
`haustive. For instance, earlier work on timing attacks
`on multi-user machines suggested that inter-keystroke
`timings may additionally reveal which characters in the
`
`4Note that here we only consider users that use touch typing.
`
`password are upper-case [Tro98].
`
`3.3 Gaussian Modeling
`
`From the plot of the latency distribution of a given char-
`acter pair, such as the ones shown in Figure 3, we can see
`that the latency between the two key strokes of a given
`character pair forms a Gaussian-like unimodal distribu-
`tion. Hence a natural assumption (which is confirmed
`by our empirical observations) is that the probability of
`the latency y between two keystrokes of a character pair
`Q, Pr
`, forms a univariate Gaussian distribution
`q
`y q
`m q
`s q
`, meaning
`
`
`
`Pr
`
`1
`
`2
`
`m q
`2s 2
`q
`
`e
`
`  y
`
`y q
` 2ps
`
`q
`where m q is the mean value of the latency for character
`pair q and s q is the standard deviation. Given a set of
`training data
`N, where qi is the i-th charac-
`qi
`yi
`1
`i
`
`ter pair and yi is the corresponding latency in the data
`

`m q
`s q
`collection, we can derive the parameters
`q
`Q
`
`based on maximum likelihood estimation, i.e., we com-
` 

`pute the mean and the standard deviation for each char-
`acter pair.
`
`Figure 5 shows the estimated Gaussian models of the la-
`tencies of the 142 character pairs. Our empirical result
`
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`shows that most of the latencies of the character pairs
`lie between 50 and 250 milliseconds. The average of
`the standard deviation of the 142 character pairs is about
`30 milliseconds. The graph also indicates that the la-
`tency distributions of the character pairs severely over-
`lap, which means the inference of character pairs using
`just latency information is a challenging task.
`
`3.4
`
`Information Gain Estimation
`
`Q Pr
`
`where Pr
`
`H1
`
`y0 
`
`We would like to estimate quantitatively how much
`information the latency information reveals about the
`character pairs typed. This will be an upper bound
`of how much information an attacker can extract from
`the timing information using any particular method.
`We estimate it by computing the information gain in-
`duced by the latency information. If we select a char-
`acter pair uniformly at random from the character-
`pair space, and if the attacker does not get any addi-
`tional information, the entropy of the probability dis-
`tribution of character pairs to the attacker is H0
`q
`  (cid:229) q
`If the attacker learns
`Q Pr
`log2 Pr
`log2 Q 
`q
`q
` 
`the latency y0 between the two keystrokes of the char-
`acter pair, the estimated entropy of the probability dis-
`tribution of character pairs to the attacker is H1
`q y
`y0  (cid:229) q
`log2 Pr
`q y0
`q y0 
`q y0 
`Pr y0
`Pr q
`q
`and Pr
`is computed using the
`y0 q
`  (cid:229)
`
`Pr q
`Q Pr y0
`q
`q
` 
`
`Gaussian distribution obtained in the parameter estima-
`tion phase in the previous subsection. The information
`gain induced by the observation of latency y0 is the dif-
`ference between the two entropies, H0
`q
`q y
`
`Using the parameter estimation of the 142 character
`pairs obtained in the previous section, we can compute
`and H0
`as shown in Fig-
`q y
`q y
`H1
`y0
`H1
`y0
`q
`
`ure 6(a) and Figure 6(b).
`The estimated information gain, also called mutual in-
`formation, is I
`q; y
`Pr
`y0


`q y
`q
`q
`H0
`H1
`
`
`y0  (cid:229) q
` 
` 
`where Pr
`Q Pr
`Pr
`q
`dy0
`y0 q
`q y
` 
`From the numerical computation we obtain I
`q; y
`1
`2.
` 
`This means the estimated information gain available
`2 bits per charac-
`from latency information is about 1
`ter pair when the character pair has uniform distribution.
`Hence the attacker could potentially extract 1
`2 bits of
`information per character pair by using the latency in-
`formation in this case. Because the character pairs in
`our experiments are selected uniformly at random from
`all letter and number keys, we expect that they will be
`representative of the whole keyboard. Intuitively, Fig-
`ure 5 is a sufficiently-large random sampling of a much
`denser graph containing the latency distributions of all
`possible character pairs. More detailed analysis shows
`that the estimated information gain computed using 142
`sample character pairs is a good estimate of the infor-
`
`H1
`
`y0
`
`
`
`H0
`
`0.035
`
`0.03
`
`0.025
`
`0.02
`
`0.015
`
`0.01
`
`0.005
`
`Probability
`
`0
`
`0
`
`50
`
`100
`
`150
`
`200
`Latency (millisecond)
`
`250
`
`300
`
`350
`
`400
`
`Figure 5: Estimated Gaussian distributions of all 142
`character pairs collected from a user.
`
`7
`
`6.5
`
`6
`
`5.5
`
`5
`
`4.5
`
`Entropy (bits)
`
`4
`
`0
`
`50
`
`100
`
`150
`Latency (milliseconds)
`
`200
`
`250
`
`300
`
`(a) Entropy of character pairs given a latency obser-
`vation
`
`50
`
`100
`
`150
`Latency (milliseconds)
`
`200
`
`250
`
`300
`
`3.5
`
`3
`
`2.5
`
`2
`
`1.5
`
`Information Gain (bits)
`
`1
`
`0.5
`
`0
`
`(b) Information gain induced by a latency observation
`
`Figure 6: Entropy and information gain as a function of
`the inter-keystroke latency.
`
`ASSA ABLOY Ex. 1008 - Page 7
`ASSA ABLOY AB v. CPC Patent Technologies Pty Ltd.
`IPR2022-01006 - U.S. Patent No. 9,665,705
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`mation gain when the character-pair space includes all
`letter and number character pairs. This estimate is com-
`parable to the back-of-the-envelope calculation in Sec-
`tion 3.2 based on our classification into five categories
`of keystroke pairs.
`
`Because the entropy of written English is so low (about
`0.6–1.3 bits per character [Sha50]), the 1
`2-bit informa-
`tion gain per character pair leaked through the latency in-
`formation seems to be significant. 5 For example, we can
`expect that users’ PGP passphrases will often contain
`only 1 bit of entropy per character. Hence the latency in-
`formation may reveal significant information about PGP
`passphrases.
`
`The information gain curve in Figure 6(b) shows a con-
`vex shape. Note that latencies greater than 175 millisec-
`onds are relatively rare; however, whenever we see such
`a long time between keystrokes, we learn a lot of in-
`formation about what was typed, because there are not
`many possibilities that would lead to such a large la-
`tency. The character pairs that take longer than 175
`milliseconds to type are mostly pairs containing number
`keys or pairs typed with one finger. Hence this analysis
`suggests that passwords containing number keys or char-
`acter pairs that are typed with one finger are particularly
`vulnerable to such timing attacks.
`
`Another interesting observation is that the mean of the
`standard deviations of the character pairs is only about
`30 milliseconds as shown in our experiments, while the
`standard deviation of round-trip time on the Internet in
`many cases is less than 10 milliseconds [Bel93]. There-
`fore even when the attacker is far from the SSH client
`host, he can still get sufficiently-precise inter-keystroke
`timing information. This makes the timing attack even
`more severe.
`
`4 Inferring Character Sequences From
`Inter-Keystroke Timing Information
`
`In this section, we describe how we can infer charac-
`ter sequences using the latency information. In partic-
`ular, we model the relationship of latencies and charac-
`ter sequences as a Hidden Markov Model [RN95]. We
`extend the standard Viterbi algorithm to an n-Viterbi al-
`gorithm that outputs the n most likely candidate char-
`acter sequences. We further estimate how many bits of
`information about the real character sequence this algo-
`
`5Note that the 1
`2-bit information gain is estimated for the case of
`randomly selected passwords where the sequence of characters have
`a uniform distribution. However, this is not the case for texts. More
`careful calculation is needed to estimate the information gain in the
`case of natual text.
`
`rithm extracts from the latency information and show it
`is nearly optimal.
`
`4.1 Hidden Markov Model
`
`In general, a Markov Model is a way of describing a
`finite-state stochastic process with the property that the
`probability of transitioning from the current state to an-
`other state depends only on the current state, not on any
`prior state of the process [RN95]. In a Hidden Markov
`Model (HMM), the current state of the process cannot be
`directly observed. Instead, some outputs from the state
`are observed, and the probability distribution of possible
`outputs given the state is dependent only on the state.
`Using a HMM, one can infer information about the prior
`path the process has taken from the sequence of observed
`outputs of the states, and efficient algorithms are known
`for working with HMM’s. Because of this, HMM’s have
`been widely used in areas such as speech recognition and
`text modeling.
`
`In our setting, we consider each character pair of inter-
`est as a hidden (non-observable) state, and the latency
`between the two keystrokes of the character pair as the
`output observation from the character-pair state. Each
`state corresponds to a pair of characters, so that the typ-
`KT , is a process that
`ing of a character sequence K0
`    
`goes through T states, q1
`1
`qT , where qt
`t T
`    
`represents the t-th character pair
`typed. Let
`Kt
`Kt
`1
`denote the observed latency of state qt.
`1
`t T
`yt
`Then we model the typing of a character sequence as a
`HMM. This means we make two assumptions. First, the
`probability of transition from the current state to another
`state is only dependent on the current state, not on the
`prior path of the process. If the character sequence is a
`password chosen uniformly at random, this assumption
`obviously ho