Patent Publication Number: US-10785017-B2

Title: Mitigating timing attacks via dynamically scaled time dilation

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
     The present application is related to commonly-owned U.S. patent application Ser. No. 15/961,830, entitled “Mitigating Timing Attacks via Dynamically Triggered Time Dilation,” which is filed concurrently herewith. The entire contents of this related application are incorporated herein by reference for all purposes. 
     BACKGROUND 
     In computing, a timing attack is a type of side-channel attack (i.e., an attack based on information gained from the physical implementation of a computer system) in which an attacker attempts to compromise the system by analyzing the amount of time it takes to complete one or more operations. Every logical operation in a computer system takes some time to execute, and that time can differ based on the input(s) to the operation. Accordingly, with sufficiently precise measurements of an operation&#39;s execution time, an attacker can create a time model for the operation and deduce its input(s) (which may include a secret). Recent highly publicized security vulnerabilities that rely on timing attacks include the Meltdown and Spectre vulnerabilities which affect most modern microprocessor architectures. 
     Generally speaking, to carry out a timing attack, an attacker needs to be able to quantify an operation&#39;s execution time via a reference clock—in other words, a clock in which clock ticks arrive at a consistent rate. The attacker may establish the reference clock by consulting an explicit clock (i.e., one that is derived from hardware signals and typically represented in either wall clock time or CPU time). For example, the attacker may call an application programming interface (API) that returns timestamp values as determined by the system hardware. The attacker may also establish the reference clock by creating an implicit clock (i.e., one that is derived from an arbitrary unit of time measure, without need for an explicit clock). For example, the attacker may track the number of times it can take some action or carry out some task (e.g., call an API, run a calculation, etc.) while an operation executes and use that number to quantify the duration of the operation. 
     There are a number of existing approaches for mitigating timing attacks, such as clamping explicit clocks to a relatively low resolution, altering runtime hardware frequencies, and adding noise or interference to such frequencies. However, these existing approaches can be worked around and thus fail to structurally prevent timing exploits. Additionally, since these existing approaches cause a computer system to deviate from ideal operating conditions, they negatively impact the experience of users interacting with the system (e.g., performance becomes worse, resource usage increases, power efficiency decreases, etc.). 
     SUMMARY 
     Techniques for mitigating timing attacks via dynamically scaled time dilation are provided. According to one set of embodiments, a computer system can enable time dilation with respect to a program, where the time dilation causes the program to observe a dilated view of time relative to real time. Then, while the time dilation is enabled, the computer system can track a count of API calls or callbacks made by a program within each of a series of time buckets and, based on counts tracked for a range of recent time buckets, scale up or scale down a degree of the time dilation. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a simplified block diagram of a software environment that implements the techniques of the present disclosure. 
         FIG. 2  depicts a workflow for dynamically triggering time dilation according to certain embodiments. 
         FIGS. 3A, 3B, and 3C  depict graphs illustrating different algorithms for warping an explicit clock according to certain embodiments. 
         FIG. 4  depicts a workflow for dynamically scaling time dilation according to certain embodiments. 
         FIG. 5  depicts a simplified block diagram of a computer system according to certain embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for purposes of explanation, numerous examples and details are set forth in order to provide an understanding of various embodiments. It will be evident, however, to one skilled in the art that certain embodiments can be practiced without some of these details, or can be practiced with modifications or equivalents thereof 
     1. Overview 
     Embodiments of the present disclosure provide techniques for mitigating timing attacks via “time dilation”—in other words, dilating or warping the view of time of an observer (e.g., a potential attacker) relative to real time such that the observer cannot establish a consistent reference clock for carrying out a timing attack. 
     According to a first set of embodiments, a computer system can track the number of API calls and/or callbacks made by an observer within each of a series of time windows, referred to as buckets. The API calls/callbacks may be explicit clock APIs and/or APIs that can be used to construct implicit clocks. If the system determines that the observer has exceeded a threshold number of API calls/callbacks for a predefined consecutive number of buckets, the system can dynamic trigger (i.e., turn-on) time dilation with respect to that observer. This can include, e.g., (1) injecting a random amount of wait time (i.e., pausing) into the implicit clock API calls/callbacks called by the observer, thereby preventing the observer from constructing a consistent implicit clock from those calls/callbacks, and/or (2) randomly jittering/warping the time values returned by explicit clock APIs to the observer, thereby preventing the observer from using those explicit clock APIs establish a consistent view of real time. 
     According to a second set of embodiments, the computer system can dynamically scale the amount of time dilation that is introduced via (1) and (2) for the observer based on the observer&#39;s continued behavior/activity. For example, if the observer continues to act “badly” (e.g., issue a high number of API calls/callbacks over an extended period of time) which indicates that the observer is likely perpetrating a timing attack or continuing to perpetrate such an attack, the system may increase the amount of wait time injected into implicit clock API calls/callbacks called by the observer and/or increase the amount of jitter/warping of time values returned by explicit clock APIs to the observer. Conversely, if the observer&#39;s behavior improves (e.g., reduces its call/callback activity to a normal level for an extended period of time), the time dilation introduced via the wait time injection and explicit clock jittering/warping may be dialed back or even turned off entirely. 
     With the two high-level concepts described above (dynamic triggering and dynamic scaling of time dilation), the observer can run under ideal conditions, with no significant delay relative to real time, as long as the observer is well-behaved. However, as soon as the observer begins to exhibit behavior that is indicative of a timing attack, the system can introduce time dilation safeguards that prevent the observer from establishing a consistent reference clock via either implicit clocks or explicit clocks. Further, as the observer becomes more or less aggressive in its activity/behavior, the system can dynamically increase or decrease the degree of time dilation as appropriate. In this way, these techniques can mitigate timing attacks in a manner that is more intelligent, efficient, and performant than existing solutions. 
     The foregoing and other aspects of the present disclosure are described in further detail in the sections that follow. For purposes of illustration, the embodiments and examples below are presented in the context of a web platform application (e.g., a web browser) that runs JavaScript code downloaded by the application. In these examples/embodiments, the JavaScript code is the observer/potential perpetrator of timing attacks and the web platform application is the entity that implements the techniques of the present disclosure. This is a valuable use case to consider, since JavaScript and web content in general is typically the most common vector by which unknown, potentially malicious code can reach end-user systems. However, it should be appreciated the present disclosure is not solely limited to this context and can be applied to other contexts in which timing attack mitigations are useful and appropriate. By way of example, the techniques described herein are equally applicable for mitigating timing attacks in system-level software. 
     2. Software Environment 
       FIG. 1  is a simplified block diagram of a software environment  100  in which embodiments of the present disclosure may be implemented. As shown, software environment  100  includes a web platform application  102 , which may be a web browser or any other software application that hosts/presents web content, and a JavaScript program  104  that is downloaded and run by application  102 . Web platform application  102  includes a set of web platform APIs  106  that can be invoked by JavaScript program  104 . Examples of such APIs include standard JavaScript APIs (including callback functions such as setTimeout( ), setInterval( ), etc.), Document Object Model (DOM) APIs, HTML5 APIs, browser-specific APIs, and so on. 
     Web platform application  102  also includes an event loop  108  that adheres to the HTML5 event loop standard and is generally responsible for coordinating the execution of different processes within web platform application  102 , including JavaScript program  104 . In one set of embodiments, event loop  108  can execute code each time a given API  106  is invoked by JavaScript program  104  and thus can act as a “chokepoint” for all such API invocations. 
     As alluded to in the Background section, the mitigation of timing attacks is becoming an increasingly important issue with the emergence of far-reaching security vulnerabilities such as Meltdown and Spectre. JavaScript code, such as JavaScript program  104  of  FIG. 1 , is perhaps one of the most common vectors by which malicious code capable of perpetrating a timing attack can find its way onto end-user systems. For instance, JavaScript program  104  may be surreptitiously injected into an online advertisement that is then disseminated across a number of different websites via an ad network. Accordingly, it is desirable to have mitigations at the web platform level that prevent or make it difficult for JavaScript program  104  to carry out a timing attack. 
     Unfortunately, existing approaches to timing attack mitigation suffer from various limitations and disadvantages that make them less than ideal solutions for this use case. For examples, approaches that simply clamp explicit clocks to a coarse granularity or modify runtime frequencies introduce performance and power management problems that negatively affect the user experience of end-users interacting with the web platform (e.g., animations begin to stutter, UI responsiveness degrades, web pages are slow to load, etc.). These problems are particularly acute on mobile devices which depend on efficient resource usage and power management for day-long operation. 
     To address the foregoing and other similar issues, web platform application  102  of  FIG. 1  is enhanced to include, within event loop  108 , a call rate tracking module  110 , a pause task module  112 , and an explicit clock warp module  114 . At a high level, modules  110 - 114  can interoperate to implement two novel timing attack mitigation mechanisms according to embodiments of the present disclosure: (1) dynamically triggered time dilation and (2) dynamically scaled time dilation. 
     With respect to mechanism (1) (described in further detail in section (3) below), call rate tracking module  110  can track the number of calls made by JavaScript program  104  to web platform APIs  106  that enable program  104  to construct an implicit clock (e.g., callback functions) and/or consult an explicit clock (e.g., timestamp APIs). Note that this is possible because event loop  108  acts as a chokepoint for all of the API invocations made by JavaScript program  104  and other processes of web platform application  102 . Call rate tracking module  110  can perform this tracking on a per-bucket basis, where each bucket is a time window of a predefined period (e.g., 200 milliseconds), and can compare the number of calls/callbacks made within each bucket to a threshold. If call rate tracking module  110  determines that the threshold has been exceeded for a certain number of consecutive buckets, event loop  108  can trigger time dilation for JavaScript program  104  by inserting wait time into each implicit clock API call/callback via pause task module  112 , and/or warping the time values returned by explicit clock APIs via explicit clock warp module  114 . The end result of this is that JavaScript program  104  begins to observe a dilated view of time that is inconsistent with real time, and thus makes it difficult or impossible for program  104  to construct a consistent reference clock in order to perpetrate a timing attack. 
     Significantly, since the wait time insertion and explicit clock warping is only turned-on in scenarios where JavaScript program  104  is deemed to be a potential attacker (via the bucket-based call tracking above), this approach does not introduce any performance or resource usage overhead for web content that is well-behaved. This is a significant advantage over existing mitigation techniques, which tend to turn-on heavy-handed mitigations by default and thus introduce performance/power problems for all web pages, whether good or bad. 
     With respect to mechanism (2) (described in further detail in section (5) below), call rate tracking module  110  can continue to track the number of calls/callbacks made by JavaScript program  104  to web platform APIs  106  on a per-bucket basis once time dilation is turned on (either via the dynamic triggering mechanism of (1) or via a static configuration). Based on this continued tracking, call rate tracking module  110  can apply one or more policies to determine whether JavaScript program  104  is becoming more or less aggressive in its API calling behavior (indicating that the program is likely continuing to, or is no longer or perhaps never was, attempting to perpetrate a timing attack). This, in turn, can cause the system to scale up or down the degree of time dilation for JavaScript program  104  (via pause task module  112  and explicit clock warp module  114 ) in a proportional way. For example, if JavaScript program  104  continues to call implicit or explicit clock-related APIs at a high frequency for an extended period of time, the system can conclude that program  104  is continuing to perpetrate a timing attack and can ramp up the amount of wait time inserted into each implicit clock-related API call/callback, and/or the amount of warping applied to explicit clock time values. This ramping-up process can continue as long as JavaScript program  104  persists in its bad behavior, and may ultimately cause program  104  to be terminated. 
     Conversely, if the API call/callback rate of JavaScript program  104  drops to a low or normal level for an extended period of time, event loop  108  can conclude that program  104  is now well-behaved and can begin ramping down the amount of wait time inserted into each implicit clock-related API call/callback, and/or the amount of warping applied to explicit clock time values. This ramping-down process can continue as long as JavaScript program  104  persists in its good behavior, and may ultimately cause time dilation to be turned off entirely for program  104 . 
     Thus, with mechanism (2), web platform application  102  can more intelligently apply its timing attack mitigations in a manner that is proportional and responsive to the real-time activity/behavior of JavaScript program  104 . 
     It should be appreciated that software environment  100  of  FIG. 1  is illustrative and not intended to limit embodiments of the present disclosure. For example, while the various entities shown in this figure are arranged according to a particular configuration, other configurations are also possible. Further, these entities may include various subcomponents and/or functions that are not specifically described. One of ordinary skill in the art will recognize other variations, modifications, and alternatives. 
     3. Dynamically Triggering Time Dilation 
       FIG. 2  depicts a workflow  200  that provides additional details regarding the processing that may be performed by web platform application  102  and its constituent components (e.g., call rate tracking module  110 , pause task module  112 , explicit clock warp module  114 ) for dynamically triggering time dilation with respect to JavaScript program  104  according to certain embodiments. 
     At block  202 , while JavaScript program  104  is running, call rate tracking module  110  can start a timer for a current bucket (i.e., time window). At block  204 , call rate tracking module  110  can receive and count the number of calls/callbacks made by JavaScript program  104  to web platform APIs  106  that either enable program  104  to construct an implicit clock (i.e., a clock based on an arbitrary, internal unit of time measure) or consult an explicit clock (i.e., a clock that is based on hardware signals and represented as, e.g., wall clock time or CPU time). Examples of the former include JavaScript API calls or callbacks such as setTimeout( ) and setInterval( ). Examples of the latter include any API that returns a hardware-derived timestamp or time value. 
     At block  206 , call rate tracking module  110  can determine that the timer started at block  202  has reached a predefined time limit (e.g., 200 milliseconds) and close the current bucket. In addition, module  110  can record the total number of API calls/callbacks counted during that bucket (block  208 ) and check whether the total number exceeds a predefined threshold (block  210 ). If so, call rate tracking module  110  can mark the bucket as a “bad” bucket (block  212 ). Otherwise, call rate tracking module  110  can mark the bucket as a “good” bucket (block  214 ). 
     Once call rate tracking module  110  has marked the bucket appropriately, module  110  can check whether the last X consecutive buckets were bad buckets, where X is some predefined number (block  216 ). If not, call rate tracking module  110  can return to block  202  in order to start a timer for a new bucket and repeat the preceding steps. 
     However, if the last X consecutive buckets were in fact bad buckets, it can be concluded that JavaScript program  104  is exhibiting bad behavior that is indicative of a timing attack. As a result, web platform application  102  can trigger (i.e., turn-on) time dilation with respect to JavaScript program  104  by leveraging pause task module  112  and/or explicit clock warp module  114  (block  218 ). 
     For example, according to one set of embodiments, for each successive call that JavaScript program  104  makes to an API function or callback that relates to implicit clock creation, event loop  108  can (via, e.g., a task scheduler) instruct pause task module  112  to insert a randomly generated amount of wait time into the API execution flow, before the call/callback returns to program  104 . The result of this is that the API never completes in a consistent amount of time from the perspective of JavaScript program  104 , which makes it difficult or impossible for program  104  to count instances of these API calls/callbacks to construct an implicit clock. In a particular embodiment, the amount of wait time that pause task module  112  inserts into each API call/callback can be a random value from 0 to 255 microseconds. 
     According to another set of embodiments, for each successive call that JavaScript program  104  makes to an API function that relates to an explicit clock, event loop  108  can (via, e.g., a task scheduler) instruct explicit clock warp module  114  to randomly dilate or warp the time value that is returned by the API to program  104 . The result of this is that JavaScript program  104  never receives a consistent view of time from these explicit clock APIs, which makes it difficult or impossible for program  104  to create a consistent reference clock based on the explicit clocks. There are different ways in which explicit clock warp module  114  can warp the time values that are generated by the explicit clock APIs, which include clamping/random jitter and applying randomly-generated linear or higher-order functions that transform real time to warped time. These various techniques are discussed in section (4) below. 
     According to yet other embodiments, event loop  108  can trigger any combination or subset of the time dilation techniques described above according to various policies. For example, if call rate tracking module  110  determines that JavaScript program  104  has invoked a threshold number of explicit clock APIs for X consecutive bad buckets (but not a threshold number of APIs related to implicit clocks), event loop  108  may solely trigger explicit clock warping. As another example, if call rate tracking module  110  determines that JavaScript program  104  has invoked a threshold number of APIs related to implicit clocks for X consecutive bad buckets (but not a threshold number of explicit clock APIs), event loop  108  may solely trigger wait time insertion. As yet another example, a large number of API calls/callbacks for implicit clocks may also trigger explicit clock warping, and vice versa. All of these permutations, and more, are within the scope of the present disclosure. 
     It should be appreciated that workflow  200  of  FIG. 2  is illustrative and various modifications are possible. For example, although workflow  200  shows that a single instance of call rate tracker module  110  can track the counts of both explicit and implicit clock API calls/callbacks, in some cases two separate instances of module  110  may be used for these purposes respectively. Further, the various steps shown in workflow  200  can be sequenced differently, certain steps can be combined as needed, and certain steps can be omitted as needed. One of ordinary skill in the art will recognize other variations, modifications, and alternatives. 
     4. Warping Explicit Clocks 
     As mentioned above, at the time of determining that time dilation should be turned on with respect to JavaScript program  104 , web platform application  102  can leverage explicit clock warp module  114  in order to dilate or warp the time values that are returned by explicit clocks APIs to program  104 , thereby preventing program  104  from observing a consistent view of time via explicit clocks. Generally speaking, explicit clock warp module  114  can use any algorithm to transform the time values returned by the explicit clock APIs (referred to as real time) into the time values observed by JavaScript program  104  (referred to as observed time), as long as program  104 &#39;s observed view of time is non-decreasing. 
     According to one set of embodiments, explicit clock warp module  114  can perform this warping by clamping the time values to a relatively coarse granularity, such as 5 or 20 microsecond intervals, and then randomly jittering the point at which a particular time value is clamped (i.e., performing the clamping at different random times within each clamping period). These concepts are visualized in  FIGS. 3A and 3B  according to an embodiment. In particular,  FIG. 3A  depicts a graph  300  with real time on the x-axis and observed time on the y-axis, where the y values (i.e., time observed by JavaScript program  104 ) are clamped at regular 20 microsecond intervals.  FIG. 3B  depicts a graph  310  where the y values are clamped, but the point at which the clamping occurs is randomly jittered. This results in clamping periods of different random lengths. 
     According to another set of embodiments, explicit clock warp module  114  can perform the warping by using a linear transformation function such as y=ax+b where variables a and b are chosen randomly, or a nonlinear transformation function such as y=ax t +b where variables a, b, and t are chosen randomly. An example nonlinear transformation function is shown as graph  320  in  FIG. 3C . One advantage of using these transformation functions over the random jitter technique is that the delay experienced by JavaScript program  104  is less variable; in the case of the transformation functions, the maximum delay will be defined by the function itself, whereas with random jittering the maximum delay may be as high as twice the clamping interval (depending upon how the clamping turnover points are randomly determined). 
     In various embodiments, the transformation function described above can be made as complex as needed (by, e.g., adding more variables/dimensions) in order to make it difficult for an attacker to reverse-engineer the function and determine how time is being warped. In some embodiments, multiple transformation functions may be spliced together for further security. 
     5. Dynamically Scaling Time Dilation 
       FIG. 4  depicts a workflow  400  that provides additional details regarding the processing that may be performed by web platform application  102  and its constituent components (e.g., call rate tracking module  110 , pause task module  112 , explicit clock warp module  114 ) for dynamically scaling time dilation with respect to JavaScript program  104  according to certain embodiments. Workflow  400  assumes that time dilation has already been turned on for JavaScript program  104 , either by virtue of the dynamic triggering mechanism described in section (3) or via a static (e.g., default) configuration. 
     Blocks  402 - 414  of workflow  400  are substantially similar to blocks  302 - 314  of workflow  300 . In particular, at block  402 , call rate tracking module  110  can start a timer for a current bucket (i.e., time window). While this timer is running, call rate tracking module  110  can receive and count the number of calls/callbacks made by JavaScript program  104  to web platform APIs  106  that either enable program  104  to construct an implicit clock (i.e., a clock based on an arbitrary, internal unit of time measure) or consult an explicit clock (i.e., a clock that is based on hardware signals and represented as, e.g., wall clock time or CPU time) (block  404 ). 
     At block  406 , call rate tracking module  110  can determine that the timer started at block  402  has reached a predefined time limit (e.g., 200 milliseconds) and close the current bucket. In addition, module  110  can record the total number of API calls/callbacks counted during that bucket (block  408 ) and check whether that total number exceeds a predefined threshold (block  410 ). If so, call rate tracking module  110  can mark the bucket as a bad bucket (block  412 ). Otherwise, call rate tracking module  110  can mark the bucket as a good bucket (block  414 ). 
     Once call rate tracking module  110  has marked the bucket appropriately, module  110  can check the number of bad buckets have been encountered within some range of Y recent buckets (block  416 ). Note that this condition is different from that used in the triggering workflow (which looks at consecutive bad buckets), since when scaling time dilation it is generally more useful to look at patterns of behavior over non-contiguous periods of time (to account for scenarios where JavaScript program  104  may temporarily halt or slow down its call rate activity in an attempt to fool mitigation mechanisms). 
     If the number of bad buckets encountered within the last Y buckets is between some low watermark A and some high watermark B, it can be concluded that the call rate behavior of JavaScript program  104  is about the same as before (i.e., has gotten neither better nor worse) and call rate tracking module  110  can return to block  402  in order to start a timer for a new bucket and repeat the preceding steps. Note that in this case, pause task module  112  and explicit clock warp module  114  will continue to insert wait time and warp explicit clock values for JavaScript program  104  in accordance with what they were doing before. 
     On the other hand, if the number of bad buckets encountered within the last Y buckets is greater than the high watermark B, it can be concluded that the call rate behavior/activity of JavaScript program  104  is increasing/getting worse. In this case, high watermark B can be incremented/increased and web platform application  102  can scale up the degree of time dilation applied to JavaScript program  104  (block  418 ). For example, if pause task module  112  was previously inserting a randomly generated amount of wait time into the API execution flow according to a certain range (e.g., 0 to 255 microseconds), module  112  can increase the top value of this range such that the maximum possible wait time is increased. Further, if explicit clock warp module  114  was previously warping the time values returned by explicit clock APIs to program  104  according to some clamping interval and some amount of random jitter, module  114  can increase the clamping interval and/or range of random jitter, such that the time observed by JavaScript program  104  is even further removed from real time. Call rate tracking module  110  can then return to block  402  in order to start a timer for a new bucket and repeat the preceding steps. 
     Finally, if the number of bad buckets encountered within the last Y buckets is less than the low watermark A, it can be concluded that the call rate behavior/activity of JavaScript program  104  is decreasing/getting better. In this case, low watermark A can be decremented/decreased and web platform application  102  can scale down the degree of time dilation applied to JavaScript program  104  (block  420 ). For example, if pause task module  112  was previously inserting a randomly generated amount of wait time into the API execution flow according to a certain range, module  112  can decrease the top value of this range such that the maximum possible wait time is decreased. Further, if explicit clock warp module  114  was previously warping the time values returned by explicit clock APIs to program  104  according to some clamping interval and some amount of random jitter, module  114  can decrease the clamping interval and/or range of random jitter, such that the time observed by JavaScript program  104  is less removed from real time. Call rate tracking module  110  can then return to block  402  in order to start a timer for a new bucket and repeat the preceding steps. 
     Generally speaking, in various embodiments, web platform application  102  can scale up or down the wait time inserted by pause task module  112  and warping performed by explicit clock warp module  114  either independently or in a combined manner according to various policies. For example, if call rate tracking module  110  detects a significant change in call rate behavior with respect to explicit clock APIs but not implicit clock APIs, application  102  may solely scale explicit clock warping. Conversely, if call rate tracking module  110  detects a significant change in call rate behavior with respect to implicit clock APIs but not explicit clock APIs, application  102  may solely scale wait time insertion. As another example, a significant change in behavior with respect to implicit clocks may also cause scaling of explicit clock warping, and vice versa. All of these permutations, and more, are within the scope of the present disclosure. 
     Further, although not shown in  FIG. 4 , in some embodiments web platform application  102  may take special measures if JavaScript program  104  behaves exceptionally well or badly over an extended period of time. For instance, if high watermark B reaches a maximum threshold (indicating that the call rate activity/behavior of JavaScript program  104  has gotten progressively worse), application  102  may simply terminate the process associated with the program rather than continuing to scale the time dilation experienced by the program any further. Similarly, if low watermark A reaches a minimum threshold (indicating that the call rate activity/behavior of JavaScript program  104  has gotten progressively better), application  102  may completely turn off time dilation for program  104 . In this scenario, time dilation may be triggered again per workflow  300  if JavaScript program  104  begins exhibiting bad behavior again in the future. 
     6. Leveraging Telemetry 
     In some embodiments, web platform application  102  can include a telemetry component that enables it to communicate information regarding the time dilation performed with respect to JavaScript program  104  and other downloaded JavaScript code to one or more remote servers. Examples of such information include the web pages/URLs comprising program  104 , the measured call rate activity of program  104 , whether time dilation was triggered for program  104 , whether time dilation was scaled for program  104 , the particular parameters used for the time dilation triggering/scaling, system performance parameters when time dilation was turned on, and so on. 
     The servers can then aggregate this information across a large population of applications/users and determine statistics and trends which can be used for various purposes. For example, in one set of embodiments, the servers can identify a “whitelist” of web pages that are highly unlikely to contain JavaScript code that is attempting to perpetrate a timing attack, as well as a “blacklist” of web pages that are highly likely to contain such malicious JavaScript code. The whitelist and blacklist can then be communicated back to web platform application  102  (and/or to other client applications such as anti-virus/anti-malware software), which can uses these lists to, e.g., block user access to blacklisted sites, turn off time dilation by default for whitelisted sites, and/or implement more relaxed time dilation policies/parameters/rules for whitelisted sites. 
     As another example, the statistics determined by the remote servers can be used to inform and fine tune the time dilation triggering and scaling algorithms described above, such that they perform as expected and without unnecessary or burdensome performance penalties. For instance, the remote servers may determine that one particular algorithm used to perform explicit clock warping results in excessive stuttering on a few popular websites, which may instigate a change to a different algorithm. Further, the remote servers may determine that the scaling algorithm may cause a particular non-malicious website to crash when loaded, which may result in a modification to the scaling parameters to avoid this incompatibility. One of ordinary skill in the art will recognize other possible use cases for this collected data. 
     7. Example Computer System 
       FIG. 5  is a simplified block diagram illustrating the architecture of an example computer system  500  according to certain embodiments. Computer system  500  (and/or equivalent systems/devices) may be used to run any of the software described in the foregoing disclosure, including web platform application  102  of  FIG. 1 . As shown in  FIG. 5 , computer system  500  includes one or more processors  502  that communicate with a number of peripheral devices via a bus subsystem  504 . These peripheral devices include a storage subsystem  506  (comprising a memory subsystem  508  and a file storage subsystem  510 ), user interface input devices  512 , user interface output devices  514 , and a network interface subsystem  516 . 
     Bus subsystem  504  can provide a mechanism for letting the various components and subsystems of computer system  500  communicate with each other as intended. Although bus subsystem  504  is shown schematically as a single bus, alternative embodiments of the bus subsystem can utilize multiple busses. 
     Network interface subsystem  516  can serve as an interface for communicating data between computer system  500  and other computer systems or networks. Embodiments of network interface subsystem  516  can include, e.g., an Ethernet module, a Wi-Fi and/or cellular connectivity module, and/or the like. 
     User interface input devices  512  can include a keyboard, pointing devices (e.g., mouse, trackball, touchpad, etc.), a touch-screen incorporated into a display, audio input devices (e.g., voice recognition systems, microphones, etc.), motion-based controllers, and other types of input devices. In general, use of the term “input device” is intended to include all possible types of devices and mechanisms for inputting information into computer system  500 . 
     User interface output devices  514  can include a display subsystem and non-visual output devices such as audio output devices, etc. The display subsystem can be, e.g., a transparent or non-transparent display screen such as a liquid crystal display (LCD) or organic light-emitting diode (OLED) display that is capable of presenting 2D and/or 3D imagery. In general, use of the term “output device” is intended to include all possible types of devices and mechanisms for outputting information from computer system  500 . 
     Storage subsystem  506  includes a memory subsystem  508  and a file/disk storage subsystem  510 . Subsystems  508  and  510  represent non-transitory computer-readable storage media that can store program code and/or data that provide the functionality of embodiments of the present disclosure. 
     Memory subsystem  508  includes a number of memories including a main random access memory (RAM)  518  for storage of instructions and data during program execution and a read-only memory (ROM)  520  in which fixed instructions are stored. File storage subsystem  510  can provide persistent (i.e., non-volatile) storage for program and data files, and can include a magnetic or solid-state hard disk drive, an optical drive along with associated removable media (e.g., CD-ROM, DVD, Blu-Ray, etc.), a removable or non-removable flash memory-based drive, and/or other types of storage media known in the art. 
     It should be appreciated that computer system  500  is illustrative and other configurations having more or fewer components than computer system  500  are possible. 
     The above description illustrates various embodiments of the present disclosure along with examples of how aspects of these embodiments may be implemented. The above examples and embodiments should not be deemed to be the only embodiments, and are presented to illustrate the flexibility and advantages of the present disclosure as defined by the following claims. For example, although certain embodiments have been described with respect to particular process flows and steps, it should be apparent to those skilled in the art that the scope of the present disclosure is not strictly limited to the described flows and steps. Steps described as sequential may be executed in parallel, order of steps may be varied, and steps may be modified, combined, added, or omitted. As another example, although certain embodiments have been described using a particular combination of hardware and software, it should be recognized that other combinations of hardware and software are possible, and that specific operations described as being implemented in software can also be implemented in hardware and vice versa. 
     The specification and drawings are, accordingly, to be regarded in an illustrative rather than restrictive sense. Other arrangements, embodiments, implementations and equivalents will be evident to those skilled in the art and may be employed without departing from the spirit and scope of the present disclosure as set forth in the following claims.