WEBVTT 00:00.000 --> 00:04.360 So, this is the outline. 00:04.720 --> 00:08.060 Number one, we will introduce our malware scouting system. 00:08.220 --> 00:12.280 And number two, we will show you how we designed the Dalvik bytecode loader. 00:12.300 --> 00:17.180 And number three, we will go through two cases of real malware analysis using Quark Engine. 00:17.380 --> 00:21.760 And number four, we will share our strategy of generating the detection rules. 00:22.080 --> 00:24.800 And the last thing, yes, the feature works. 00:24.800 --> 00:27.140 Because we still have a lot of things to do. 00:27.980 --> 00:31.800 Alright, so let's introduce the malware scoring system. 00:31.980 --> 00:38.500 As we know, when developing a malware analysis engine, it is important to have a scoring system. 00:38.500 --> 00:43.580 However, those systems are either business secrets or just too complicated. 00:43.680 --> 00:50.080 Therefore, we decided to create a simple but solid one and take that as a challenge. 00:50.400 --> 00:59.570 And since we wanted to design a novel scoring system, we stopped reading and decoding whatever people do in the field of cybersecurity. 01:00.000 --> 01:04.570 Because we don't want our ideas to be subjected to the existing systems. 01:05.180 --> 01:10.240 So we started to find ideas in fields other than cybersecurity. 01:10.240 --> 01:12.500 And luckily, we found one. 01:13.000 --> 01:17.200 Yes, the best practice we found is the criminal law. 01:17.720 --> 01:23.740 So, when sentenced a penalty for a criminal, the judge weighs the penalties based on the criminal law. 01:23.740 --> 01:27.320 And after decoding the law, we find principles behind it. 01:27.320 --> 01:31.040 And we develop a scoring system for Android malware. 01:31.960 --> 01:35.420 There are only eight principles decoded from the criminal law. 01:35.420 --> 01:38.020 And I'll go through it in the following slides. 01:38.120 --> 01:40.360 Now let's see principle number one. 01:40.360 --> 01:44.700 A malware crime consists of action and targets. 01:44.920 --> 01:50.220 In the criminal law, the definition of a crime consists of action and targets. 01:50.220 --> 01:53.680 For example, steal money and kill people. 01:53.680 --> 02:00.360 So, with this principle in mind, we develop the definition of a crime for Android malware. 02:00.500 --> 02:05.920 And the definition is, the malware crime consists of action and targets. 02:06.260 --> 02:11.540 For example, steal photos or steal your banking account password. 02:12.620 --> 02:15.000 Now let's see principle number two. 02:15.000 --> 02:19.400 We consider that the loss of fame is greater than the loss of wealth. 02:19.400 --> 02:25.640 So, in the criminal law, physical body injury is more serious than psychological injury. 02:25.700 --> 02:32.560 So, the principle we decoded here is, when things are hard to recover, we consider it a felony. 02:32.900 --> 02:37.240 So, with this principle decoded, we develop our second principle. 02:37.240 --> 02:45.120 We consider the loss of fame is greater than the loss of wealth, because it's easier to make money back than rebuild your reputation. 02:45.820 --> 02:49.760 Okay, now let's see principle number three, arithmetic sequence. 02:50.080 --> 02:59.620 In the criminal law, when a murderer is sentenced to 20 years in prison, and a robber is sentenced to 7 years in prison for his crime, why the number? 02:59.640 --> 03:02.120 Why 20 and 7 years? 03:02.120 --> 03:03.180 Why the number? 03:03.180 --> 03:10.180 We found no obvious principles in the criminal law, so we use arithmetic sequence to weigh the penalty of each crime. 03:10.180 --> 03:17.740 For example, the penalty weight of Y1 is 10, and Y2 is 20, and Y3 is 30, etc. 03:18.800 --> 03:22.520 So, now let's see the most important part of the scoring system. 03:22.600 --> 03:26.720 We create an order theory, which consists of three principles. 03:26.720 --> 03:29.900 Here are principle number four, five, and number six. 03:30.280 --> 03:32.380 Now, let's look at principle number four. 03:32.380 --> 03:36.360 The later the stage, the more we're sure that a crime is practiced. 03:36.360 --> 03:42.920 As mentioned in chapter four of Taiwan criminal law, each crime consists of a sequence of behaviors. 03:42.920 --> 03:46.660 Those behaviors can be categorized in a specific order. 03:47.080 --> 03:49.460 So, let's take murder for example. 03:49.460 --> 03:51.040 Determined in stage one. 03:51.040 --> 03:54.520 Determined means somebody decided to kill someone. 03:54.520 --> 03:56.460 And in stage two, conspiracy. 03:56.460 --> 04:00.320 It means he or she started to make a plan for the murder. 04:00.320 --> 04:02.060 And in stage three, preparation. 04:02.060 --> 04:07.900 It means buying stuff, for example, weapons or arranging services for the murder plan. 04:08.040 --> 04:10.580 And in stage four, starts. 04:10.580 --> 04:16.540 It means when things are all set, the murderer takes action and is on the way to kill someone. 04:16.760 --> 04:18.480 And in stage five, practice. 04:18.480 --> 04:22.100 It means the murderer does pull the trigger and shoot someone. 04:22.880 --> 04:28.000 So, as we can see here, the later the stage, the more we're sure that a crime is practiced. 04:29.440 --> 04:35.320 So, with this principle in mind, we developed Android malware crime author theory. 04:35.560 --> 04:39.760 And in this theory, we also have five stages for the crime. 04:39.760 --> 04:50.720 For example, if a malware tries to send out your location data by using SMS, in stage one, we will check if related permission is requested by the malware. 04:50.720 --> 04:54.760 And then, we will check if the key native API is called. 04:54.760 --> 05:00.040 And in stage three, we will see if a certain combination of native API exists. 05:00.360 --> 05:04.180 And then, we will check if the APIs are called in a specific order. 05:04.420 --> 05:09.700 Finally, we will check if the APIs are handling the same register. 05:10.780 --> 05:16.260 Okay, so now you can see from this picture, this is a two-dimensional map for Android malware crime. 05:16.260 --> 05:19.280 For the crimes, we put them in y-axis. 05:19.280 --> 05:25.200 And for each crime, we use x-axis to see the evidence we caught for the crime. 05:25.240 --> 05:36.140 So, x5, y1 means in crime number one, we have found native APIs that are caught in a correct sequence and are handling the same register. 05:36.300 --> 05:44.880 And x3, y5 means in crime number five, we have found certain combinations of native APIs that is used in this APK. 05:44.880 --> 05:49.540 So, now let's look at principle number five. 05:49.540 --> 05:53.480 The more evidence we caught, the more penalty weight we give. 05:53.480 --> 05:59.620 So, we give stage two more weight than stage one, and stage three more weight than stage two, etc. 06:00.780 --> 06:04.840 Okay, principle number six, proportional sequence. 06:05.020 --> 06:11.500 As we decode it from the criminal law, the later the stage, the more we are sure that a crime is practiced. 06:11.500 --> 06:19.760 So, we consider proportional sequence, for example, 2 to the power of n, to present such principle in our scoring system. 06:19.900 --> 06:24.040 Alright, principle number seven, crimes are independence events. 06:24.040 --> 06:30.660 For simplicity, we assume crimes are independence events, and penalty weights can be added up directly. 06:30.800 --> 06:34.020 So, this is an example of adding up two crimes. 06:34.020 --> 06:39.860 In the malware, we find two crimes, they are stealing photos and stealing your banking account password. 06:39.860 --> 06:43.620 So, the calculation of the total penalty weights is quite simple. 06:43.620 --> 06:53.540 For example, for each crime, we use penalty weights of crime to multiply proportion of caught evidence and add up the results of the two. 06:54.040 --> 06:58.860 The last principle, principle number eight, thresholds generate system. 06:58.860 --> 07:08.520 So, after calculating the total penalty weights for a malware, we need to have threat level threshold, so that we can tell which threat level does the malware fill in. 07:08.520 --> 07:18.820 Unfortunately, we can't find them in criminal law, but we know we need to design a threshold-generated system for that, not just give any number by intuition. 07:19.180 --> 07:30.400 So, we decided that threshold for each level is the sum of the same proportion of caught evidence multiplies penalty weights of crimes. 07:30.400 --> 07:37.100 We know it's not a perfect one, but we're sure that we build a foundation for future optimization. 07:37.100 --> 07:42.580 Alright, now let's talk about the design logic of Delphi Bytecode Loader. 07:43.260 --> 07:47.000 And Junwei, he will take care of this part. 07:48.020 --> 07:53.440 Hello everyone, my name is Junwei, and I will take care of this part. 07:53.560 --> 07:59.300 So now, let's talk about the design logic of Delphi Bytecode Loader. 08:00.300 --> 08:08.680 Our Delphi Bytecode Loader is actually the implementation of the Android malware crime order theory. 08:08.680 --> 08:12.280 We implement every stage of the theory. 08:12.280 --> 08:14.060 There are five stages. 08:14.060 --> 08:16.600 The first three stages are easy. 08:16.600 --> 08:25.300 We simply use APIs in another open source tool, Android Guard, to implement the first three stages. 08:25.300 --> 08:31.300 As I just mentioned, the implementation of the first three stages are easy. 08:31.300 --> 08:35.300 But in stage 4, we need to do a little bit more. 08:35.440 --> 08:40.600 So before the implementation, we need to know what does stage 4 do. 08:41.000 --> 08:50.300 In stage 4, we find the calling sequence of native APIs and check if they are called in a specific order. 08:50.300 --> 09:12.740 For example, if a malware sends out your location data by SNS, then first it will call native API getCellLocation to get your location data, and then call native API sendTextMessage to send your location data by SNS. 09:12.740 --> 09:17.940 Normally, native APIs are wrapped in functions. 09:17.940 --> 09:24.460 So we trace back to see which function is cross-referenced from the native APIs. 09:24.460 --> 09:28.960 And we call those functions a parent function. 09:29.020 --> 09:35.930 And we will keep tracing back until we find a mutual parent function for both the native APIs. 09:36.320 --> 09:38.660 Here is the example. 09:38.660 --> 09:47.600 sendTextMessage is called by sendSNS, which is the parent function of sendTextMessage. 09:47.600 --> 09:56.400 And getCellLocation is called by getLocation, which is the parent function of getCellLocation. 09:56.400 --> 10:08.120 And if we keep tracing back, we will see that both sendSNS and getLocation shares the same parent function, sendTextMessage. 10:08.800 --> 10:21.820 And after we find the mutual parent function, we will scan through a small light code of the mutual parent function and check which function is called first. 10:21.820 --> 10:26.180 So this is the small light code of sendMessage. 10:26.400 --> 10:33.720 We can see that getLocation is called first to get location data of the cell phone. 10:33.840 --> 10:38.980 And sendSNS is called to send out the location data. 10:40.220 --> 10:49.900 And in stage 4, we found that our design can also overcome the obfuscation techniques used by the malware. 10:49.900 --> 10:56.940 When applying obfuscation techniques, functions except native APIs are renamed. 10:57.080 --> 11:02.220 This has made the decompiled source code hard to read for humans. 11:02.620 --> 11:08.140 A machine can still run the code because the logic of the code remains the same. 11:08.520 --> 11:10.840 Here is the example. 11:10.840 --> 11:19.500 When applying obfuscation techniques, native API sendTextMessage is called by function k. 11:19.500 --> 11:23.720 And function k is called by function f. 11:23.720 --> 11:29.500 The alternative API getCellLocation is called by function e. 11:29.500 --> 11:36.320 And both function e and f share the same parent function, which is a. 11:36.320 --> 11:45.660 You see, if you start reading the decompiled source code of a, it will be hard to figure out what is going on there. 11:45.660 --> 11:56.940 And by the way, since our goal is to find the mutual parent function, so it doesn't matter how many layers the wipers are. 11:57.900 --> 12:02.320 Now, let's see the implementation of stage 5. 12:02.320 --> 12:05.520 Yes, this is the most important part. 12:05.520 --> 12:12.840 In stage 5, we need to confirm that if the native APIs are handling the send register. 12:12.840 --> 12:15.880 Let's use the send example. 12:15.880 --> 12:20.480 Send out your location data by using SNS. 12:20.500 --> 12:29.360 So when native API getCellLocation is called, it will return the location data of the cell phone. 12:29.360 --> 12:42.420 And what we do in stage 5 is to check if the alternative API sendTextMessaging sends out the location data returned from getCellLocation. 12:43.040 --> 12:48.440 So in stage 5, we simulate the CPU operation. 12:48.440 --> 12:57.860 We read line by line of the Somali-like source code and operate like CPUs to get two things. 12:57.860 --> 13:02.080 First, the value of every register. 13:02.080 --> 13:08.500 Second, the information like functions who have operated the send register. 13:08.680 --> 13:13.820 To make this happen, we create a self-defined data type. 13:13.820 --> 13:16.800 We call it register object. 13:16.880 --> 13:22.080 In each register object, we store three kinds of information. 13:22.080 --> 13:24.680 No.1, the register name. 13:24.680 --> 13:27.740 No.2, the value of register. 13:27.740 --> 13:31.840 And No.3, the function who use this register. 13:31.840 --> 13:34.420 Let's see an example. 13:34.600 --> 13:37.740 So the register name is fee7. 13:37.740 --> 13:41.580 And the value of the register is a string. 13:41.580 --> 13:48.780 And the string appends the value of string1 and the result of function1. 13:48.780 --> 13:56.660 And then we can see that the register is used as the input resource of the function2. 13:56.780 --> 14:11.620 By the way, when filling the value of useByWhich function in the register object, we extend every register by cross-referencing other register objects in the table. 14:11.620 --> 14:21.040 So, for example, by cross-referencing, we know that fee8 is a string called userLocation. 14:21.040 --> 14:25.080 And fee3 is a function called getLocation. 14:25.260 --> 14:36.200 As you can see in the lower right corner, the result of getLocation is appended to the string which is userLocation. 14:36.200 --> 14:42.660 And the new string is sent out by using function sendSns. 14:42.660 --> 14:55.620 In other words, the value of register fee7 is generated by using function getLocation, which has native API 1 in it. 14:55.620 --> 15:05.420 And the value is used as an input for function sendSns, which has native API 2 in it. 15:05.420 --> 15:15.820 So now, we prove that by using the register object, we can check if the APIs are handling the same register. 15:15.820 --> 15:22.880 So after we scan through the source code, we produce dots of register objects. 15:23.180 --> 15:30.620 And those register objects will be organized when a two-dimensional Python list. 15:30.740 --> 15:34.320 It is a similar idea like hash table. 15:34.320 --> 15:39.600 We use it to boot out the read and write of the list. 15:39.600 --> 15:42.780 So now, let's see the table. 15:42.820 --> 15:49.200 As you can see here, register fee4 has three register objects. 15:49.200 --> 15:55.220 That means in the source code we scanned, fee4 was used three times. 15:55.220 --> 16:04.720 And every time when it was used, we store the present value of register and the function who used it if there is one. 16:04.800 --> 16:09.820 So basically, the whole table is the history of the registers. 16:10.520 --> 16:22.740 So when we finish constructing the table, we then scan through all register objects in the table to check if the native APIs are handling the same registers. 16:22.740 --> 16:28.520 So now, let's see how to use Quark engine to analyze the malware. 16:28.660 --> 16:31.660 Now, let's get back to Kunyu. 16:35.330 --> 16:38.850 In this section, we prepared two malware. 16:38.850 --> 16:43.430 One is non-obfuscated and the other one is obfuscated. 16:43.430 --> 16:49.550 And for each malware, we will show how we detect the behavior of the malware with the detection rule. 16:49.750 --> 16:51.710 Now let's look at the first malware. 16:51.710 --> 16:54.070 This is a non-obfuscated one. 16:54.070 --> 17:01.870 We will use the rule in Quark engine to detect whether if the malware sends out the cell phone location data by using SMS. 17:02.210 --> 17:06.190 So this is the detailed report of Quark engine. 17:06.190 --> 17:14.250 In this report, the engine shows the detection results of one single malware behavior or you can say one single malware crime. 17:14.770 --> 17:20.750 For example, we try to find if the malware sends out your location data by using SMS. 17:20.750 --> 17:27.690 So in this report, we list out the evidence we found in each stage of the Android malware crime order theory. 17:27.690 --> 17:36.530 And this report shows we find evidence in every stage, which means we have 100% of confidence that the malware has this behavior. 17:36.530 --> 17:37.530 So let's see. 17:37.530 --> 17:45.030 In stage 1, permissions like send SMS, access course location, and find locations are requested. 17:45.030 --> 17:51.710 In the second stage, the key native APIs like getCellLocation and sendTextMessages are used. 17:51.710 --> 17:56.350 And in stage 3, we find certain combinations of native API exist. 17:56.350 --> 18:03.790 And in stage 4, we found out that in functions like sendMessage and doBytes, the APIs are called in the right sequence. 18:03.790 --> 18:11.990 And in stage 5, in function sendMessage, we find out that those APIs are handling the same register. 18:12.650 --> 18:21.790 So now let's think, if you are analyzing this malware, and you want to trace the compiled source code to see the evidence, how do you do it? 18:21.830 --> 18:25.470 Our suggestion is you start backwards. 18:25.630 --> 18:28.670 That means you start from stage 5. 18:28.670 --> 18:40.670 For example, in stage 5, we know that inside a function of sendMessage, it has two functions that contains the two native APIs respectively, and they are handling the same register. 18:40.670 --> 18:46.810 So you start to locate a function sendMessage in the decompiled source code. 18:47.110 --> 18:52.030 And in stage 4, we know that those two functions are called in the right sequence. 18:52.030 --> 18:59.910 So we can start to find the functions that contains the native APIs and check if they are really called in the right sequence. 19:00.010 --> 19:05.430 The information of the two functions and the sequence will be shown in the next version of Quark Engine. 19:06.370 --> 19:09.650 So now let's look at a real example. 19:09.650 --> 19:12.270 Let's locate the function sendMessage. 19:12.270 --> 19:17.810 And we found out that two functions that contains the two native APIs respectively. 19:17.990 --> 19:20.690 They are sendSMS and getLocation. 19:20.690 --> 19:27.810 And if we dive into the function of getLocation, we will see that it contains native API getLocation. 19:27.970 --> 19:36.050 And if we dive in this function of sendMessage, sendSMS, we will see that it contains native API sendTextMessage. 19:36.050 --> 19:42.490 So the code here means it first collects your cell phone location data and sends it out through SMS. 19:43.270 --> 19:46.130 So now let's dive into the source code of getLocation. 19:46.130 --> 19:55.070 As you can see in the source code, it tries to call native API getCellLocation and returns this information at the end of the code. 19:55.830 --> 19:59.670 And now let's dive into the source code of sendSMS. 19:59.670 --> 20:04.890 Native API sendTextMessage is used to send out the location information. 20:04.890 --> 20:06.650 So quite simple, isn't it? 20:06.690 --> 20:09.690 Now let's look at the second malware. 20:09.690 --> 20:12.310 This is an obfuscated one. 20:12.370 --> 20:22.470 We will use the rule in Quark Engine to find whether if the malware detects Wi-Fi hotspot by gathering information like active network info and cell phone location. 20:22.950 --> 20:27.670 Okay, so as a malware analyst, we read the report backwards. 20:27.730 --> 20:33.950 As you can see in stage 5, there are functions like p.a, addView.c, and af.run. 20:33.950 --> 20:41.710 And those functions have two functions that contain native APIs respectively, and they are handling the same register. 20:41.710 --> 20:51.090 And in stage 4, those two functions are also called in the right sequence in functions like p.a, addView.c, and af.run. 20:51.090 --> 20:59.690 So according to the report, we can see that malware has the behavior of Wi-Fi hotspot detection in three parts of the source code. 20:59.690 --> 21:02.630 And we can pick any parts for further analysis. 21:02.630 --> 21:05.870 So we just pick function p.a. 21:06.730 --> 21:09.830 So now let's look in... now let's see the source code. 21:09.830 --> 21:13.210 Let's first locate the function p.a. 21:13.210 --> 21:21.150 And we found out that two functions that contains the two native APIs respectively, they are ap.a and af.f. 21:21.150 --> 21:29.230 And if we dive into the source code of function ap.a, we will see that it contains native API getActiveNetworkInfo. 21:29.230 --> 21:36.250 And if we dive into the function of af.f, we will see that it contains native API getCellLocation. 21:36.270 --> 21:47.410 So this quote here means, after collecting information from function ap.a and af.f, they sent the information as an input for function am.a. 21:47.770 --> 21:51.210 So now let's dive into the source code of function ap.a. 21:51.210 --> 21:59.870 As you can see in the source code, it tries to call native API getActiveNetworkInfo and return the related information at some points. 22:00.290 --> 22:04.270 So now let's dive into the source code of af.f. 22:04.470 --> 22:10.330 Native API getCellLocation is used to get the cell phone location data. 22:10.330 --> 22:13.550 And this information is processed with some other strings. 22:13.550 --> 22:18.430 And at the end of this function, it returns the strings with the information. 22:18.430 --> 22:30.310 As mentioned earlier, after collecting information from function ap.a and af.f, they used the information as an input for function am.a. 22:30.390 --> 22:33.350 And we noticed one thing. 22:33.350 --> 22:39.830 The function am.a used byte arrayed output string as one of its input parameter. 22:39.830 --> 22:48.190 And we know when seeing byte arrayed output string, it means the function is probably trying to write the data into a file. 22:48.190 --> 22:50.410 This is amazing, isn't it? 22:50.410 --> 22:56.270 So with Quark engine, malware analysis can really boost up their productivity. 22:57.170 --> 23:02.190 Okay, so now I will introduce our detection rule generate strategy. 23:02.470 --> 23:06.690 So why do we need to develop the detection rule generate strategy? 23:07.210 --> 23:15.170 Because to make our engine practical and easy to use, we need to have more detection rules. 23:15.170 --> 23:19.330 However, the speed of rule generated by human is quite slow. 23:19.590 --> 23:25.950 And human generated rules is subjected to his or her experiences of malware analysis. 23:26.070 --> 23:31.270 So we developed a rule generate strategy to boost up the production of detection rules. 23:31.670 --> 23:44.510 And since our goal is to find all kinds of behaviors in the malware, so if we use permissions and native APIs to generate all possible rules, we will have an amazing amount of rules. 23:44.510 --> 23:53.290 After generating the rules, we then use Quark engine to find the intersection between those amazing amounts of rules and the malware we prepared. 23:53.290 --> 23:57.010 In other words, we find rules that match the malware behaviors. 23:57.090 --> 24:03.630 However, this is not a good way to generate detection rules because it's time and resource consuming. 24:03.770 --> 24:07.910 So we developed a seven step generate strategy. 24:07.910 --> 24:15.210 Step one, we curl down all native API information on Android official API reference. 24:15.210 --> 24:20.210 For example, this is the native API information of send text message. 24:20.890 --> 24:26.410 You can see the input parameters, return value and a description of this API. 24:27.070 --> 24:32.070 Okay, step two, we did a little bit modification to our engine. 24:32.070 --> 24:37.890 We ignore the permission checks in stage one of the Android malware crime order theory. 24:37.970 --> 24:45.450 And in step three, we find all kinds of API combination and generate rules without permission information. 24:46.070 --> 24:52.710 And in stage four, we use the modified Quark engine to find intersections of the rules and the malwares. 24:52.710 --> 24:59.150 We call the rules... we call rules in intersection the first stage verified rules. 24:59.150 --> 25:03.930 In other words, these rules need to be verified again. 25:04.450 --> 25:13.550 And since we don't need to generate rules with permission and verify the permissions in Quark engine, the whole process of rule production speed up. 25:13.870 --> 25:17.990 And in stage five, we try to generate rules with permissions. 25:18.130 --> 25:24.790 So inside the intersection, we have first stage verified rules matched with malwares. 25:24.790 --> 25:34.590 We then use the first stage rules and permission in the matched malware to generate rules with permissions, which is the second stage rules. 25:35.150 --> 25:39.730 And in step six, we then use the Quark engine. 25:39.730 --> 25:52.050 Yes, the full function version to find again, the intersection of the second stage rules, which are the one with permission and the malware we prepared. 25:52.050 --> 25:58.410 So after that, for each rule, we label the number of matched malware. 25:58.430 --> 26:05.530 For example, the behavior of rule number one can be found in a hundred malware, etc. 26:06.110 --> 26:13.450 And in step seven, after labeling the rules, we then sort the rules by number of matched malware. 26:13.730 --> 26:19.030 We will review the rules from the highest matched one to the lowest one. 26:19.910 --> 26:23.250 All right, the last part, feature works. 26:24.670 --> 26:28.630 As I mentioned earlier, we still have a lot of things to do. 26:28.630 --> 26:37.770 For example, we need to have more detection rules, and we also need to deal with the .so files and the packed APKs. 26:37.830 --> 26:43.710 And we want to have more features of Dalvik Bytecode Loader, for example, the downloader. 26:43.710 --> 26:51.090 And we also want to apply the scoring system to other binary formats. 26:51.150 --> 26:52.950 Yeah, it is in our to-do list. 26:53.410 --> 26:57.730 And we noticed that API changes in different versions of Android. 26:57.730 --> 27:01.770 So we will also take care of this problem in the next version of Quark. 27:01.850 --> 27:08.050 Oh, we probably would change the core library since Android God is quite inactive recently. 27:08.650 --> 27:10.410 And one more thing. 27:10.710 --> 27:15.630 Actually, we are trying to make Quark itself easier to be integrated to other tools. 27:15.630 --> 27:23.230 For example, users can import Quark as a Python library and output the analysis result as a JSON file. 27:23.230 --> 27:31.590 So now Quark is collected in BlackArch Linux and will soon be integrated to a threat intelligence analysis tool, Intel O. 27:32.470 --> 27:35.310 And there is one nugget that I would like to share. 27:35.310 --> 27:37.710 We work at the limits of our tools. 27:37.710 --> 27:41.030 When new tools come along, new things are possible. 27:41.390 --> 27:43.630 Okay, so that's all for today. 27:43.630 --> 27:49.130 And if you have any questions, please feel free to DM our Twitter accounts. 27:49.130 --> 27:50.310 Thank you.