1 1 00:00:00,330 --> 00:00:02,220 Profiling techniques. 2 2 00:00:02,220 --> 00:00:04,140 When we talk about profiling techniques, 3 3 00:00:04,140 --> 00:00:06,600 our goal here is to identify who works 4 4 00:00:06,600 --> 00:00:08,190 at a particular company. 5 5 00:00:08,190 --> 00:00:10,080 We can do this a lot of different ways 6 6 00:00:10,080 --> 00:00:12,330 including gathering all those different emails 7 7 00:00:12,330 --> 00:00:13,860 that are out there on the internet, 8 8 00:00:13,860 --> 00:00:15,750 as well as looking at social media. 9 9 00:00:15,750 --> 00:00:17,880 Let's first talk about emails. 10 10 00:00:17,880 --> 00:00:20,370 Email harvesting is an open source intelligence 11 11 00:00:20,370 --> 00:00:21,960 or OSINT technique that's used 12 12 00:00:21,960 --> 00:00:24,870 to gather email addresses from a given domain. 13 13 00:00:24,870 --> 00:00:25,980 So for example if I wanted 14 14 00:00:25,980 --> 00:00:27,330 to find all the people who worked 15 15 00:00:27,330 --> 00:00:29,700 at Dion Training, I can start finding email 16 16 00:00:29,700 --> 00:00:33,150 addresses online by searching for @diontraining.com. 17 17 00:00:33,150 --> 00:00:34,500 And based on those addresses, 18 18 00:00:34,500 --> 00:00:36,480 I might be able to start gathering who else works 19 19 00:00:36,480 --> 00:00:38,190 at that company and get information there 20 20 00:00:38,190 --> 00:00:40,080 to build my list as I start trying 21 21 00:00:40,080 --> 00:00:42,420 to profile the people at this organization. 22 22 00:00:42,420 --> 00:00:45,810 For example, if I find jason@diontraining, 23 23 00:00:45,810 --> 00:00:47,310 I can start figuring out a couple of things. 24 24 00:00:47,310 --> 00:00:50,010 One, Jason works there, and two, 25 25 00:00:50,010 --> 00:00:52,020 the format of those email addresses, 26 26 00:00:52,020 --> 00:00:54,870 probably people's first name @diontraining. 27 27 00:00:54,870 --> 00:00:58,170 Or what if I found one that was jdion@diontraining? 28 28 00:00:58,170 --> 00:00:59,490 Well, that tells me they're probably using 29 29 00:00:59,490 --> 00:01:01,290 a first letter of the first name 30 30 00:01:01,290 --> 00:01:03,660 and then their last name as part of their email address. 31 31 00:01:03,660 --> 00:01:05,280 And based on that I can start guessing 32 32 00:01:05,280 --> 00:01:06,900 other people that work there. 33 33 00:01:06,900 --> 00:01:09,930 Maybe I have one that's Jason.dion@diontraining. 34 34 00:01:09,930 --> 00:01:12,090 That tells me it's first name dot last name 35 35 00:01:12,090 --> 00:01:13,920 and again, I can start guessing those addresses 36 36 00:01:13,920 --> 00:01:16,650 because if I know the CEO's name is John Smith, 37 37 00:01:16,650 --> 00:01:20,640 then probably his email is john.smith@diontraining.com 38 38 00:01:20,640 --> 00:01:22,200 and I can start attacking that person 39 39 00:01:22,200 --> 00:01:24,510 with some kind of a social engineering campaign. 40 40 00:01:24,510 --> 00:01:26,190 Now, some companies realize this 41 41 00:01:26,190 --> 00:01:28,560 and they start making very confusing email addresses, 42 42 00:01:28,560 --> 00:01:32,250 like jd1234@diontraining.com. 43 43 00:01:32,250 --> 00:01:33,690 Based on that, it'd be very hard 44 44 00:01:33,690 --> 00:01:35,730 for me to start guessing who works there. 45 45 00:01:35,730 --> 00:01:38,970 I wouldn't know if the guy's name was John or Jason or Joey, 46 46 00:01:38,970 --> 00:01:41,610 but I know it's JD, and that's probably some initials. 47 47 00:01:41,610 --> 00:01:43,590 And then this number is just randomized, and so 48 48 00:01:43,590 --> 00:01:45,900 that makes it very hard to figure out who it is. 49 49 00:01:45,900 --> 00:01:48,030 Now, most companies aren't doing this though. 50 50 00:01:48,030 --> 00:01:48,863 Why? 51 51 00:01:48,863 --> 00:01:50,550 Because this makes it harder for their employees 52 52 00:01:50,550 --> 00:01:52,483 to email each other too, 53 53 00:01:52,483 --> 00:01:53,970 because they don't know each other's names then, right? 54 54 00:01:53,970 --> 00:01:55,980 And so there is usually a standard convention 55 55 00:01:55,980 --> 00:01:57,630 like a first name, a last name, 56 56 00:01:57,630 --> 00:01:59,160 a first letter and a last name, 57 57 00:01:59,160 --> 00:02:01,020 or a first name dot a last name, 58 58 00:02:01,020 --> 00:02:02,610 or something of that nature. 59 59 00:02:02,610 --> 00:02:04,020 Now, some other things you can look at when 60 60 00:02:04,020 --> 00:02:06,558 you start looking at these different emails is 61 61 00:02:06,558 --> 00:02:07,391 some other ways that they're done. 62 62 00:02:07,391 --> 00:02:11,130 For instance, our company uses support@diontraining.com 63 63 00:02:11,130 --> 00:02:12,480 and you know that's a valid address 64 64 00:02:12,480 --> 00:02:14,340 that goes to lots of different people. 65 65 00:02:14,340 --> 00:02:16,050 But if you emailed something like 66 66 00:02:16,050 --> 00:02:18,840 operations@diontraining.com, that might work too 67 67 00:02:18,840 --> 00:02:20,910 and it might get to my operations team. 68 68 00:02:20,910 --> 00:02:22,980 Or you might have instructor@diontraining 69 69 00:02:22,980 --> 00:02:25,320 and you guess things based on the person's position, 70 70 00:02:25,320 --> 00:02:27,180 that might be something that's there. 71 71 00:02:27,180 --> 00:02:29,580 Or if you have things like that, you might guess 72 72 00:02:29,580 --> 00:02:32,460 that the COO or the CEO has their email address 73 73 00:02:32,460 --> 00:02:36,690 of coo@diontraining.com for your chief operating officer. 74 74 00:02:36,690 --> 00:02:38,880 And by guessing these things, you can start then 75 75 00:02:38,880 --> 00:02:41,476 building up your campaigns and figuring things out. 76 76 00:02:41,476 --> 00:02:44,220 Now, there are many different ways to gather email 77 77 00:02:44,220 --> 00:02:46,290 addresses when you're doing this email harvesting, 78 78 00:02:46,290 --> 00:02:48,060 besides just sitting there and guessing them 79 79 00:02:48,060 --> 00:02:49,440 like I was just doing. 80 80 00:02:49,440 --> 00:02:51,270 We can go and buy them from spammers 81 81 00:02:51,270 --> 00:02:53,790 or legitimate sites as sales leads 82 82 00:02:53,790 --> 00:02:56,513 and we would get a list of 10,000 emails for $20, 83 83 00:02:56,513 --> 00:02:58,890 and then we could start going through those. 84 84 00:02:58,890 --> 00:03:00,990 Or we might go to Google and we start searching 85 85 00:03:00,990 --> 00:03:02,520 for things like I said before. 86 86 00:03:02,520 --> 00:03:05,520 For example, you might search for a star@diontraining.com 87 87 00:03:05,520 --> 00:03:07,830 on Google to see what comes back anywhere 88 88 00:03:07,830 --> 00:03:10,710 that we have an @diontraining.com email address 89 89 00:03:10,710 --> 00:03:12,570 listed on some website, some piece 90 90 00:03:12,570 --> 00:03:14,910 of social media or something like that. 91 91 00:03:14,910 --> 00:03:17,100 Or you can actually test these emails 92 92 00:03:17,100 --> 00:03:18,750 by checking if they're going to bounce back 93 93 00:03:18,750 --> 00:03:21,480 by going through and using an email dossier. 94 94 00:03:21,480 --> 00:03:24,570 As I do pen tests, I love to use centralops.net. 95 95 00:03:24,570 --> 00:03:26,370 I can put in an email address here 96 96 00:03:26,370 --> 00:03:28,350 and it will actually test that email and tell me 97 97 00:03:28,350 --> 00:03:31,620 if it's valid before I ever send an email to that server. 98 98 00:03:31,620 --> 00:03:33,630 This way, I can prepare my attacks 99 99 00:03:33,630 --> 00:03:35,130 as part of my reconnaissance phase 100 100 00:03:35,130 --> 00:03:37,770 and weaponization before I move into delivery. 101 101 00:03:37,770 --> 00:03:40,380 Now, once the attacker has this list created, 102 102 00:03:40,380 --> 00:03:41,970 now they can start using that 103 103 00:03:41,970 --> 00:03:43,800 in social engineering attempts, 104 104 00:03:43,800 --> 00:03:46,200 and there's lots of different ways you can do this as well. 105 105 00:03:46,200 --> 00:03:49,050 For example, I might take some of those email addresses 106 106 00:03:49,050 --> 00:03:51,270 and use them to find you on social media. 107 107 00:03:51,270 --> 00:03:52,710 Now that I have you on social media, 108 108 00:03:52,710 --> 00:03:54,360 I can build up a friendship and trust 109 109 00:03:54,360 --> 00:03:55,620 and then conduct additional 110 110 00:03:55,620 --> 00:03:58,140 social engineering attempts against you there. 111 111 00:03:58,140 --> 00:03:59,880 By using this information that I've gathered 112 112 00:03:59,880 --> 00:04:02,190 from this email harvesting, it gives me a foothold 113 113 00:04:02,190 --> 00:04:04,350 and a way into your organization to be able 114 114 00:04:04,350 --> 00:04:06,510 to start finding out more details about you, 115 115 00:04:06,510 --> 00:04:07,770 about the people you employ, 116 116 00:04:07,770 --> 00:04:10,140 and what weaknesses you may have. 117 117 00:04:10,140 --> 00:04:12,180 Now, another way we can do this is we can actually 118 118 00:04:12,180 --> 00:04:15,060 start taking that information from the social media sites 119 119 00:04:15,060 --> 00:04:17,460 and use OSINT software to collect it all for us 120 120 00:04:17,460 --> 00:04:19,740 because this can be really time consuming otherwise, 121 121 00:04:19,740 --> 00:04:21,180 and we want to have a way to aggregate 122 122 00:04:21,180 --> 00:04:22,710 and process all that data 123 123 00:04:22,710 --> 00:04:24,180 from all these different sites 124 124 00:04:24,180 --> 00:04:25,650 to create this detailed picture 125 125 00:04:25,650 --> 00:04:27,600 of the user's interests, their habits, 126 126 00:04:27,600 --> 00:04:30,600 their geographic locations, and other things like that. 127 127 00:04:30,600 --> 00:04:31,680 And just three of those sites 128 128 00:04:31,680 --> 00:04:33,090 that are used by attackers are ones 129 129 00:04:33,090 --> 00:04:37,830 like these, pipl.com, peeku.com, and echosec.net. 130 130 00:04:37,830 --> 00:04:40,170 All of these are open source intelligent tools 131 131 00:04:40,170 --> 00:04:42,480 that do this aggregation for you to find out 132 132 00:04:42,480 --> 00:04:45,930 and build these profiles on those users for a given company. 133 133 00:04:45,930 --> 00:04:48,330 Now, the last one I want to talk about is the harvester 134 134 00:04:48,330 --> 00:04:50,190 and the harvester is one you'll actually talk 135 135 00:04:50,190 --> 00:04:52,980 about and learn about inside pen test plus. 136 136 00:04:52,980 --> 00:04:56,100 This is a command line tool used by penetration testers 137 137 00:04:56,100 --> 00:04:57,840 to gather subdomain information 138 138 00:04:57,840 --> 00:05:00,900 and email addresses across an organization 139 139 00:05:00,900 --> 00:05:02,340 as they're trying to do their pen test 140 140 00:05:02,340 --> 00:05:03,780 to gather information for a 141 141 00:05:03,780 --> 00:05:05,610 follow on social engineering attack. 142 142 00:05:05,610 --> 00:05:07,960 Now for the exam, everything I covered 143 143 00:05:08,917 --> 00:05:11,146 in this lesson is not really going to be tested. 144 144 00:05:11,146 --> 00:05:12,030 You're not going to get a lot of questions on them, 145 145 00:05:12,030 --> 00:05:13,410 but you should be thinking about these 146 146 00:05:13,410 --> 00:05:16,173 as you're building your profiles and your scenarios 147 147 00:05:16,173 --> 00:05:17,940 as part of threat hunting and threat modeling, 148 148 00:05:17,940 --> 00:05:21,706 because they are ways and attack vectors that could be used 149 149 00:05:21,706 --> 00:05:23,513 by an attacker to target your organization.