Welcome back, everyone. Today, we're going to be talking about fuzzy hashing and specifically the tool SS deep. So you might be thinking, what is fuzzy hashing? Well, let's first take a look at kind of traditional or regular hashing using MD5 sum. So MD5 sum creates MD5 hashes on OSX, just MD5. On Windows, there's lots of different tools that can create MD5 hashes. And right now I have several text files inside this folder. So if I run MD5 sum against all of the text files, I get this string, this hexadecimal string. And these, each of these is called a hash value. Okay, so these are MD5 hashes. And what we can do with these basically MD5 sum is reading in the data from each of those text files, the at the, at the binary level, and feeding that into the MD5 hashing algorithm. So the idea of a hash is that for all of the data that comes in, the hash basically takes that data and produces a unique output. Okay, so for basically any data that's exactly the same as whatever is in this 10 period file, this email, if we had another email that was exactly the same, we would get exactly the same hash value output. Now, don't think about the text, think about the underlying data that's that's in there. So comparing it at the bit level, if we have two files that are exactly the same at the binary level, we should get the same hash output. Okay, so in this case, if we look at it, all of these different hashes, we can do a quick kind of check and we see that none of these hashes are the same. So none of the data is exactly the same. Okay, now, these types of hashes are very good for lots of different things in digital forensics. First off, it's very fast. So we can, if we know the hash value of a known good or a known bad file, we can use that hash value to very quickly search through a disk or a bunch of files and see if any files match the known hash value. If they do, then we can either filter them out or focus on them, depending on what type of file it is or what we want to do with it. Okay, now the problem here is that some of these files are very, very similar to each other, right? But the hash value doesn't care about similarity, it cares about exactness. Okay, so sometimes it's interesting to find files that are similar. So for example, if someone was modifying documents, or let's say if they took intellectual property and they kind of changed it a little bit, but they actually stole the original intellectual property and we want to define the original or even the modified version, we want to know about similarities, not exact matches in some cases. Let me remove this. So what I'm going to do here, I have four different text files and I'm going to focus on email 26. So I'll just go in here and show you what it is. Email 26, we can see here this is the email header. These emails are actually from the Enron test data set. We have the email header, and then we have the body of the email. If we look at email 17, same thing, or close to the same we have the email header, and then we have the body of the text, and we can see that the text is very different. So if you look, this is several paragraphs, this is basically just one paragraph with different people in the email, things like that. Okay, so between 17 and 26, there's not really much similarity. Okay, but imagine that I took email 26, and I modified it slightly. Well, it looks like pretty much the same email, except we can, we can kind of see jumping back and forward, probably what changed. But there is a slight modification and I made a slight modification to it. So let's just pretend that I want to change the contents of this email but still make it look like the original email. Okay, so we have the original and we have the modified versions and we have two other emails that are basically not related to this. Okay. Now, out of all of these, I want to know which ones are similar and how similar are they. Well, we can use something called fuzzy hashing. And the tool that we're talking about is SS deep. And I'll put a link below with a link below to SS deep. So SS deep does what we call fuzzy hashing. Now if we do SS deep star, then it should hash all of the files in this directory. And we can see that the hashes are quite a bit different. So let me do md5 sum again. So md5 sum gives me a hex decimal value, relatively short. And then for SS deep, we get this type of hash value. So this is the hash. For SS deep. And what we can do with this is instead of comparing to see if this number and this number are exactly the same. We can basically use this to compare with other five other other fuzzy hashes to see how similar they actually are. Okay, so we're using the the hash values to see how similar two different files are. Okay. So what I can do here is do SS deep. And then I want to hash everything and save it to let's call it fuzzy dot ssd. And this is where I'm going to save all of the hashes too. So I'm running SS deep getting all the hash values and then saving the output to fuzzy ssd. And I'm going to suppress any. Okay, I'll just do it. Okay, then it says did not process files large enough to produce meaningful results. Sometimes this can basically you have to have quite a bit of data to do reliable fuzzy hashing. In this case, the emails that should work. But if your files are too small, you might have some problems. Okay, so now I have, if we look inside the hash, hash file that I just created, we see the SS deep header, we see the fuzzy hashes for the four different text files that I have in the same directory. Okay, now if I look, I want to. Find out if any of these files actually match, or are somehow related so I can do SS deep dash m and then feed it my fuzzy hashes that I've just the fuzzy hash database I've just created. I want to hash or compare to all of the files in the directory and I'm going to suppress any error messages here. Okay, so enter. Now we have a couple interesting matches here. So you notice here demo text 10 period matches demo text 10 period 100%. Well, we would expect that. But 10 is not matching anything else. Okay, so whenever we look 10 doesn't match anything else. It's not similar in any way. 17 same thing 17 doesn't match. Basically in any other way. Then we have 26. So 26 matches 26 100% we expect that 26 matches 26 mod 99%. Okay, so something in that file changed and not very much that the match is very high, very good match here. Same 26 mod matches 26 99%. Okay, that's just the reverse and 26 mod matches 26 mod 100%. Okay, if I want to see all of the matches all of the comparisons that are done I can do dash a and it will show me 0% matches. So in this case 17 matches 10 0%. Well, we expect that the only thing similar in between 10 and 17. If we look at it. So 17 has a bunch of text has this header, the header is to a specific person. If we look at, yeah, even the header is quite different. So we have some things that are kind of similar in the header, but then again, the two is very different. So we have to just one person we have CC, we have a subject here we have to we do have a subject, but it's also different. So the similarity between these would be very, very, very low. And in this case, it's counting as zero just because there's so much text that's different above, basically. So these two files are considered not similar at all. Because even the message ID, even if this tag is basically the same, the ID or the value is going to be different as well. Okay. Yeah. So these two are considered completely different files. So they match at a zero. Where was it? I think. Yeah, here. Okay, so 10 matches 10 100%. 10 matches 17 0. 10 matches 26 0. And that's what we expect. 10 shouldn't match anything else because it's an email and it's quite different than any other email. Let's see 17. Basically the same thing 26. Basically the same thing except for 26 mod where it matches a lot. Now fuzzy hashing works really, really well against text files, or non compressed file types basically but essentially text files if you're trying to look for similarities for example if you have a part of an email. Let's go back. If I was looking for for example some of this text inside the email. Well I could just do searches through the email but if I was looking for for example entire paragraphs or something like that. You could figure out which ones might contain that paragraph depending on how you are searching but this basically just says you know if there is a modification between two files, we can now identify which files are probably going to be modified. Okay, now just to give you another example. Let's go out of text so it works really well for text. Let's go out it doesn't work so well for images. So sometimes it works sometimes it doesn't and the images folder I've set up a couple examples here. I have this cat and this is the original photo it's a JPEG image. It's called Kitty JPEG 1 and then I also have Kitty JPEG 2 which has been modified slightly so a bit of a swirly face here. Kitty JPEG 2 has been edited using an image editing program saved as Kitty JPEG 2 and then I also have Kitty PNG 1 and it's basically an exact copy of Kitty JPEG 1 just saves a PNG with compression. Kitty PNG 2 same thing except with the swirl saved with compression PNG 3 I added a black box to the image a small black box saved as PNG with compression. Kitty PNG 4 just added an extra eyeball to the image also saved as a JPEG with compression. Kitty PNG 5 basically no edits saved without compression and Kitty PNG 6 the swirl again saved without compression. So a couple different images here and we're trying to look to see can we detect different changes in the images now remember SS Deep is not doing image it's not doing computer vision right we are not extracting features from the image and analyzing those features or the similarity between features in the picture we are analyzing the images at a binary level. Okay so what are some I guess hypotheses we can already make about the images at the binary level well I'm guessing that these JPEGs are not going to match. Why well because the compression in the JPEG format basically those two images at the binary level we will be completely different PNGs I think will be a little bit similar and PNGs with no compression I think will be very similar. Okay so yeah it really has to do with the file formats JPEGs I don't think we'll be able to get a similarity match at the binary level PNGs I think we will. Okay so that's my hypothesis here so just like before with the text remember we're doing it this yeah okay so binary level we're not doing computer vision here that's a different a different thing. So we can do SS Deep star and then I'm going to suppress the text dash s. Okay so here's all of the hashes we have for each of the file so what we need to do now is create the hashes for all of the values I'm going to suppress the errors. And save everything to fuzzy dot ssd so we're going to take all the hashes save them to fuzzy ssd and then if we cat fuzzy then we can see all of the hash values. Now we want to compare with all of the files that are currently in here and see if any of these images are actually similar so we can do ssd and then feed it the file dash m fuzzy ssd. And then I want to compare with all of the all of the files in the directory and I want to suppress all of the errors okay so hit enter. Now we can see a few things right so just like with the text files we have JPEG one matches JPEG one okay we expected that but we don't see JPEG one anywhere else so JPEG one is it looks like it's not matching anything else okay and this was the original file remember this is the file that everything else was built off of. JPEG two basically the same thing it matches JPEG two but we don't see it anywhere else okay so yeah it looks like my JPEG hypothesis is working out the JPEGs don't match each other because they were modified and most likely well because of the compression in JPEG it looks like that the files don't match the binary level because they basically get recompressed. Right so PNG one is basically a direct copy of JPEG one and it matches PNG one now PNG one also matches PNG two forty one percent so let's look at that real quick so we have PNG one. Which is the cat with basically no modifications PNG two the cat with a blurry face okay so the only thing we did is on this layer blurred out and then saved right so they both have the same compression level. The only thing that basically changed was the blurry space right and apparently this space is enough with the compression that's used to be able to match up to forty one percent okay so yeah forty one percent here so matching forty one percent with some modifications okay. And that's because of the way that yeah basically the file format and the way that PNG compression works or the PNG compression and GIMP works okay PNG one matches PNG four fifty five percent okay so let's take a look at that. So PNG one matches PNG four fifty five percent now why is that okay well basically the same reason instead of having it blurry in all of this space we've only modified actually a very small portion of the data so this is a relatively small portion the other one. Let me open up to again we can see that the area that was modified is basically let's say this big if you can see that it's this big where the area in PNG four is much much smaller so even though it's smaller is still only matches fifty five percent again because of the type of compression that's done we're still losing some information there. Okay so those are the matches for PNG four and PNG one. Now you might notice something on here PNG three okay we matched PNG two and we matched PNG four so forty one percent fifty five percent because of the amount of data that was actually changed in the original image. But what we don't see on here is PNG three now let's look at PNG three. Okay so this is basically just added this box so why doesn't this match well it doesn't match because whenever I added this box in GIMP I actually added a new layer to the image. Okay so whenever I added a new layer to the image it essentially changed all of the data. Okay so by adding a new layer over it I've changed all of the data because I've added basically a new layer on top of the entire image. That's changing all of the data underneath whenever it finally gets rendered basically so PNG three doesn't match even though it's just there because of the way that I added the layer. On these other ones I just modified the layer directly I did not add another layer on top okay so that's an important I guess important to think about the way the way the editing makes a huge difference in whether you're going to get any type of match or not. Really if you're trying to do if you're trying to compare two images you should be using feature extraction and not binary matching but I'm just showing you basically that it is possible but really some things you have to watch out for. Let's see PNG two PNG two matches okay PNG three matches 100% PNG four and PNG one just like we saw before 55% PNG four and PNG two 40% just like we saw before. And then PNG five with no compression matches PNG five with no compression PNG six with no compression matches PNG six with no compression. Yeah and they don't they don't match each other either that's basically just changing the compression levels around now if we go back and do dash a we can see all of the ones that didn't match as well. PNG five no compression yeah so everything else basically matches zero so it's automatically filtered out now we can also use SSD to filter out only like matches that are above let's say 50% or 60% but the problem here is that PNG two and PNG four actually matched they are they are similar because they're derived from the same image but they only match 40% and there's a lot of different reasons for that low matching but this is actually a match so choosing be very my point is be very careful about how you choose to filter if you filter based on for example I'm going to take everything that's 50 50% or above match well that might not be very good depending on what types of files you're looking at working with some data types especially text you usually want a higher match than before for example like 80% 90% something like that. But really you have to understand your data set and what you're trying to match before you can figure out where to set your threshold so just be aware of that don't just arbitrarily pick you know 60 and above because you would have missed these two. But if you pick anything you know lower than 50 you might get a lot of false positives so just be aware that you really need to understand the data that you're trying to match here. Okay, so again like I said whenever we're matching images you should be using feature extraction not not necessarily binary extraction but just know the data types. If you are looking for data types that you know can match it a binary level okay then no problem but for images or video you really should be doing feature extraction and similarity matching over the features. Right so let's see. The only other thing I have this show is if we go back to the text. If we go back to the text now we already know that 26 and 26 mod are related we found that out by using ssd but I want to know exactly what was different or what was different in the in the files. So I can use diff diff in Linux and then just say 26 and 26 mod and this will tell me exactly the lines that were different. So here it said inappropriate the original 26 was the original 26 mod is the modified so the original goes first then inappropriate materials such as this are opened. Okay well in the new one we have inappropriate cookies such as this are opened right so using diff is a good way to see exactly what line was changed once you find out that two files are similar. Now we could have done the same thing. Yeah okay anyway that's just to show you how to pull out or how to find what was changed in two different files. Okay so that's it today for fuzzy hashing and ssd. Thank you very much. If you like this video please subscribe for more.


To je tady s drojí určitě. Ne, tady už je vegráčka. Tak to neskáne fungu. Máme vegráčka nastavených drojí. A, tady to nevidíš. Nechci zaplnout ten pučíteč velký? No, mám cuná... No, mám cuná... Pustě nysna z toto. No, s kde máš? FN, tohle? No. Vy jsi na to nezobrazí na pučtiči. Tak nemá výstuk. Musí tam i duplikať si. Vyskají, vyskají ven. No, můžeme vytloubec. Můžeme použiat takhle velký pučíteč, že bychom si na něj dál. Můžete, jo. Tak to není to velký. Jako věrejtně to jde. Musí z nich vělouzit výstuk. Tak já to zkusím. To je... To je... To je... mohl dnes. Něč. Zoznedané plečka. To ní to teda online, když to možná na Google. Možná se. To je velký. To bylo to něčešté funguje dneska. Zvídové. Já jsem pěná smací na... Pěknutky. Jel to platí pro všechny močiný ena loga na Sahra. Já si vidím, že kvádky je. Cha. No to chlebe kutro. skeleton. Heorial impokorni that has lecture about TLS and DTLS in Linux kernel. Welcome to my presentation about AFK TLS. We will discuss basically other family kernel transport layer security and we will discuss basically a Linux kernel that implements part of TLS and DTLS protocols right inside the kernel. So before we start let's discuss about TLS and DTLS in an actual. TLS stands for Transport Layer Security. The DTLS means Datagram Transport Layer Security. You are probably familiar with the term SSL that is historical naming. Now the proper way to call this protocol is TLS. We have currently version 1.2. There's a draft that describes version 1.3 And it introduces a lot of cool stuff such as zero round trip times, so you're basically sending data with the handshake. You'll probably know libraries such as DTLS and OpenSSL. These are open source implementations of these protocols. You are probably familiar with other libraries such as Libre SSL, but most of them were basically forked from OpenSSL to some vulnerabilities. If we look at TLS and DTLS protocols, basically we can see that we have a control layer and the record layer. And basically the control layer is used to control TLS and DTLS protocol. So you can see messages like hey, I want to establish connections, so I want to establish keys, or hey, I want to do rekeying when there's some problem with the session and we might be compromised. And then there's record layer. Basically the record layer is used for transferring your data. So if you transfer your data, it's in the record layer. The main difference between TLS and DTLS is that TLS requires the underlying protocol to be reliable. That means that you have to use protocols such as TCP. On the other hand, DTLS protocol does not have such a requirement, so you can use it on top of UDP. On the implementation level, that means also that if you are implementing DTLS, you have to deal with things such additional information so you don't receive some records twice and you are basically dealing with reply attacks. If you are using VET, you probably are familiar with TLS. Basically, HTTP protocol is used quite widely with TLS, so called HTTPS, the S stands for Secured. TLS is also used for e-mail and probably note that known projects are high availability proxy. That is basically a load balancer on top of TLS. And also we have SSL-based VPNs. SSL here is a term that is quite tightly bound to this expression, but it can be also TLS-based VPN. Such VPNs are open connect that is open source implementation. It's open source implementation, but there is also any connect that is implementation by Cisco. Basically, open connect is alternative to any connect and it's also compatible with any connect. If we look at very basic scenario, we have a client that communicates with some device that is located on local area network and we are communicating through pounds so we want to take care that our communication is encrypted and no one sees what's going on and what we are basically sending. What client needs to do to basically send data, there needs to be established connection, but once we are done with the connection establishment, we basically do encryption and we add some additional information such as header to our message, so it's TLS specific. Then when we are done with encryption and the encapsulation, we basically write the record to the kernel and kernel does all the things for us. It basically sends the record on the wire or any other media you use for transferring your records. Once your record is on the server, server listens on some particular port and once your record is ready, you basically open connect server basically issues read or receive operation and it decrypts our records so basically there's reverse operation to the client. Once we decrypt, we check integrity and once we are done with it, we have raw data that was transferred and once we have raw data, open connect server basically issues write and our record is routed through the kernel to desired device on local area network. If we look at this scenario, we can see that on the server we are basically reading from the kernel and writing and we are reading just to do decryption in user space. If we try to identify this thing and basically move the decryption to the kernel, we basically do optimization and if we take a look at it, we basically saved two context switches so we are no longer required to do read write and to user space and what we achieved, we also achieved that data that were transferred are no longer copied from kernel space to user space for the decryption and from user space to kernel space for sending and basically this was the idea behind AFK TLS so address family kernel transport layer security it's a module, it's a Linux kernel module that sits right inside your kernel and it basically introduces a new socket type AFK TLS it implements part of TLS and DTLS protocols there's only implemented the record layer so if you want to use AFK TLS you have to do handshake in the user space with your libraries such as OpenSSL or GNU TLS and once you establish the connection you basically ask these libraries for key material and you instantiate AFK TLS and then you pass all this key material to the kernel and you can do read write operation on a socket just like raw socket and you are basically accessing raw data from the socket currently there's implemented only ASJCN cipher but AFK TLS can be extended with other cifers as well very nice candidate is Chechapoli that is used on mobile devices AFK TLS supports all socket operations so you can do socket to instantiate AFK TLS you can do bind send receive or write and read you can also do some advanced stuff like send file or supplies and the only socket operation that is not supported is Konect basically when I was designing API it didn't make much sense to implement Konect there was no semantics for it so if you look at the optimizations we wanted to save context switches basically this is not possible now with Linux kernel because there is no such Cisco that would do something like hey, this is my socket and read everything from this socket and write to another socket there are Cisco such as send file and supplies basically send file is used for sending files and it's implemented on top of supply syscall and supplies system call is designed to basically do things like ok, I have a socket or file descriptor and I need a structure that basically points to the kernel space and points to memory that is allocated in user space so I'm no longer required to copy from user space to kernel space such structure is called pipe and you can use FKTLS mostly with send file and supplies so you reduce copies and this optimization was quite successful we are no longer required to copy data from kernel space to user space only for decryption or encryption when we were implementing FKTLS there were issues such as issue with padding when you do optimization you have to take care about a lot of things such thing is padding if we look at TLS record you can see that there are basically 5 bytes and everything that follows is shifted so we are not aligned and we are accessing memory that is not aligned the same applies for DTLS record as you can see there is a second number that is carried to construct a sliding window here are some results and basically as you can see the send file using FKTLS is the fastest way how to transfer data over TLS or DTLS it is faster by 5-10% like 7% in average here is user send that means it is sending data through TLS or OpenSSL send so you are doing basically encryption and decryption in user space and the last bar represents when you map the whole file into memory and basically you do send using user space library so you do encryption and decryption in user space so AFKTLS has various UCG basically it was designed for OpenConnect and high availability proxy but as it showed it is not possible to use it so there is lack of tune device support for supply system call but AFKTLS could be used for example with KCM that is a kernel multiplexer where you have a socket and you can do multiplexing with various threads in the user space you can also AFKTLS you can also access raw data right in the kernel this gives you power to use Berkeley socket filtering or Linux socket filtering you can also use BCC that is a nice tool where you basically say hey every time there is called this function in the kernel I want to run my code so BCC allows you and probably the most useful thing for AFKTLS is CPU offloading where you basically have a specialized hardware which does encryption and encryption for you and this allows you to offload CPU so you can do something else on CPU basically two companies contacted me and both of them were using AFKTLS for CPU offloading so the question was if Linux Crypto helps in this way so with AFKTLS we are using Linux Crypto it was needed to batch it because there were some issues with implementation of AFKTLS it was needed to batch it because there were some issues with implementation of ASJCM basically Facebook batched it and TLS in the kernel is not novel there were implementation in Solaris Solaris implemented all TLS protocol into the kernel and it active like a proxy so you had one port to another and it was basically forwarded Netflix experimented with something similar to AFKTLS they adopted BSD kernel this implementation is not open source they published a few articles and they were basically adapting adapting OpenSSL we experimented with AFKTLS RedHead and also cooperated with Facebook and a few patches you can find AFKTLS on the feedhub and that's it, so any questions? yeah? do you have a merged merged for upstream? not, so the question was if we merge this to the upstream not yet, there are still open issues that we need to deal with so basically it will be merged someday, hopefully do you know where the performance improved and comes from? so if I understood it correctly what was basically the key in the optimization so the key in the optimization was that we reuse number of copies so we are no longer required to copy from kernel space to user space so is it going to be used in HAProxy? HAProxy uses tune device to communicate and tune device doesn't have support for splice so it's not possible to basically use it with HAProxy right now once splice support will be in the device how do you use a synchronous TLS which is happening on the wire? how do you handle a synchronous TLS that happens on the wire in the downstream? so the question was how do we do synchronous TLS on the wire? so even if you have your TLS connection already set up how do you send that to user space? so basically when you use FKTLS any error, we basically check only if it is data and if it is not data report we immediately return error to user space and we notify hey this is not data and all the key material has to be best again to user space libraries and basically user space libraries so you can't use libraries anymore for dealing with state TLS you can't use a normal socket like we used to do Linux that you pipe or give the pipes basically you have two file descriptors one is the file descriptor that is used for entry to communication and then there is another one where you access the grid data so you are basically playing when you want to use the encrypted socket you have to pass all the key material from general space to user space because there is also a data how do you deal with asynchronous errors from the TCP layer and while keeping and checking the politics rule so what basically do you file descriptors the lowest file descriptor number so the question was like how do we deal with errors on TCP as a data basically any error that is on during communication we notify the user space and use the user space to deal with it but somehow you use those file descriptors somehow you have to close the file descriptors or do you keep them tightly the only thing you would have to synchronize is the state any other question does this work with NSF? sorry does it work with the NSF except because it can't share the key material basically if it allows us


Today we're out here starting the phase one of the recovery process, removing the household hazardous waste. We're out here looking out for propane tanks, the ones like barbecue size, paints, household cleaners, batteries from cars, fuel tanks, ammunition that we need to remove. We're also, we have a certified asbestos consultant with us, so we're looking for any asbestos containing materials that are out here and removing them. And then we're also doing personal monitoring to ensure our crews aren't exposed to any particulates. Items that we have assessed that are empty and can stay on site, we have marked with white paint such as the propane cylinders, we devalve them when they're empty and mark them with white paint, a white X, this is a big job and we're just starting.


I'm scared I'm genuinely scared for what we're walking into What is good mama squad and welcome back to our channel guys today. We are doing something very spontaneous We got a lady video coming off. Actually, I don't know if it's lit for us But it's definitely gonna be lit for you because we are gonna be finding out how the worst rated hotel in our area Actually is And we're gonna see if what people are actually saying is true, but then we're actually gonna go there We have already a place. We booked it. We got a room a couple days ago We've been waiting for this day all week. Hopefully it's a lot better than what people are saying Yeah, we're gonna do some reviews before we go there really quick Make sure to subscribe to our channel guys if you have not already help us get to 1.2 million Subscribers and make sure to give this video a thumbs up as well Give us a thumbs up for our sake because we're going into the lion's den We have never been to a one-star hotel before literally I'm not even kidding one star hotel and we're gonna see if it's actually one star or if people just rated it wrong If they just had some bad reviews, you know what I mean? You never know Hope guys low key the room look pretty nice That's why I was confused why I was a one-star Yeah, so we're gonna actually see when we get there if it's gonna be bad or worse cuz like all people are saying some stuff And I was looking I'm like these actually looks kind of nice We saw the reviews and we did not like the place after the review So let me just get started with the first review. Here go Bless you. Bless you in that case Always when I'm filming. I know she's allergic to me. No, I'm allergic to cats I think you're allergic to me, but I'm allergic to cats, but not where I'm like stuffed up I just sneeze a lot, but we own a cat. Yeah, and we have taco here for the day I'm also really stuffy. Yeah, that's your fault So going into the reviews the first review that I read like right when I went on the page And it said I was promised free Wi-Fi and breakfast which I never received. We need that Wi-Fi That's like the first main thing. Do they not know what's coming in We got some social media stars coming in to their whole town Back up back up. I'm gonna make money. Okay. Okay.  Literally work for word I just read that just a disclaimer. We do not want to show the hotel We do not want you to think that we're talking down on a certain place So we're not gonna say the name We're not gonna show the outside of the building because we really don't know what we're walking into these are just The reviews that we're reading and I don't want to like publicly like spread hate on their place because it's not our voice To bring them down. We're just gonna review it. Yes now this comment freaks me out a little bit This was four years ago. So hopefully four years have changed some other stuff I just read from like a month ago like three weeks ago this one I asked for a non smoking room the room just smelled really really bad The floor was dirty stains on the sheets bed bugs. Oh, and I got seen several times We realize they were bed bugs on the pictures on the website.  I will leave. I will not stay at first bed bugs. I'm right out of everyone I can't believe you've been thinking of walking in this building We're bringing our masks a whole box of masks just for this video And I'm probably gonna use every single one of them because this is not okay I ain't sleeping on no bed with bed bugs now if it's clean We should buy a sleeping bag low-key like make our own little bed in the corner of the room if it's that bad What what do you see? Oh the next comment this from six months ago, okay all caps do not go here Stay away with six exclamation marks and it also says the hall smelled of a mildew Mildew Mildew. Oh But someone did comment great rooms reasonable price.   You guess. Oh, we should get some get some Pellet I'm scared. I'm generally scared for what we're walking into wish us luck. I hope you enjoyed this video This is gonna be super late. I'm very curious. Let's go guys. All right guys, so we just made it to target This is our final stop before we make it to the one-star hotel guys. We came here. What do we come here? Just specifically for though. We're gonna get some disinfectant wipes and like for breeze some for breeze We want to come prepared and we want to make sure we have hand sanitizer like masks disinfecting Mics and snacks in case we stay the night because we still might stay the night if it's nice enough Right, so that's our that's our goal for tonight. This is the first thing that we're getting guys disinfecting mice We got to be with these next. I think we should get gloves and some other stuff. We need everything So we actually also got hand sanitizer We got some gloves some blue gloves and we got a flashlight We are prepared and we are outside the place right now Mariah. How does it look on a scale of one to ten? Kind of scary kind of scary, but I'm giving it a three or four for looks right now It's I see there's there's water down the walls It's not it's not really kept up to date, but that doesn't matter the inside is what matters So let's go in and let's see what happens All right, so we just made it and as you can see right off the bat guys the lighting is just absolutely terrible Like a toilet It kind of smells like a toilet in here We could probably take off our masks because we're out of room But we've been searching for our room for about like five minutes and we finally found it It was literally on the second story and I honestly liked the first or better, but we're gonna go inside right now This is what the door looks like. It's actually decent. It's pretty clean I'm really curious about how this is we have our gloves on let's go Wait, I'm actually put on the mask because I don't know what's in there. Are you ready? This is it. I'm scared. It's unlocked First looks guys the one and a half star the rated place. What do you got going on? You know what the lights which is our good thing I bought this I know all right, so all right Oh my gosh, so the lights which didn't work at first and I just literally inserted this key And it said insert key for power and now the power works I was I was so confused why it didn't work. Oh, let me pop this on. It's actually it's looking good so far It's actually pretty pretty quick. What what do you mean? What? Wait hold that way guys the okay hold up. Is there someone in here? Like should we check the closets and all because it looks like someone just came out of this bed right now Tell me it doesn't doesn't look like someone was sleeping there What oh wait wait a minute? Wait this place is really nice actually why is all this stuff tipped over hold on I promise you guys This is the first time we are in this room. What?  There's a connecting room. Oh, yeah now that's staying locked Hopefully no one has the key that's It seems decent for now I want to turn on more lights and then we're gonna go for because this is just like what we saw before We have to go into depth and actually inspect the room because I'm not it's not too promising right now I did not expect all of this Maybe maybe when they made it up. It just they forgot to do I don't even know it looks I bet you they didn't even clean this since the last time someone was here, babe Because look at this. All right. We got some more yellow lighting here. We're gonna start with the bed Mariah Be very careful. Yeah, be very careful. I want to check this bed up and down. What's going on with this thing? Why? I don't do rats. Hey was there a bird? Okay, here pick up peel this back. I need all this back. I want to make sure it literally looks like someone's body imprint was here, babe No, look at right right here is where their butt was and then their hat. Oh my gosh. Someone was definitely sleeping here. What? I don't I don't know like Well, no you have to look underneath the bed bugs come underneath like they don't they don't just sit right on top They go deep because they like like the warmth You don't want to do it here. You can hold the flashlight. Okay. Hold up guys check this like clearly. What's that? Wait? What's that?  That's just a fuzz. Are you sure? That's a fuzz. Oh my god I'm like tweaking now Marvel squad.  What's that? What is another feather? I don't know Why are there so many feathers in here was it animal? All right, hold on There's We struck gold Hold up. Hold up. Hold up guys. We literally found hair and something else. Hold on. What is that? Ew. Ew. Ew. Is that a bag bug?  I don't know if I'm gonna sleep here tonight. I don't know. I don't know I guess it's just under these trees. Okay. That is nasty. I'll check the other side of the bed. Come on. It's really quick Ew, I'm scared. What's that? What is that? Is that a bug?  That's another feather Literally. Oh, ew. Ew. Is that like a booger? Is that? There's hair all over this.  Well, at least it that's the only thing we see. At least it's not on top So honestly, that's that's better than anything. We'll keep you guys posted if we find anything else. So far everything looks decent For being a one-star, they probably up their s*** theorem was talking about their stuff. They're like, oh my god They must have just upgraded or something because this is way better than I thought it would be. Me too. Now the main point that I saw the first comment was that they did not have Wi-Fi last time or it wasn't free You guys know we need Wi-Fi. We need Wi-Fi and we're gonna put this to our theory right now Our job works. We don't have Wi-Fi. We can't upload free time. You're daring. You know that? I still have gloves on. This is still a low rated place. How are you still comfortable right now? I haven't seen much yet besides you know hair on the bed. Oh, but besides someone else's DNA on the bed That's it. That's like under under. We have not even dug deep in this place yet, babe. Things have been okay so far She says it's okay. I think they definitely upgraded.  Did you even sanitize your bed? Wait, so you're sleeping here. Are you not? 100% well, I didn't know we discussed that yet. Are we? Are we? I thought we made it clear it's not as bad as we thought it was gonna be. Alright, alright I think we should try to make it to the morning.  Oh my gosh guys If we actually stay in this place, they had to do something like maybe Maybe yeah, no, maybe they put us in like a like their best room here or something because all their other rooms are like Alright, let's test out this Wi-Fi though I'm actually really curious to see if they come through with it because that was one of the top comments and that it wasn't free Like they said it was alright, so I'm trying to connect to the Wi-Fi right now Wait hold up Okay, okay, so third straight up if you become a member of this hotel Which I don't know why anyone would want to become a member about this hotel Then you get free Wi-Fi, but you cannot have free Wi-Fi No, it's ten dollars nine ninety five an hour nine ninety five an hour So if you stay here for 24 hours like one full day, that's literally two hundred and fifty dollars I have never heard that in my entire life two hundred and forty dollars. Sorry. That's two hundred and forty dollars a day then We're gonna have to dip in early in the morning because we gotta get our video uploaded for tomorrow I'm literally about to pay for Wi-Fi Upload our video as soon as we can and then right when it's done uploading I'm gonna cancel my subscription to the Wi-Fi because And watch if you become a member it's probably still like cost money to become a member So you don't even get free Wi-Fi like you probably still have to pay your way in oh my god Like what that okay now? I know why there's a bad review like I said guys We are not gonna go on the website and say bad reviews say that there was hair in here We we don't we want to be biased and we want people to make their own judgments But from what we've seen right now this place is like a six out of ten The looks are good the looks the outside looks are not good the inside looks are really good But like just the little things that they have here. I don't even know if the TV works I don't even think it's plugged in guys like it's like just a first show. So I don't know We're gonna let you guys give you a little update Wait, we're just luck if we find anything though. Honestly, we'll let you know right away So a couple hours has passed. It's getting almost to midnight now. It's actually 11 30 We've been chilling out in the bad guys. It's been a stretch But we keep we checked every square inch of this place and I cannot find anything wrong with him, right? I am so confused why this was rated so bad Like guys we literally like there has to be something wrong with this place like they said that their Wi-Fi Well, okay, the Wi-Fi was kind of that was it that we don't eat We still don't have Wi-Fi we're on like cellular and it's not that good But anyways, I've been looking everywhere everywhere the sheets the air like what what would be bad in here that? They would review I mean those were like four years ago. So a lot changed no I saw some three weeks ago six months ago, and then two years four years. Yeah, probably some just really awful people People don't really reviews. Yeah, that's true So the people that do leave reviews are kind of like the people that just like maybe they were just fed up with the staff or Something because they definitely walked out of this place and they misguided us. I'm telling you right now Like come on the whole bathroom is fine the showers find the water is not in the ground Okay, it's like it's crystal clear. I really I really am so confused right now. I'll wait. Where's the roaches? Where are the bugs? That's what we came here for me. I know I know we really came here This is honestly best-case scenario We came here hoping for a place to stay that was hopefully nicer than the reviews were because I feel like if there was bad We'd be gone already, babe. We'd be out of here I really want to go home.  That's what I would say if there was a roach, but there's no Right they definitely had to have done a pretty good job cleaning up all the bugs before we got here I am so confused like like this would be like for me kind of like a three and a half star place Probably like three and a half star place, and they they marked it as one and a half crazy So guys we're gonna keep watching your show if anything happens We'll update you guys I guess We're gonna put our own review at the end of this video And I also want you guys to comment down below your guys's review I really wish we could have showed you the building and I really wish we could have showed you like the lobby and everything But we didn't want to throw a bad name on this company, but honestly this company I don't think would have that bad of a name Good morning guys. Oh my gosh. Hold up right sleeping right now.  Oh my god. That's way better than I thought we did it. We did it babe Wake up Wake up Ryan guess what we did it Wake up. We need to talk really quick Marvel Squad we actually stayed in this hotel Overnight like we did it it's morning right now as you can tell we had a decent night's sleep I woke up a couple times because I heard some weird noises But I don't know if that was just like the AC or something that was going on I really don't want to think too much into it. I cannot believe we actually did that I did not think we were gonna stay here and we did it right. How do you feel about that? She's yeah, I know we don't have a king bed look at how big this bed is it's a king bed, too And then get our house. I have a big old bed. Oh stay tuned because that's what we need We have a we have a full full right now, right? Yeah, that's that's still too small even though It's kind of big. I know I just want to tell you guys right now. This was so unplanned I really thought this place was gonna be like crappy I really thought we were gonna walk in to a bird's nest and have to look at Nasty walls and all this stuff But honestly it turned out good and I always want to tell you guys if we ever do do a video like this again Or we do like a five-star hotel We're gonna find an actual dirty place or a luxury place no matter what like even if we walk into it And it's not luxury and we were gonna go to a five-star We're gonna leave and go to a different one because I feel like they did we got jipped it out for this one Which was kind of weird, but I feel like that was probably like the maze fall Yeah, so I don't I don't know that's still weird like we when we got in the room as you saw earlier The bed was on me, but they might have just like messed up and threw it together, but thank you guys so much for watching this video I hope you enjoyed it make sure to give it a thumbs up and subscribe down below Mariah would be talking, but it's really early and we actually have to check out soon So you gotta wake your little butt up babe cuz we gotta get out of here soon also subscribe to our marble vlogs channel We just posted a video on there the couples telepathy challenge, and you guys are loving it already So make sure to go check that out help us get that channel to 100,000 subscribers, and I think that's all Stay tuned today. We're filming something with my twin sister for our main channel You guys are gonna love this one and that video is gonna go up Friday Today's Thursday today's Thursday. Yeah, so tomorrow get ready for tomorrow's video. We love you guys Thank you for staying tuned, and we will see you in the next video, but until then peace love you guys