Aqw Packet Spammer

[Luz Tonks]

ThePacket Flooder tool is a UDP Network Traffic Generator. It sends UDP packets to a target IPv4 or IPv6 address. You have control over the target port and payload in the UDP packets. Under the right circumstances it can send UDP packets at a rate fast enough to achieve 98% or so bandwidth usage* on a 100BaseT ethernet interface. Version 11.90 introduced a method for controlling the desired outgoing interface bandwidth.

WS needs to be plugged into a port off of the switch that is designated as a monitor port. That port, depending on how it is configured, will be mirroring all of the traffic that is moving through the switch.

I know that this was already answered, but I wanted to add that I use a software called BotHunter for this purpose. I have it installed on our web filter PC which all traffic goes thru. This software uses WinPcap (which is the library Wireshark uses to capture packets) and automatically logs packets that look like spam and display them in a very readable format. It will even show the IP address from a particular machine with a good bit of detailed info on the packets being sent.

would drop all packets from any ip sending more than 100 syn packets in less than 5 seconds to any HOME_NET IP. The spamming IP would be unblocked after 30 seconds.

The downside to this approach is that the rule will flood your alert logs due to triggering on all SYN packets when not dropping.

For testing I wrote a Python script that sends UDP multicast packets over my WiFi network. And then on another multicast group I have two ESP8266 sending replies. One sends at 60Hz to simulate the real set-up, and the other spams at 10 times that rate to simulate congestion from the other devices.

First I only looked at packet loss, which was kind of as expected. But then I started to look at round-trip time. I made my Python script write the current time, and made the "real" node echo them back. (spammer messages are ignored)

I figured the packets probably get stuck in a queue somewhere, so some googling suggested you can look at the queue length with the following command. Sure, the queue is not empty, but if I turn the spammer node off, nothing much changes in the queue length, while round-trip drops back to 10ms.

I thought maybe my Python code is just too slow to handle 600 messages per second. Running on PyPy made absolutely no difference, so I'm a bit hesitant to write the whole thing in C. It has almost 2ms per packet to do almost nothing.

Voice over IP (VoIP) is a key enabling technology for the migration of circuit-switched PSTN architectures to packet-based networks. The problem of spam in VoIP networks has to be solved in real time compared to e-mail systems. Many of the techniques devised for e-mail spam detection rely upon content analysis and in the case of VoIP it is too late to analyze the media after picking up the receiver. So we need to stop the spam calls before the telephone rings. From our observation, when it comes to receiving or rejecting a voice call people use social meaning of trust and reputation of the calling party. In this paper, we describe a multi-stage spam filter based on trust, and reputation for detecting the spam. In particular we used closed loop feedback between different stages in deciding if the incoming call is a spam or not. For verifying the concepts, we used a laboratory setup of several thousand soft-phones and a commercial grade proxy server. We verified our filtering mechanisms by simulating the spam calls and measured the accuracy of the filter. Results show that multistage feedback loop fares better than any single stage. Also, the larger the network size, the harder to detect a spam call. Further work includes understanding the behavior of different controlling parameters in trust and reputation calculations and deriving meaningful relationships between them.

Despite many radiotap parameters being currently defined, most only make senseto appear on received packets. The following information is parsed from theradiotap headers and used to control injection:

After composing the packet contents, it is sent by send()-ing it to a logicalmac80211 interface that is in Monitor mode. Libpcap can also be used,(which is easier than doing the work to bind the socket to the rightinterface), along the following lines::

Do a packet capture, or setup some kind of logging filter on your perimeter equipment (firewall or border router). Watch for outgoing traffic destined for port 25. Once you find the IP of the traffic work backwards to find the machine. This may mean looking at the CAM tables on your switches to find out which port is associated with the MAC address the IP belongs to.

When a comment is sent to your weblog, the IP address is included in the packet of information that travels with that comment across the internet. Think of it as a phone number, and the WordPress comment moderation acts like call display to show you where the comment is coming from.

It should be noted that spammers are notorious for hijacking IP addresses, so it is possible that the IP address attached to a spam item is, in fact, "stolen" from a legitimate internet-connected device.

And other sequential or similar number orders. You have the ability to add a simpler IP address to your comment spam word list by dropping one or more of the IP numbers, thusly: 192.168 -- in this way, any IP address that starts with 192.168 will be screened as spam regardless of the numbers that appear with this "wildcard". It saves you having to type in lots of individual numbers. Be careful with how generic you make your wildcard IP numbers though, because just using 192. would probably eliminate legitimate IP addresses to comment.

The .htaccess file - which also controls your permalinks - can be used to completely block an IP from even seeing your site. You can place this either in your site root, or the directory where your blog is (if they are different).

If you do start blocking IPs, then a blocked visitor will see a 403 error page. Try to make sure that such a page has your contact details listed. Check your hosting to see how to make a custom 403 (or see below too).

When a spam-bot comes in, it hits the file directly and usually does not leave a referrer. This allows for some nifty detection and action direct from the server. If you are not familiar with Apache directives, then write the following in your root directory .htaccess file::

Note: As easy as it is to block IP addresses, it is ineffective against spambots which use compromised machines to spread their spam for two reasons:1. The original owners of the machine are still accessing the machine and are most likely decent people who don't deserve to be blocked2. The sheer number of such machines will overwhelm anyone trying to block by IP

Many bloggers show referrer's to their site or links from which people came to visit their site. Spammers exploit this and indiscriminately spam blogs (even bloggers who do not have this feature enabled) with referral links pointing to their spammy sites. They end up wasting your resources, polluting your legitimate referrer's list and slowing down access for your readers.

In an effort to economize their resources, spammers often send out comment spam bots with their spam referrers for that two-in-one-shot effect. Consequently, you can block quite a few comment spam bots by blocking the referrer spam.

The aforementioned .htaccess rules were brought to you by Tom Raftery, who originally used regular rewrite conditions and later decided that "using SetEnvIfNoCase instead of RewriteCond - seems to be quite effective (especially for referrers)."

Hello, there is someone spamming our email server and I blocked the IP in two ways in which I'll attach pictures of. One is a basic firewall rule to drop traffic from a list of spammer/hacker IPs. The other is a DNAT rule which takes that same list and is supposed to route the traffic to a random IP that has nothing to do with our network. When I look at the logs, the firewall rule appears to work but the DNAT rule is apparently ALLOWING the traffic to go through and I cannot for the life of me figure out why. I have these rules as high as they can go (firewall starts at 16 because of automatic rules before it). Nat rule #1 is the "black hole" rule and Nat rule #5 is any > smtp > our mail server > destination: our spam firewall. The "going to" IP is our WAN IP. Something else I'm confused on too is that the blocked message from packet filter #16 seems to be the Nat rule because that's the rule that routes traffic to 240.0.0.0; the firewall rule is just set to drop obviously, but the logs seem to show the block coming from the firewall rule and not the NAT? I'm confused. Any ideas? Sorry I am by no means an expert on this device.

this didn't stop the attacker as it kept hammering our spam firewall. it wasn't doing a lot of damage, just slowing down mail queues a bit but in the future I would want to just put in an IP and stop them from accessing our network entirely

I guess I'm confused because packet filter rule #16 isn't a DNAT it's just supposed to drop the packets but the log shows it routing to 240.0.0.0 which is the DNAT. The NAT rule seemingly is allowing the connection to go to the our WAN IP. During the logs shown, the attacker was still hammering our spam firewall so it wasn't blocking him fully and I'm trying to find out why

In the Wireshark capture I conducted back in January, I observed that the notifications followed a standardized format, with only a few bytes distinguishing them for each type of notification. To conduct these captures, I have my own Faraday Box, which provides a controlled environment for capturing various types of notifications.

From the image, several key points become evident. Our goal is to replicate this data to be sent by the Flipper or any other device. The data adheres to a standardized format, as previously mentioned.

From this point, you can replay it on any device, triggering a notification.However, for consistency, we can perform some data cleanup.Like all Apple BLE Notifications, AirTag data follows a structured format, and by starting from the end and zeroing out two bytes at a time, we can identify which ones are unnecessary.

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