Ok, I’ve done it. I’ve installed the ActivityPub plugin and put this blog on the Fediverse. Why? Well, it’s new, it’s interesting, and I really want to see the Fediverse grow.
What is the Fediverse, you ask? Well, in a nutshell, it’s a network comprised of various types of social networks that use a common protocol to talk between them. That protocol is ActivityPub.
There are tons of different types of social networks you can use. Mastodon is the primary one folks know about. Mastodon is a social network similar to Twitter, Bluesky, etc. But there are others.
PeerTube is a Youtube style network for hosting videos.
PixelFed is an image sharing network. Think Imgur or Instagram.
Lemmy is a conversation platform similar to Reddit.
And there are a TON more! Each of these is an app you can run on your own, thus owning your own content and making whatever choices you want with respect to the content. And if you’re not interested in running your own instance, there are plenty of instances out there that you can join.
Right now I’m just experimenting to see how this works with a blog on the Fediverse, but I’m hoping it works out and I can get back to blogging on a regular basis.
Welcome to the world of metrics collection and performance monitoring. As with most things IT, entire market sectors have been built to sell these tools. And, of course, there are a number of open source tools that serve the same purpose. It’s one of these open source tools that we’re going to take a look at.
What is Prometheus?
Prometheus is a metrics collection and alerting tool developed and released to open source by SoundCloud. Prometheus is similar in design to Google’s Borgmon monitoring system and a relatively modest system can handle collecting hundreds of thousands of metrics every second. Properly tuned and deployed, a Prometheus cluster can collect millions of metrics every second.
Prometheus is made up of roughly four parts:
The main Prometheus app itself that is responsible for scraping metrics, storing them in the database, and (optionally) retrieving them when queried.
The database backend is an internal Time Series database. This database is always used, but data can also be sent to remote storage backends.
Exporters are optional external programs that ingest data from a variety of sources and convert it to metrics that Prometheus can scrape.
Exporters are purpose built applications for working with specific applications and hardware.
AlertManager is an alert management system. It ships with Prometheus.
Client Libraries that can be used to instrument custom applications.
I say “roughly” four parts because there are plenty of additional applications that are often used with a standard Prometheus cluster. If you need or want better graphing capabilities, applications like Grafana can be deployed. If you need to store metrics for long periods of time, remote storage backends are worth looking into. And the list goes on. For the purposes of this article, however, we’re going to focus on Prometheus itself with a small detour into exporters.
What is a metric?
But before we get there, we need to understand why something like Prometheus exists. So let’s start with a question. What are metrics? Simply put, metrics measure something. For instance, the time it takes you to read this article is a metric. The number or words is a metric. The average number of letters in the words of this article is a metric.
But those metrics are fairly static and not something you’d necessarily need a system like Prometheus for. Prometheus excels at metrics that change over time. For instance, what if you wanted to know how many “views” this article is getting? Or what if you wanted to know how much traffic was entering and leaving your network? Or how many build and deploy cycles are happening each hour? All of these are metrics that can be fed into Prometheus.
Now that we understand what a metric is, let’s take a look at how Prometheus gets the metrics it needs to store. The first thing Prometheus needs is a target. Targets are the endpoints that supply the metrics that Prometheus stores. These endpoints can be the actual endpoint being monitored or they can be a piece of middleware known as an exporter. Endpoints can be supplied via a static configuration or they can be “found” through a process called service discovery. Service Discovery is a more advanced topic and will be covered in a future article.
Once Prometheus has a list of endpoints, it can begin to retrieve metrics from them. Prometheus retrieves metrics in a very straightforward manner, a simple HTTP request. The configuration points at a specific location on the endpoint that will supply a stream of text identifying the metric and its current value. Prometheus reads this stream of text, ignoring lines beginning with a # as comments, and stores the metrics it receives in a local database.
A short sidetrack into Exporters
Prometheus can only talk HTTP to endpoints for metrics collection. So what happens when you’re trying to monitor a router or switch that only communicates using SNMP? Or perhaps you want to monitor a cloud service that doesn’t have a native Prometheus metrics endpoint? Fortunately, there’s a solution. Exporters.
Exporters come in many shapes and sizes. These are small, purpose-built programs designed to stand between Prometheus and anything you want to monitor that doesn’t natively support Prometheus. Some exporters sit idle until Prometheus polls them for data. When this happens, the exporter reaches out to the device it’s monitoring, gets the relevant data, and converts it to a format that Prometheus can ingest. Other exporters poll devices automatically, caching the results locally for Prometheus to pick up later.
Regardless of design, exporters act as a translator between Prometheus and endpoints you want to monitor. Chances are, if you’re trying to monitor a common device or application, there’s an exporter out there for it.
Prometheus uses a special type of database on the back end known as a time series database. Simply put, this database is optimized to store and retrieve data organized as values over a period of time. Metrics are an excellent example of the type of data you’d store in such a database.
External storage is also an option. There are many options such as Thanos, Cortex, and VictoriaMetrics that provide a variety of benefits. One of the primary benefits is to centralize the gathered metrics and allow for long term storage. Tools such as Grafana can query these third party storage solutions directly.
So you have a bunch of metrics…
Now that you’re an expert on Prometheus and you have it storing metrics, how do you use this data? Much like a SQL database, Prometheus has a custom query language known as PromQL. PromQL is pretty straightforward for simple metrics but has a lot of complexity when needed. Simply supplying the name of a metric will show all “instances” of that metric:
Or you can use some PromQL methods and generate a graph representing the data you’re after.
Of course, if you’re serious about graphing, it’s worth looking into a package such as Grafana. Grafana allows you to create dashboards of metrics, send alerts, and more.
While graphs are pretty to look at, metrics can serve another, important, purpose. They can be used to send alerts. Prometheus includes a separate application, called AlertManager, that serves this purpose. AlertManager receives notifications from Prometheus and handles all of the necessary logic to dedupe and deliver the alerts.
Alerts are created by writing alert rules. These rules are simply PromQL queries that fire when the query is true. That is, if you have a query that checks if the temperature on the cpu is over 80C then the query fires for each metric that meets that condition.
Alert rules can also include a time period over which a rule must evaluate to true. Expanding on our temperature example, exceeding 80C is ok if it’s a brief period of time. But if it lasts more than 5 minutes, send an alert. Alerts can be sent via email, slack, twitter, sms, and pretty much anything else you can write an interface for.
Monitoring is important. It helps identify when things have gone wrong and it can show when things are going right. Proper monitoring can be used across a variety of disciplines to squeeze everything you can out of the object being monitored.
PromQL provides a robust querying language that can be used for graphing as well as alerting. The built-in graphing system is great for quick visualizations but longer term dashboarding should be handled in external applications such as Grafana.
One of the first industry jobs I ever had was for a small regional ISP. At the time, 56k modems were shiny and new. We had a couple dozen PoPs (Points of Presence) where we installed banks of modems and fed the data back to our main office via a series of full and fractionalT1 lines.
We provided the typical slew of services: email, net news, and general internet access. Of course, to provide these services we needed servers of our own. The solution was to set up a cluster of SCO UNIX systems. Yes, *that* SCO. It’s been quite a while, but a cluster setup like this is hard to forget. The servers were set up in such a way that they had dependencies on each other. If one failed, it didn’t cause everything to come crashing down, but getting that one server back up generally required restarting everything.
The general setup was that the servers nfs mounted each other on startup. This, of course, causes a race condition during startup. The engineers had written a detailed document that explained the steps required to bring the entire cluster up after a failure. The entire process usually took 30-45 minutes.
At the time, I was a lowly member of tech support, spending the majority of my time hand-holding new customers through the process of installing the software necessary to get online. I was relatively new to the world of Unix and high speed networking and sucking up as much knowledge as I could.
One of the folks I worked with, Brett, taught me a lot. He wrote the network monitoring system we used and spent his time between that and keeping the network up and running. He was also a bit of a prankster at times.
At the end of one pretty typical day, I happened to be on the unix cluster. Out of the blue, my connection failed and I was booted back to my local OS. This was a bit weird, but it happened occasionally so I simply logged back in. Within a few seconds I was booted out again.
I started doing a bit of debugging, trying to figure out what was going on. I don’t remember everything that I did, but I do remember putting together some quick scripts to log in, check various processes, and try ot figure out what was happening. At some point I determined that I was being booted off the system by another user, Brett.
Once I figured out what was going on, I had to fight back. So I started playing around with shell scripting, trying to figure out how to identify what the pid of his shell was so I could boot him offline. This went back and forth for a bit, each of us escalating the attacks. We started using other services to regain access, launch attacks, etc.
That is, until I launched what I thought would be the ULTIMATE attack. I wrote a small shell script that searched for his login, identified the shell, and subsequently killed his access. Pretty simple, but I added the ultimate twist. After the script ran, it ran a copy of itself. BOOM. No way he can get back on now.
And it worked! Brett lost his access and simply could not gain a foothold over the next 5 or so minutes. And, of course, I had backgrounded the task so I could interact with the console and verify that he was beaten. I had won. I had proven that I could beat the experienced engineer and damn, I felt good about it.
ksh: fork: Resource temporarily unavailable
The beginning of the end
I had never seen such an error before. What was this? Why was the system doing this? And why was it streaming all over my console, making it impossible for me to do anything?
It took a few moments but Brett noticed the problem as well. He came out to see what had happened. I explained my brilliant strategy and he simply sighed, smiled, and told me I’d have to handle rebooting and re-syncing the servers. And then he took the time to explain to me what I had done wrong. That was the day I learned about “exec” and how important it is.
Unfortunately, Brett passed away about a decade or so after this. He was a great friend, a great mentor, and I miss him.
Memory is a funny construct. What we remember is often not what happened, or even the order it happened in. One of the earliest memories I have about technology was a trip my father and I took. We drove to Manhattan, on a mission to buy a Christmas gift for my mother. This crazy new device, The Itty Bitty Book Light, had come out recently and, being an avid reader, my mother had to have one.
We drove to Manhattan, on a mission to buy a Christmas gift for my mother. This crazy new device, The Itty Bitty Book Light, had come out recently and, being an avid reader, my mother had to have one.
After navigating the streets of Manhattan and finding a parking spot, we walked down the block to what turned out to be a large bookstore. You’ve seen bookstores like this on TV and in the movies. It looks small from the outside, but once you walk in, the store is endless. Walls of books, sliding ladders, tables with books piled high. It was pretty incredible, especially for someone like me who loves reading.
But in this particular store, there was something curious going on. One of the tables was surrounded by adults, awed and whispering between one another. Unsure of what was going on, we approached. After pushing through the crowd, what I saw drew me in immediately. On the table, surrounded by books, was a small grey box, the Apple Macintosh. It was on, but no one dared approach it. No one, that is, except me. I was drawn like a magnet, immediately grokking that the small puck like device moved the pointer on the screen. Adults gasped and murmured, but I ignored them all and delved into the unknown. The year was, I believe, 1984.
Somewhere around the same time, though likely a couple years before, my father brought home a TI-99/4A computer. From what I remember, the TI had just been released, so this has to be somewhere around 1982. This machine served as the catalyst for my love of computer technology and was one of the first machines I ever cut code on.
My father tells a story about when I first started programming. He had been working on an inventory database, written from scratch, that he built for his job. I would spend hours looking over his shoulder, absorbing everything I saw. One time, he finished coding, saved the code, and started typing the command to run his code (“RUN”). Accordingly to him, I stopped him with a comment that his code was going to fail. Ignoring me as I was only 5 or 6 at the time (according to his recollection), he ran the code and, as predicted, it failed. He looked at me with awe and I merely looked back and replied “GOSUB but no RETURN”.
At this point I was off and running. Over the years I got my hands on a few other systems including the Timex Sinclair, Commodore 64 and 128, TRS-80, IBM PS/2, and finally, my very own custom built PC from Gateway 2000. Since them I’ve built hundreds of machines and spend most of my time on laptops now.
The Commodore 64 helped introduce me to the online world of Bulletin Board Systems. I spent many hours, much to the chagrin of my father and the phone bill, calling into various BBS systems in many different states. Initially I was just there to play the various door games that were available, but eventually discovered the troves of software available to download. Trading software become a big draw until I eventually stumbled upon the Usenet groups that some boards had available. I spent a lot of time reading, replying, and even ended up in more than one flame war.
My first introduction to Unix based operating systems was in college when I encountered an IBM AIX mainframe as well as a VAX. I also had access to a shell account at the local telephone company turned internet service provider. The ISP I was using helpfully sent out an email to all subscribers about the S.A.T.A.N. toolkit and how accounts found with the software in their home directories would be immediately banned. Being curious, I immediately responded looking for more information. And while my account was never banned, I did learn a lot.
At the same time, my father and I started our own BBS which grew into a local Internet Service Provider offering dial-up services. That company still exists today, though the dial-up service died off several years ago. This introduced me to networks and all the engineering that comes along with it.
From here my journey takes a bit of a turn. Since I was a kid, I’ve wanted to build video games. I’ve read a lot of books on the subject, talked to various developers (including my idol, John Carmack) and written a lot of code. I actually wrote a pacman clone, of sorts, on the Commodore 64 when I was younger. But, alas, I think I was born on the wrong coast. All of the game companies, at the time, were located on the west coast and I couldn’t find a way to get there. So, I redirected my efforts a bit and focused on the technology I could get my hands on.
Running a BBS, and later an ISP, was a great introduction into the world of networking. After working a few standard school-age jobs (fast food, restaurants, etc), I finally found a job doing tech support for an ISP. I paid attention, asked questions, and learned everything I could. I took initiative where I could, even writing a cli-based ticketing system with an mSQL database backing it.
This initiative paid of as I was moved later to the NOC and finally to Network Engineering. I lead the way, learning everything I could about ATM and helping to design and build the standard ATM-based node design used by the company for over a decade. I took over development of the in-house monitoring system, eventually rewriting it in KSH and, later, Perl. I also had a hand in development of a more modern ticketing system written in Perl that is still in use today.
I spent 20+ years as a network engineer, taking time along the way to ensure that we had Linux systems available for the various scripting and monitoring needed to ensure the network performed as it should. I’ve spent many hours writing code in shell, expect, perl, and other languages to automate updates and monitoring. Eventually, I found myself looking for a new role and a host of skills ranging from network and systems administration to coding and security.
About this time, DevOps was quickly becoming the next new thing. Initially I rejected the idea, solely responding to the “Development” and “Operations” tags that make up the name. Over time, however, I came to realize that DevOps is yet another fancy name for what I’ve been doing for decades, albeit with a few twists here and there. And so, I took a role as a DevOps Engineer.
DevOps is a fun discipline, mixing in technologies from across the spectrum in an effort to automate away everything you can. Let the machine do the work for you and spent your time on more interesting projects like building more automation. And with the advent of containers and orchestration, there’s more to do than ever.
These days I find myself leading a team of DevOps engineers, both guiding the path we take as we implement new technology and automate existing processes. I also spend a lot of time learning about new technologies both on my own and for my day job. My trajectory seems to be changing slightly as I head towards the world of the SRE. Sort of like DevOps, but a bit heavier on the development side of things.
Life throws curves and sometimes you’re not able to move in the direction you want. But if you keep at it, there’s something out there for everyone. I still love learning and playing with technology. And who knows, maybe I’ll end up writing games at some point. There’s plenty of time for another new hobby.
“Intruder alert! Strace to PID 45555, log and store.” “Roger that, overseer. Target acquired.” “Status report!” “Suspicious activity verified, permission to terminate?” “Permission granted, deploy Sigkill.” “Process terminated at 2146 hours.”
Intrusion Prevention Systems, or IPS, are tools designed to detect and stop intrusions in their tracks. They come two basic flavors, network-based and host-based. As you may suspect, a network-based IPS is meant to be deployed to monitor the network and a host-based IPS is deployed on a host with the intention of monitoring just a single host.
How these tools work varies from vendor to vendor, but the basics are the same. The network-based tool monitors traffic on the network and matches it to a long list of known signatures. These signatures describe a variety of attacks ranging from simple corrupt packets to more specific attacks such as SQL injection.
Host-based tools tend to have more capabilities as they have access to the entire host. A host-based IPS can look at network traffic as well as monitor files and logs. One of the more popular tools, OSSEC-HIDS, monitors traffic, logs, file integrity, and even has signatures for common rootkits.
More advanced tools have additional detection capabilities such as statistical anomaly detection or stateful protocol inspection. Both of these capabilities use algorithms to detect intrusions. This allows detection of intrusions that don’t yet have signatures created for them.
IDS vs IPS
Unlike it’s predecessor, the Intrusion Detection System, or IDS, when an IPS detects an intrusion it moves to block the traffic and prevent it from getting to its target. As you can imagine, ensuring that the system blocks only bad traffic is of utmost importance. Deploying a tool that blocks legitimate traffic is a quick way to frustrate users and get yourself in trouble. An IDS merely detects the traffic and sends an alert. Given the volume of traffic on a typical network or host today, an IDS is of limited use.
The Redhat Enterprise Packages for Enterprise Linux repository includes two great HIDS apps that every admin should look into. HIDS apps are a bit of a hybrid solution in that they’re both an IDS and an IPS. That is, they can be configured to simply alert when they see issues or they can often be configured to run scripts to react to scenarios that trigger alerts.
First up from EPEL is Tripwire, a file integrity monitoring tool, which Seth Kenlonwrote about for Enable Sysadmin back in April. Tripwire’s job in life is to monitor files on the host and send an alert if they change in ways they’re not supposed to. For instance, you can monitor a binary such as /bin/bash and send an alert if the file changes in some way such as permissions or other attributes of the file. Attackers often modify common binaries and add a payload intended to keep their access to the server active. Tripwire can detect that.
The second EPEL package is fail2ban. Fail2ban is more of an IPS style tool in that it monitors and acts when it detects something awry. One common implementation of fail2ban is monitoring the openssh logs. By building a signature that identifies a failed login, fail2ban can detect multiple attempts to login from a single source address and block that source address. Typically, fail2ban does this by adding rules to the host’s firewall, but in reality, it can run any script you can come up with. So, for instance, you can write a script to block the IP on the local firewall and then transmit that IP to some central system that will distribute the firewall block to other systems. Just be careful, however, as globally blocking yourself from every system on the network can be rather embarrassing.
OSSEC-HIDS, mentioned previously, is a personal favorite of mine. It’s much more of a swiss army knife of tools. It combines tools like tripwire and fail2ban together into a single tool. It can be centrally managed and uses encrypted tunnels to communicate with clients. The community is very active and new signatures are created all the time. Definitely worth checking out.
Snort is a network-based IDS/IPS (NIDS/NIPS). Where HIDS are installed on servers with the intention of monitoring processes on the server itself, NIDS are deployed to monitor network traffic. Snort was first introduced in 1998 and has more recently been acquired by Cisco. Cisco has continued to support Snort as open source while simultaneously incorporating it into their product line.
Snort, like most NIDS/NIPS, works by inspecting each packet as it passes through the system. Snort can be deployed as an IDS by mirroring traffic to the system, or it can be deployed as an IPS by putting it in-line with traffic. In either case, Snort inspects the traffic and compares it to a set of signatures and heuristic patterns, looking for bad traffic. Snort’s signatures are updated on a regular basis and uses libpcap, the same library used by many popular network inspection tools such as tcpdump and wireshark.
Snort can also be configured to capture traffic for later inspection. Be aware, however, that this can eat up disk space pretty rapidly.
Suricata is a relatively new IDS/IPS, released in 2009. Suricata is designed to be multi-threaded, making it much faster than competing products. Like Snort, it uses signatures and heuristic detection. In fact, it can use most Snort rules without any changes. It also has it’s own ruleset that allows it to use additional features such as file detection and extraction.
Zeek (Formerly Bro)
Bro was first release in 1994, making it one of the oldest IDS applications mentioned here. Originally named in reference to George Orwell’s book, 1984, more recent times have seen a re-branding to the arguably less offensive name, Zeek.
Zeek is often used as a network analysis tool but can also be deployed as an IDS. Like Snort, Zeek uses libpcap for packet capture. Once packets enter the system, however, any number of frameworks or scripts can be applied. This allows Zeek to be very flexible. Scripts can be written to be very specific, targeting specific types of traffic or scenarios, or Zeek can be deployed as an IDS, using various signatures to identify and report on suspect traffic.
One final word of caution. IPS tools are powerful and extremely useful in automating the prevention of intrusions. But like all tools, they need to be maintained properly. Ensure your signatures are up to date and monitor the reports coming from the system. It’s still possible for a skilled attacker to slip by these tools and gain access to your systems. Remember, there is no such thing as a security silver bullet.
I’ve spent a career building networks and servers, deploying, troubleshooting, and caring for applications. When there’s a network problem, be it outages or failed deployments, or you’re just plain curious about how things work, three simple tools come to mind: ping, traceroute, and netstat.
Ping is quite possibly one of the most well known tools available. Simply put, ping sends an “are you there?” message to a remote host. If the host is, in fact, there, it returns a “yup, I’m here” message. It does this using a protocol known as ICMP, or Internet Control Message Protocol. ICMP was designed to be an error reporting protocol and has a wide variety of uses that we won’t go into here.
Ping uses two message types of ICMP, type 8 or Echo Request and type 0 or Echo Reply. When you issue a ping command, the source sends an ICMP Echo Request to the destination. If the destination is available, and allowed to respond, then it replies with an ICMP Echo Reply. Once the message returns to the source, the ping command displays a success message as well as the RTT or Round Trip Time. RTT can be an indicator of the latency between the source and destination.
Note: ICMP is typically a low priority protocol meaning that the RTT is not guaranteed to match what the RTT is to a higher priority protocol such as TCP.
When the ping command completes, it displays a summary of the ping session. This summary tells you how many packets were sent and received, how much packet loss there was, and statistics on the RTT of the traffic. Ping is an excellent first step to identifying whether or not a destination is “alive” or not. Keep in mind, however, that some networks block ICMP traffic, so a failure to respond is not a guarantee that the destination is offline.
$ ping google.com
PING google.com (18.104.22.168): 56 data bytes
64 bytes from 22.214.171.124: icmp_seq=0 ttl=56 time=15.740 ms
64 bytes from 126.96.36.199: icmp_seq=1 ttl=56 time=14.648 ms
64 bytes from 188.8.131.52: icmp_seq=2 ttl=56 time=11.153 ms
64 bytes from 184.108.40.206: icmp_seq=3 ttl=56 time=12.577 ms
64 bytes from 220.127.116.11: icmp_seq=4 ttl=56 time=22.400 ms
64 bytes from 18.104.22.168: icmp_seq=5 ttl=56 time=12.620 ms
--- google.com ping statistics ---
6 packets transmitted, 6 packets received, 0.0% packet loss
round-trip min/avg/max/stddev = 11.153/14.856/22.400/3.689 ms
The example above shows a ping session to google.com. From the output you can see the IP address being contacted, the sequence number of each packet sent, and the round trip time. 6 packets were sent with an average RTT of 14ms.
One thing to note about the output above and the ping utility in general. Ping is strictly an IPv4 tool. If you’re testing in an IPv6 network you’ll need to use the ping6 utility. The ping6 utility works roughly identical to the ping utility with the exception that it uses IPv6.
Traceroute is a finicky beast. The premise is that you can use this tool to identify the path between a source and destination point. That’s mostly true, with a couple of caveats. Let’s start by explaining how traceroute works.
Think of traceroute as a string of ping commands. At each step along the path, traceroute identifies the IP of the hop as well as the latency to that hop. But how is it finding each hop? Turns out, it’s using a bit of trickery.
Traceroute uses UDP or ICMP, depending on the OS. On a typical *nix system it uses UDP by default, sending traffic to port 33434 by default. On a Windows system it uses ICMP. As with ping, traceroute can be blocked by not responding to the protocol/port being used.
When you invoke traceroute you identify the destination you’re trying to reach. The command begins by sending a packet to the destination, but it sets the TTL of the packet to 1. This is significant because the TTL value determines how many hops a packet is allowed to pass through before an ICMP Time Exceeded message is returned to the source. The trick here is to start the TTL at 1 and increment it by 1 after the ICMP message is received.
$ traceroute google.com
traceroute to google.com (22.214.171.124), 64 hops max, 52 byte packets
1 192.168.1.1 (192.168.1.1) 1747.782 ms 1.812 ms 4.232 ms
2 10.170.2.1 (10.170.2.1) 10.838 ms 12.883 ms 8.510 ms
3 xx.xx.xx.xx (xx.xx.xx.xx) 10.588 ms 10.141 ms 10.652 ms
4 xx.xx.xx.xx (xx.xx.xx.xx) 14.965 ms 16.702 ms 18.275 ms
5 xx.xx.xx.xx (xx.xx.xx.xx) 15.092 ms 16.910 ms 17.127 ms
6 126.96.36.199 (188.8.131.52) 13.711 ms 14.363 ms 11.698 ms
7 184.108.40.206 (220.127.116.11) 12.802 ms
18.104.22.168 (22.214.171.124) 12.647 ms 12.963 ms
8 lga34s13-in-f14.1e100.net (126.96.36.199) 11.901 ms 13.666 ms 11.813 ms
Traceroute displays the source address of the ICMP message as the name of the hop and moves on to the next hop. When the source address matches the destination address, traceroute has reached the destination and the output represents the route from the source to the destination with the RTT to each hop. As with ping, the RTT values shown are not necessarily representative of the real RTT to a service such as HTTP or SSH. Traceroute, like ping, is considered to be lower priority so RTT values aren’t guaranteed.
There is a second caveat with traceroute you should be aware of. Traceroute shows you the path from the source to the destination. This does not mean that the reverse is true. In fact, there is no current way to identify the path from the destination to the source without running a second traceroute from the destination. Keep this in mind when troubleshooting path issues.
Netstat is an indispensable tool that shows you all of the network connections on an endpoint. That is, by invoking netstat on your local machine, all of the open ports and connections are shown. This includes connections that are not completely established as well as connections that are being torn down.
$ sudo netstat -anptu
Active Internet connections (servers and established)
Proto Recv-Q Send-Q Local Address Foreign Address State PID/Program name
tcp 0 0 0.0.0.0:25 0.0.0.0:* LISTEN 4417/master
tcp 0 0 0.0.0.0:443 0.0.0.0:* LISTEN 2625/java
tcp 0 0 192.168.1.38:389 0.0.0.0:* LISTEN 559/slapd
tcp 0 0 0.0.0.0:22 0.0.0.0:* LISTEN 1180/sshd
tcp 0 0 192.168.1.38:37190 192.168.1.38:389 ESTABLISHED 2625/java
tcp 0 0 192.168.1.38:389 192.168.1.38:45490 ESTABLISHED 559/slapd
The output above shows several different ports in a listening state as well as a few established connections. For listening ports, if the source address is 0.0.0.0, it is listening on all available interfaces. If there is an IP address instead, then the port is open only on that specific interface.
The established connections show the source and destination IPs as well as the source and destination ports. The Recv-Q and Send-Q fields show the number of bytes pending acknowledgement in either direction. Finally, the PID/Program name field shows the process ID and the name of the process responsible for the listening port or connection.
Netstat also has a number of switches that can be used to view other information such as the routing table or interface statistics. Both IPv4 and IPv6 are supported. There are switches to limit to either version, but both are displayed by default.
In recent years, netstat has been superseded by the ss command. You can find more information on the ss command in this post by Ken Hess.
As you can see, these tools are invaluable when troubleshooting network issues. As a network or systems administrator, I highly recommend becoming intimately familiar with these tools. Having these available may save you a lot of time troubleshooting.
So you want to deploy an application… You’ve put in the time and developed the latest and greatest widget and now you want to expose it to the world. It’s something truly ground breaking and people will be rushing to check it out. But where do you start? What’s involved with deploying an application, anyway?
The answer, it turns out, varies quite a bit, depending on what you’re trying to accomplish. So where to begin? First you need to determine what architecture you want to use. Are you deploying a simple website that can be stood up on a LAMP stack? Or perhaps this is something a bit more involved that requires additional back end services.
Do you have data to be stored? If so, how are you planning on storing this data? Are you using a SQL or NoSQL database? Do you need to store files?
How much traffic are you expecting? Do you need to be able to scale to larger workloads? Will you scale vertically or horizontally? Do you have or need a caching layer?
What about security? How will you protect the application? What about the data the application stores? How “secure” do you need to make your application and it’s deployment?
As you can see, there are a lot of questions to answer, despite this being a relatively simple application. Over the next few blog posts I’ll be digging into various deployment strategies, specifics on various choices you can make, and the pros and cons of each. Links to each post can be found below:
Back in 2012 I gave a talk at Derbycon 2.0. This was my first infosec talk and I was a little nervous, to say the least. Anyway, I described a system I wanted to write that handled distributed baseline scanning.
After a lot of starts and stops, I finished a basic 1.0 version in 2014. It’s still quite rough and I’ve since been working, intermittently, on making the system more robust and solid. I’ve been working on a python replacement for the GUI as well, instead of the current PHP one. The repository is located here, if you’re interested in taking a look.
Why am I telling you all of this? Well, as part of the updates I’m making, I wanted to do things the “right way” and make sure I have unit testing in place before I start making additional changes to the code. Problem is, while I learned about unit testing, I’ve never really implemented it in any meaningful way, so this is a bit new to me.
So why unit testing? Well, the hypothesis is that by creating tests that check every line of code, you ensure that the code is working as expected. Thus, if the tests pass, then the code should be solid and bug free. In reality, this is rarely the case. Tests can be just as flawed as any other code. Additionally, you may miss testing certain corner cases and miss potential bugs. In the end, the general consensus is that unit testing is a complicatedreligiousargument.
Let’s assume that we want to unit test anyway and move on to the actual testing bits, shall we? We’ll start with a contrived example to make things easier. Assume we have the following code in a file called mytestcode.py:
Simple enough, just a simple function to return the value of two numbers added together. Let’s create some test cases, shall we?
from mytestcode import add
assert add(1,1) == 2
assert add(1,1) != 3
What we have here are two simple test cases. First, we test to make sure that if we call the add function with two values, 1 and 1, we get a 2 as a return value. Second, we test that providing the same values as input does not return a 3. Simple, right? But have we really tested all of the corner cases? What happens if we feed the function a negative? How about a non-numeric value? Are there cases where we can cause an exception?
To be fair, the original function is poorly written and is merely being used as a simple example. This is the problem with contrived examples, of course. They miss important details, often simply things too much, and can lead to beginners making big mistakes when using them as teaching tools. So please, be aware, the above code really isn’t very good code. It’s intended to be simple to understand.
Let’s take a look at some “real” code directly from my distributed scanner project. This particular code is something I found on Stack Overflow when I was looking for a way to identify whether a process was still running or not.
"""Check whether pid exists in the current process table.
if pid < 0:
if pid == 0:
# According to "man 2 kill" PID 0 refers to every process
# in the process group of the calling process.
# On certain systems 0 is a valid PID but we have no way
# to know that in a portable fashion.
raise ValueError('invalid PID 0')
except OSError as err:
if err.errno == errno.ESRCH:
# ESRCH == No such process
elif err.errno == errno.EPERM:
# EPERM clearly means there's a process to deny access to
# According to "man 2 kill" possible error values are
# (EINVAL, EPERM, ESRCH)
Testing this code should be relatively straightforward, with the exception of the os.kill call. For that, we’ll need to delve into mock objects. Let’s tackle the simple cases first:
from libs.funcs import pid_exists
assert pid_exists(-1) == False
with pytest.raises(ValueError) as e_info:
That’s relatively simple. We verify that False is returned for a negative PID and a ValueError is returned for a PID of zero. We also test that a TypeError is returned if we don’t provide an integer value. What’s left is handling a valid PID and testing that it returns True for a running process and False otherwise. In order to test the rest, we could go through a lot of elaborate setup to start a process, get the PID, and then test our code, but there’s a lot that can go wrong there. Additionally, we’re looking to test our logic and not the entirety of another module. So, what we really want is a way to provide an arbitrary return value for a given call. Enter the mock module.
The mock module is part of the unittest framework in python. Essentially, the mock module allows you to identify a call or an object that you want to create a fake version of, and then provide the behavior you’re expecting that mocked version to have. So, for instance, you can mock a function call and simply provide the return value you’re looking for instead of having to call the function directly. This functionality allows you to precisely test your logic versus doing a deeper integration test.
To finish up our testing code for the pid_exists() function, we want to mock the os.kill() function and have it return specific values so we can check the various branches of code we have.
The above code tests all of the branching available in the rest of the code, verifying the logic we’ve written. The code should be pretty straightforward. The return_value attribute of a mock object directly defines what we want the mocked function to recall while the side_effect attribute allows us to throw an exception in response to the function call. With those two features of a mocked object, we’re able to successfully test the rest of the cases we need.
This little journey to learn how to write unit tests has been fun and informative. I just need to finish up the rest of the code, striving to hit as close to 100% coverage as I can while keeping the test cases reasonable. It’s taken a while to get going, but the more code I’ve been writing, the faster and more accurate I’m getting. As they say, “practice makes perfect,” though I’d settle with functionally complete and relatively bug-free.
One final word of caution. I’m a sole developer working on this code, so I’m the only one around to write test cases. In a larger shop, the originator of the logic should not be the one writing the test cases. The reason for this is that the original coder typically knows their code quite well and has expectations regarding how the code will be used. For instance, I’m expecting that anyone calling the add() function I wrote above to only supply numbers and I haven’t added any sort of type checking or input validation. As a result, I avoided adding test cases that supply invalid inputs, knowing that would fail. Someone else writing the test cases would likely have provided a number of different inputs and found that input validation was missing. So if you’re in a larger shop, do yourself a favor and have someone else write your test cases. And to ensure they provide robust test cases, only provide the function prototypes and not the full function definitions.
It looks a bit different around here lately. Sure, it’s roughly the same as what it was, but something is off.. A little bit here and there, so what changed?
Well, to tell the truth, I’ve switched blogging platforms. Don’t get me wrong, I love Serendipity. I’ve used it for years, love the features, love the simplicity. Unfortunately, Serendipity doesn’t have the greatest support for offline blogging, updates are relatively sparse, and it’s limited to just blogging. So I decided it’s time for a change.
Ok. Deep breath. I’ve switched to WordPress. Yes, yes, I know. I’ve decried WordPress as an insecure platform for a long time, but I’ve somewhat changed my thinking. The team at WordPress has done a great job ensuring the core platform is secure and they’re actively working to help older installations upgrade to newer releases. Plugins are where the majority of the security issues exist these days, and many of the more popular plugins are being actively scanned for security issues. So, overall, the platform has moved forward with respect to security and is more than viable.
I’ve also been leveraging Docker in recent years. We’ll definitely be talking about Docker in the coming days/weeks, so I won’t go into it here. Suffice it to say, Docker helps enhance the overall security of the system while simultaneously making it a breeze to deploy new software and keep it up to date.
So, enjoy the new digs, and hopefully more changes will be coming in the near future. WordPress is capable of doing more than just blogging and I’m planning on exploring some of those capabilities a bit more. This is very much a continuing transition, so if you see something that’s off, please leave a comment and I’ll take a look.
I’ve recently been reading Wired for War by P.W. Singer and one of the concepts he mentions in the book is Network Enhanced Telepathy. This struck me as not only something that sounds incredibly interesting, but something that we’ll probably see hit mainstream in the next 5-10 years.
According to Wikipedia, telepathy is “the purported transmission of information from one person to another without using any of our known sensory channels or physical interaction.“ In other words, you can think *at* someone and communicate. The concept that Singer talks about in the book isn’t quite as “mystical” since it uses technology to perform the heavy lifting. In this case, technology brings fantasy into reality.
Scientists have alreadydeveloped methods to “read” thoughts from the human mind. These methods are by no means perfect, but they are a start. As we’ve seen with technology across the board from computers to robotics, electric cars to rockets, technological jumps may ramp up slowly, but then they rocket forward at a deafening pace. What seems like a trivial breakthrough at the moment may well lead to the next step in human evolution.
What Singer describes in the book is one step further. If we can read the human mind, and presumably write back to it, then adding a network in-between, allowing communication between minds, is obvious. Thus we have Network Enhanced Telepathy. And, of course, with that comes all of the baggage we associate with networks today. Everything from connectivity issues and lag to security problems.
The security issues associated with something like this range from inconvenient to downright horrifying. If you thought social engineering was bad, wait until we have a direct line straight into someone’s brain. Today, security issues can result in stolen data, denial of service issues, and, in some rare instances, destruction of property. These same issues may exist with this new technology as well.
Stolen data is pretty straightforward. Could an exploit allow an attacker to arbitrarily read data from someone’s mind? How would this work? Could they pinpoint the exact data they want, or would they only have access to the current “thoughts” being transmitted? While access to current thoughts might not be as bad as exact data, it’s still possible this could be used to steal important data such as passwords, secret information, etc. Pinpointing exact data could be absolutely devastating. Imagine, for a moment, what would happen if an attacker was able to pluck your innermost secrets straight out of your mind. Everyone has something to hide, whether that’s a deep dark secret, or maybe just the image of themselves in the bathroom mirror.
I’ve seen social engineering talks wherein the presenter talks about a technique to interrupt a person, mid-thought, and effectively create a buffer overflow of sorts, allowing the social engineer to insert their own directions. Taken to the next level, could an attacker perform a similar attack via a direct link to a person’s mind? If so, what access would the attacker then attain? Could we be looking at the next big thing in brainwashing? Merely insert the new programming, directly into the user.
How about Denial of Service attacks or physical destruction? Could an attacker cause physical damage in their target? Is a connection to the mind enough access to directly modify the cognitive functions of the target? Could an attacker induce something like Locked-In syndrome in a user? What about blocking specific functions, preventing the user from being able to move limbs, or speak? Since the brain performs regulatory control over the body, could an attacker modify the temperature, heart rate, or even induce sensations in their target? These are truly scary scenarios and warrant serious thought and discussion.
Technology is racing ahead at breakneck speeds and the future is an exciting one. These technologies could allow humans to take that next evolutionary step. But as with all technology, we should be looking at it with a critical eye. As technology and biology become more and more intertwined, it is essential that we tread carefully and be sure to address potential problems long before they become a reality.