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October 29, 2014
If you manage any Linux machines, it is essential that you know where the log files are located, and what is contained in them. Such files are usually in /var/log. Logging is controlled by the associated .conf file.
Some log files are distribution specific and this directory can also contain applications such as samba, apache, lighttpd, mail etc.
From a security perspective, here are 5 groups of files which are essential. Many other files are generated and will be important for system administration and troubleshooting.
1. The main log file
a) /var/log/messages – Contains global system messages, including the messages that are logged during system startup. There are several things that are logged in /var/log/messages including mail, cron, daemon, kern, auth, etc.
2. Access and authentication
a) /var/log/auth.log – Contains system authorization information, including user logins and authentication machinsm that were used.
b) /var/log/lastlog – Displays the recent login information for all the users. This is not an ascii file. You should use lastlog command to view the content of this file.
c) /var/log/btmp – This file contains information about failed login attemps. Use the last command to view the btmp file. For example, “last -f /var/log/btmp | more”
d) /var/log/wtmp or /var/log/utmp – Contains login records. Using wtmp you can find out who is logged into the system. who command uses this file to display the information.
e) /var/log/faillog – Contains user failed login attemps. Use faillog command to display the content of this file.
f) /var/log/secure – Contains information related to authentication and authorization privileges. For example, sshd logs all the messages here, including unsuccessful login.
3. Package install/uninstall
a) /var/log/dpkg.log – Contains information that are logged when a package is installed or removed using dpkg command
b) /var/log/yum.log – Contains information that are logged when a package is installed using yum
a) /var/log/daemon.log – Contains information logged by the various background daemons that runs on the system
b) /var/log/cups – All printer and printing related log messages
c) /var/log/cron – Whenever cron daemon (or anacron) starts a cron job, it logs the information about the cron job in this file
b) /var/log/maillog /var/log/mail.log – Contains the log information from the mail server that is running on the system. For example, sendmail logs information about all the sent items to this file
b) /var/log/Xorg.x.log – Log messages from the XWindows system
October 22, 2014
This post Seven Habits of Highly Fraudulent Users from Izzy at SiftScience describes patterns culled from 6 million transactions over a three month sample. The “fraud” sample consisted of transactions confirmed fraudulent by customers; “normal” samples consisted of transactions confirmed by customers to be non-fraudulent, as well as a subset of unlabeled transactions.
These patterns are useful to Security Operations Center (SOC) teams who “hunt” for these things.
Habit #1 Fraudsters go hungry
Whereas there is a dip in activity by normal users at lunch time, no such dip is observed in fraudulent transactions. When looking for out-of-ordinary behavior, the absence of any dip during the day might speak to a script which never tires.
Habit #2 Fraudsters are night owls
Analyzing fraudulent transactions as a percentage of all transactions, 3AM was found to be the most fraudulent hour in the day, and night-time in general was a more dangerous time. SOC teams should hunt for “after hours” behavior as a tip-off for bad actors.
Habit #3 Fraudsters are international
Look for traffic originating outside your home country. While these patterns change frequently, as a general rule, international traffic is worth trending and observing.
Habit #4 Fraudsters don multiple identities
Fraudsters tend to make multiple accounts on their laptop or phone to commit fraud. When multiple accounts are associated with the same device, the higher the likelihood of fraud. A user who has 6 accounts on her laptop is 15 times more likely to be fraudulent than the average person. Users with only 1 account however, are less likely to be fraudulent. SOC teams should look for multiple users using the same computer in a given time frame. Even in shared PC situations (e.g, nurses station in a hospital, it is unusual for much more than one user accessing a PC in a given shift.
Habit #5 Fraudsters use well known domains
The top 3 sources of fraud originate from Microsoft sites including outlook.com, Hotmail and live.com. Traffic from/to such sites is worthy of trending and examining.
Habit #6 Fraudsters are boring
A widely recognized predictor of fraud is the number of digits in an email address. The more numbers, the more likely that it’s fraud.
Habit #7 Fraudsters like disposable things
We know that attacks almost always originate from DHCP addresses (which is why dshield.org/block.txt gives out /24 ranges). Its also true that the older an account age, the less likely (in general) its involved in fraud. SOC teams must always look out for account creation.
October 17, 2014
• All systems and applications utilizing the Secure Socket Layer (SSL) 3.0 with cipher-block chaining (CBC) mode ciphers may be vulnerable. However, the POODLE (Padding Oracle On Downgraded Legacy Encryption) attack demonstrates this vulnerability using web browsers and web servers, which is one of the most likely exploitation scenarios.
• EventTracker v7.x is implemented above IIS on the Windows platform and there MAY be vulnerable to POODLE depending on the configuration of IIS..
• ETIDS and ETVAS which are offered as options of the SIEM Simplified service, are based on CentOS v6.5 which uses Apache and may also be vulnerable, depending on the configuration of Apache.
1. Poodle Scan can be used to test if your server is vulnerable
• Below are the links relevant to this vulnerability:
October 16, 2014
Wouldn’t it be nice if you detect when an external threat actor, who’s taken over one of your users’ endpoints, goes on a poaching expedition through all the information that user has access to on your network?
Easier said than done, right? After all, when malware is running on an endpoint anything it does show up as being performed by that user. How high really are your chances of recognizing those events as being different from the user’s normal behavior?
October 15, 2014
EventTracker 7.6 is a complex software application and while there is no easy formula to compute its performance, there are ways to configure and use it so as to get better performance. All data received either real-time or by file ingest (called the Direct Log Archiver) is first indexed and then archived for optimal disk utilization. When performance of a search is cross indexed, compression speed of results depend on the type of search as well as the underlying hardware.
Searches can be categorized as:
Dense – at least one result per thousand (1,000) events
Sparse – at least one result per million (1,000,000) events
Rare – at least one result per billion (1,000,000,000) events
Needle in a haystack – one event in more than a billion events
Based on provided search criteria, EventTracker consults indexing meta-data to determine if and in which archive contains events matching the search terms. As searches go from dense to needle-in-a-haystack, they move from being CPU bound to I/O bound.
Dense searches are CPU bound because matches are found easily and there is sufficient raw data to decompress. For the fastest possible response on default hardware, EventTracker will limit return results to the first (sorted by time with newest on top) 200 results displayed. This setting can of course be defeated but is provided because it satisfies the most common use case.
As the events containing the search term get to one in a hundred thousand (100,000), performance becomes more I/O bound. The reason is there is less and less data but more and more index files have to be consulted.
I/O performance is measured as latency which is a measure of the time delay from when a disk I/O request is created, until the time the disk I/O request is completed by the underlying hardware. Windows perfmon can measure average disk/sec transfer. A rule of thumb is to have this be below 25 millisec for best I/O performance.
This can be realized in various ways:
– Having different drives (spindles) for the OS/progam and archives
– Using faster disk (15K RPM performs better than 7200 RPM disks)
– Using a SAN
In larger installations with multipleVirtual Collection Points (VCP), dedicating a separate disk spindle for each VCP can help.
October 02, 2014
In April 16 of 2013, a sniper took a hundred shots at Pacific Gas and Electric’s (PG&E) Metcalf Electric Power Transformer Station. The utility was able to reroute power on the grid and avert a black out. The whole ordeal took nineteen tension-filled minutes. The event added muscle to the regulatory grip of The North American Electric Reliability Corporation (NERC) – a not-for-profit entity whose mission is to ensure the reliability of the bulk power system in North America. A terrorist attack, domestic or otherwise, could bring the state’s power grid down.
October 01, 2014
An essential part of any IT Security program is to hunt for unusual patterns in sensor (or log) data to uncover attacks. Aside of tools that gather and collate this data (for example SIEM solutions like EventTracker), a smart pair of eyeballs is needed to sift through the data warehouse. In modern parlance, this person is called a data scientist, one who extracts knowledge from data. This requires a deep understanding of the available data and a feel for pattern recognition and visualization.
As Michael Schrage notes in the HBR Blog network “…the opportunities for data-science-enabled efficiencies and innovation are too important to defer or deny. Big organizations can afford — or think they can afford — to throw money at the problem by hiring laid-off Wall Street quants or hiring big-budget analytics boutiques. More frugal and prudent enterprises seem to be taking alternate approaches.”
Starting up a “center of excellence” or addressing a “grand challenge” is not practical for most organizations. Instead, how about an effort to deliver tangible and data-driven benefits in a short time frame?
Interestingly, Schrage notes “Without exception, every team I ran across or worked with hired outside expertise. They knew when a technical challenge and/or statistical technique was beyond the capability…the relationship was less of an RFP box-ticking exercise than a shared space…”
What does any of this have to do with SIEM you ask?
Well for the typical Small/Medium Enterprise [SME] this is a familiar dilemma. Data, data everywhere and not a drop (of intelligence) to drink. Either the “data scientist” is not on the employee roster or does not have time available. How then do you square this circle? Look for outside expertise, as Schrage notes.
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SME’s looking for expertise to leverage the existing mountain of security data within their enterprise can leverage our SIEM Simplified service.
Unicorns don’t exist but that doesn’t mean that do-nothing is a valid option.
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