SecViz | Security Visualization

This seems to be an interesting blog about visualization for security



SecViz | Security Visualization

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TaoSecurity: Fight to Your Strengths

In an interesting blog entry by Richard Bejtlich, TaoSecurity: Fight to Your Strengths, he suggests that sometimes security through obscurity might be suitable. He uses an example where he lets OpenSSH use another port than the default port and thus he gets less number of attacks against sshd. I have added a question at his blog that would be interesting to investigate:

Would it be possible to let a firewall or inline IDS automatically block incoming ssh traffic to the default port and then make ssh communication going out using the default port appear to be using a different port?

The idea would be to automatically make a temporarily obfuscation until it is possible to switch port on the server. In this way it might be possible to not interfere with the running service but still stop automated attacks. Is there anybody out there who can tell me if this would work in reality?

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About: Open-Source Security Tools Abound

An interesting article about open source security tools that also commercial actors should investigate:

Linux/Open Source - Open-Source Security Tools Abound

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Other paper: Remodeling and Simulation of Intrusion Detection Evaluation Dataset

In proceedings of the 2006 International Conference on Security & Managment (SAM'06) I have found this paper: Remodeling and Simulation of Intrusion Detection Evaluation Dataset
In the paper, the authors describe how they simulate network traffic (both innocent and malicious traffic) for testing intrusion detection systems.

They want to improve on the MIT LL dataset that is widely thought to have major drawbacks. The drawbacks make it less useful for testing intrusion detection systems.

The paper's main contribution is to create personalized simulations of users' web browsing behavior while MIT's dataset had only rough distribution of the overall behavior. They model real users' behavior as probabilistic transition diagrams for sessions of browsing that are complemented with daily connection distributions, daily connection cumulative densities and session length distributions. Then browsing traffic is generated from the collection user models either with a one to one mapping from a user model to a simulated user or by generating more simulated users than there are user models

Email traffic is simulated using a public corpus of emails while the MIT dataset used a combination of filtered real emails and automatically generated emails. The emails are clustered into four classes but it is not clear what the classes are used for. It is neither clear if the class in the cluster relates to the classes created from the source and destination addresses mentioned earlier. As well, it is not quite clear how the emails are used in the simulation.

Then they claim to have a larger set of attacks than in the MIT datset, such as DDoS, probes, WWW attacks, RPC, etc.

Finally they show that their simulated web browser behavior more resembles their reference network than the MIT dataset simulation that lacks certain characteristics.

Comment: I would like to be able to use the generated traffic as basis for my research - too bad there is no link to a public data set.

"Signatures are usually based on vulnerabilities rather than exploits"

This is interesting, when I started to read about signature-based intrusion detection systems, I thought that signatures were created by using patterns from the exploit. However, as I noticed in a previous entry and learned from the post below (that I found via TaoSecurity), this is not the case.

Errata Security: ANI 0day vs. intrusion detection providers

signatures are usually based on vulnerabilities rather than exploits

This means that learning systems, like Polygraph, that generates signatures from exploits are not automating the signature generation properly. Though, they are able to block worms exploiting unknown vulnerabilities.