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11.7. Heuristic Analysis Using Neural Networks

Several researchers have attempted to use neural networks to detect computer viruses. Neural networks are a sub-field of artificial intelligence26, 27, so the subject is very exciting. Difficult polymorphic EPO viruses such as Zhengxi have been detected successfully using a trained neural network28.

In general, a trained neural network seems to be overkill for detecting a single virus because of the amount of data and computations required. Even a well-optimized neural network scanner can decrease overall scanning performance by about 5%. Thus it is more interesting that neural networks can be applied to heuristic computer virus detection. In practice, IBM researchers have successfully applied neural networks to heuristic detection of boot29 and Win32 viruses30.

One of the key problems of any heuristic is the false positive ratio. If the heuristic is too alarming, people will not use it. IBM researchers demonstrated that single-layer classifiers yield the best results with a voting system. Figure 11.8 shows a typical single-layer classifier with a threshold31.

Figure 11.8. Single-layer classifier with threshold.


Neural networks can easily be overtrained, which is a pitfall of the method. Overtrained networks remember the training set extremely well, but they do not work with new sample sets. In other words, they fail to detect new viruses. To eliminate this problem, multiple neural networks are trained using distinct features. In addition, a voting system is used so that more than one network must agree about a positive detection. In the first experiments, IBM used four neural networks with voting, but it turned out that the best result was achieved when five networks out of eight agreed on a positive.

The basic idea of the training is the selection of n-grams (sequences a couple of bytes long) of the constant part of viruses that indicate an infection. The selection of n-grams for neural network training is the unique feature of IBM's solution. For example, 4-byte sequences can be used to train the network. To train the networks better, a corpus database is used to check whether the n-grams extracted from the constant virus body areas of known computer viruses appear more than a threshold T. If the threshold is exceeded, the n-gram is not used.

The training input vector to the network is constructed using each n-gram with corresponding values. The values have counts for each n-gram feature and the correct output value of 0 or 1 for the neural network. IBM used back-propagation training software to train the networks, and the outputs of each network were saved. Outputs were squashed through a sigmoid output unit, which generated values in the 0.0 to 1.0 range:

sigmoid(x) = 1.0 / (1.0+exp(-x));

The threshold of the sigmoid output was set to 0.65 for 4-byte n-grams.

When the network data is available, it is introduced to a scanner the following way. The neural network heuristic is called whenever an area of the file is scanned. Thus whenever the scanner scans the area of a file, such as a 4KB buffer selected around the entry point of PE files, the heuristics can trigger if enough networks vote positive.

Neural networkbased heuristics depend on a good training set. With more 32-bit Windows viruses in the training set, the automatically trained heuristic produces slightly better results. In practice, neural network heuristics are very effective against closely related variants of viruses that were used in the original training set. They also yield good results against new families of computer viruses that are similar enough to the feature set of known viruses in the training set. It is also important to select n-grams of the virus from the entire virus body. Some antivirus vendors attempted to train neural networks with n-grams selected from emulated instructions of the virus body. However, looping virus code can often generate instruction sets (n-grams) similar to normal programs, yielding an unacceptable false positive ratio.

IBM's neural network engine was released in the Symantec antivirus engine. The neural network engine produced so few false positives that it was used in default scanning (it does not depend on any user-configurable options).

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