Artificial Intelligence
Design and Implementation of Entropy Based Artificially Immune Malware Detection System
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Many Malware detection systems these days are using signature based techniques to detect malwares and viruses. The zero day or new infected files are not detected by these signature based Anti Viruses and their signature is generated only after they have done their damage. Hence it becomes very important for a user to constantly update the antivirus software. To overcome these problems, we have proposed a solution based on Artificial Intelligence techniques. So the clients will not require frequent updates and probability of detecting zero day infections will rise abruptly. This project is bas...
Many Malware detection systems these days are using signature based techniques to detect malwares and viruses. The zero day or new infected files are not detected by these signature based Anti Viruses and their signature is generated only after they have done their damage. Hence it becomes very important for a user to constantly update the antivirus software. To overcome these problems, we have proposed a solution based on Artificial Intelligence techniques. So the clients will not require frequent updates and probability of detecting zero day infections will rise abruptly. This project is based on implementing data mining algorithms mainly C4.5 Decision Tree learner. We have generated a dataset on the basis of already known malicious executable files. A C4.5 decision tree is generated based on the generated dataset and the unknown executables are passed through the tree to classify the executable as a malicious or a benign file. The purpose is to get rid of the manual signature based Malware detection systems that require constant updated signatures and making systems artificially immune to unknown and zero day malicious executables.
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