Abstract-Detecting unknown malicious code (malcode) is a challenging task. Current common solutions, such as anti-virus tools, rely heavily on prior explicit knowledge of specific instances of malcode binary code signatures. During the time between its appearance and an update being sent to anti-virus tools, a new worm can infect many computers and cause significant damage. We present a new host-based intrusion detection approach, based on analyzing the behavior of the computer to detect the presence of unknown malicious code. The new approach consists on classification algorithms that learn from previous known malcode samples which enable the detection of an unknown malcode. We performed several experiments to evaluate our approach, focusing on computer worms being activated on several computer configurations while running several programs in order to simulate background activity. We collected 323 features in order to measure the computer behavior. Four classification algorithms were applied on several feature subsets. The average detection accuracy that we achieved was above 90% and for specific unknown worms even above 99%.

Detecting unknown worms is a challenging task. Extant solutions, such as anti-virus tools, rely mainly on prior explicit knowledge of specific worm signatures. As a result, after the appearance of a new worm on the Web there is a significant delay until an update carrying the worm's signature is distributed to anti-virus tools. During this time interval a new worm can infect many computers and cause significant damage. We propose an innovative technique for detecting the presence of an unknown worm, not necessarily by recognizing specific instances of the worm, but rather based on the computer measurements. We designed an experiment to test the new technique employing several computer configurations and background applications activity. During the experiments 323 computer features were monitored. Four feature selection techniques were used to reduce the amount of features and four classification algorithms were applied on the resulting feature subsets. Our results indicate that using this approach resulted in exceeding 90% mean accuracy, and for specific unknown worms accuracy reached above 99%, using just 20 features while maintaining a low level of false positive rate.

Detecting unknown worms is a challenging task. Extant solutions, such as anti-virus tools, rely mainly on prior explicit knowledge of specific worm signatures. As a result, after the appearance of a new worm on the Web there is a significant delay until an update carrying the worm's signature is distributed to anti-virus tools. During this time interval a new worm can infect many computers and create significant damage. We propose an innovative technique for detecting the presence of an unknown worm, not necessarily by recognizing specific instances of the worm, but rather based on the computer measurements. We designed an experiment to test the new technique employing several computer configurations and background applications activity. During the experiments 323 computer features were monitored. Four feature selection techniques were used to reduce the amount of features and four classification algorithms were applied on the resulting feature subsets. Our results indicate that using this approach resulted, in above 90% average accuracy, and for specific unknown worms accuracy reached above 99%, using just 20 features while maintaining a low level of false positive rate.