README.md

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System call anomaly detection using multi-HMMs

Dataset:

UNM system call dataset

https://www.cs.unm.edu/~immsec/data/synth-sm.html

ADFA-LD: https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-IDS-Datasets/

LSTM-BASED SYSTEM-CALL LANGUAGE MODELING AND ROBUST ENSEMBLE METHOD FOR DESIGNING HOST-BASED INTRUSION DETECTION SYSTEMS https://arxiv.org/pdf/1611.01726.pdf

Idea: char-based system call

https://github.com/karpathy/char-rnn char NN

padding the sequence

https://stackoverflow.com/questions/42002717/how-should-we-pad-text-sequence-in-keras-using-pad-sequences

keras-team/keras#1641

loss functions;

https://keras.io/losses/#categorical_crossentropy

paper: LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems.

Sample code:

http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/

https://github.com/aurotripathy/lstm-anomaly-detect

Run

The train data size is that X_Train (20298, 19, 341) Y_Train (20298, 341)

19283/19283 [==============================] - 147s - loss: 0.0024 - val_loss: 0.0022

another losses

model.compile(loss="categorical_crossentropy", optimizer="rmsprop")

19283/19283 [==============================] - 153s - loss: 10.5728 - val_loss: 8.1006

model.compile(loss="categorical_crossentropy", optimizer="sgd")

19283/19283 [==============================] - 148s - loss: 9.8008 - val_loss: 13.7872

Done Training...

remove linear

19283/19283 [==============================] - 146s - loss: 2.7506 - val_loss: 2.2816

19283/19283 [==============================] - 146s - loss: 2.7523 - acc: 0.2363 - val_loss: 2.1657 - val_acc: 0.3626

https://github.com/fchollet/keras/blob/master/keras/backend/tensorflow_backend.py line 2725

TODO

1. dropout 0.5
2. visualization Slides:http://www.robots.ox.ac.uk/seminars/Extra/2015_07_06_AndrejKarpathy.pdf
3. state-transition probability