systemcallanomalydetectionlstm's Introduction

System call-based anomaly detection

System call anomaly detection with LSTM (reproduction?)

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/

References

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