nailo2c/deeplog

PyTorch implements "DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning"

deeplog's Issues

Complete implementation?

Hey, thank you for you this implementation. I wonder if this is complete? Did you implement the three models or just the log key model?

unique log keys(log templates)

Nice work!
But I still got some questions

1. As mentioned in the original paper, there are only 40 unique log keys while here are 1143
2. The original paper use "instance_id" to group sessions, but I haven't seen this in your log format

Thanks

parameters based prediction

great work,
Paper contains following features:

1. logKey based prediction of next key (= log key path stacked LSTM based prediction)
2. log Parameter based prediction of next parameter (= log parameters LSTM based prediction)
3. Workflow clustering based creation.

In the end your output is sequences how can we convert it back into log statements instead digits of sequences?
Minor Note: You've implemented only LogKeys path finding what about others?

preprocess output and prediction module

Sir, Thank you for this implementation of Deeplog.
i am using this work for further research in this work.
when executing the existing code,
the preprocess output eventid results as 1145
and once training completed, the prediction module results, precision:0, so divide by zero error.

Kindly request to support.

Svaridasan.P

The result can't be reproduced

I runned the code in colab https://colab.research.google.com/drive/1xWHovxTkysiPei68bTywqB2oCo6JelAU?usp=sharing#scrollTo=f8xH9L33cWL0, twice.
The result changed

In predict.py, I do not understand why "ground_truth" for 'abnormal_has_anomaly' is all 1. Is there no correct or normal data in abnormal data?

ground_truth = [1]*len(abnormal_has_anomaly) + [0]*len(normal_has_anomaly) predict = abnormal_has_anomaly + normal_has_anomaly

Mistake in predict.py

deeplog/example/predict.py

Lines 79 to 82 in 0c3cbb4

	elif p == 0 and t == 1:
	FN += 1
	else:
	TN += 1

Shouldn't it be the other way around?

elif p == 0 and t == 1:
	TN +=1
else:
	FN += 1