Codebase for the ICKG 2023 paper: "GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection" (PDF).
- Framework overview
- Few-shot log field extraction
- Graph-based edge anomaly detection
- Python 3.10.8
- PyTorch 1.13.1
- Install PyTorch Geometric
conda install pyg -c pyg - Install dependencies
pip install -r requirements.txt python NER.py \ --gen_data ${DATA} \ --data_name ${DATA_NAME} \ --output_dir ${OUT_DIR} \ --strategy ${STRATEGY} \ --n_shots ${NUM_SHOTS} \ --n_grams ${N_GRAMS} \ --neg_rate ${NEG_RATE} \ --labeling_technique ${LABEL_METHOD} \ --model_name_or_path ${PRETRAINED_MODEL} \ --num_train_epochs ${EPOCHS} \ --do_train \ --do_eval \ --train_batch_size ${TRAIN_BATCH} \ --eval_batch_size ${EVAL_BATCH} \ --gradient_accumulation_steps ${GRAD_CUM_STEPS} \ --ckpt_dir ${CKPT_DIR} \ --seed ${SEED} \ --overwrite_cache python graph_generation.py \ --root ${ROOT} \ --log_file ${DATA} \ --inference_type ${INFERENCE} \ --strategy ${STRATEGY} \ --label_type node \ --pretrained_model_name_or_path ${MODEL_PATH} \ --interval ${INTERVAL} \ --event_template python main.py \ --root ${ROOT} \ --checkpoint_dir ${CKPT} \ --train_batch_size ${TRAIN_BATCH_SIZE} \ --eval_batch_size ${EVAL_BATCH_SIZE} \ --model_type dynamic \ --pretrained_model_path ${MODEL_PATH} \ --lambda_seq ${LAMBDA} \ --classification ${CLASSIFICATION} \ --max_length ${MAX_LENGTH} \ --lr ${LR} \ --layers ${LAYERS} \ --weight_decay ${WEIGHT_DECAY} \ --do_train \ --do_eval \ --multi_granularity \ --global_weight ${GLOBAL_WEIGHT} \ --from_scratch If you find this repository useful in your research, please cite our paper:
@inproceedings{li2023glad, title={Glad: Content-aware dynamic graphs for log anomaly detection}, author={Li, Yufei and Liu, Yanchi and Wang, Haoyu and Chen, Zhengzhang and Cheng, Wei and Chen, Yuncong and Yu, Wenchao and Chen, Haifeng and Liu, Cong}, booktitle={2023 IEEE International Conference on Knowledge Graph (ICKG)}, pages={9--18}, year={2023}, organization={IEEE} }