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Abluva's Pattern Attention Model leads Insider Threat Detection

Data Set Details

Because using real, even de-identified, corporate data raises a variety of legal, ethical, and business issues, the DARPA Anomaly Detection at Multiple Scales (ADAMS) program turned to proxy data sets and synthetic data, with the goal to generate data to simulate the aggregated collection of logs from host-based sensors distributed across all the computer workstations within a large business or government organization over a 500 day period.

Model	Accuracy (%)	F1 Score (%)
CNN	98.65	91.48
LSTM	98.22	89.9
GRU-CNN	97.39	55.6
TD-CNN-LSTM	99.6	97.54
TD-CNN-Attention	99.95	99.71

PaPS Ensemble leads the Security Intrusion Detection Models
Top Performance in Zero-Day Intrusion Detection tasks

Dataset	Description	Model	Accuracy (%)	F1 Score (%)
BODMAS	Blue Hexagon Open Dataset for Malware AnalysiS - BODMAS dataset contains 57,293 malware samples and 77,142 benign samples collected from August 2019 to September 2020, with carefully curated family information (581 families).	Random Forest	24.68	13.5
		XGBoost	69.41	74.6
		LightGBM	68.34	73.47
		MLP	63.78	68.47
		PaPS Ensemble	85.04	89.06
UNSW NB-15	This data set has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic.	Random Forest	96.05	86.13
		XGBoost	87.29	85.38
		LightGBM	95.98	85.84
		MLP	92.12	69.63
		DNN 5 layers	76.1	79.6
		PaPS Ensemble	98.39	95.23
CIC IDS-2017	Dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs).	Random Forest	93.44	91.19
		XGBoost	64.28	59.08
		LightGBM	94.35	92.82
		MLP	88.65	87.75
		Improved AdaBoost	81.83	90.01
		XAI Approach	94	90
		DNN 5 layers	93.1	89.4
		PaPS Ensemble	92.77	92.99
UNR IDD	University of Nevada - Reno Intrusion Detection Dataset utilizes network port statistics for fine-grained analysis of intrusions.	Random Forest	97.53	97.79
		XGBoost	93.98	95.15
		LightGBM	97.53	97.79
		MLP	92.69	94.26
		Bagging Classifier	-	94
		PaPS Ensemble	99.73	99.73

Dataset	Model	Accuracy (%)	F1 Score (%)
BODMAS Blue Hexagon Open Dataset for Malware AnalysiS - BODMAS dataset contains 57,293 malware samples and 77,142 benign samples collected from August 2019 to September 2020, with carefully curated family information (581 families).	Random Forest	24.68	13.5
	XGBoost	69.41	74.6
	LightGBM	68.34	73.47
	MLP	63.78	68.47
	PaPS Ensemble	85.04	89.06
UNSW NB-15 This data set has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic.	Random Forest	96.05	86.13
	XGBoost	87.29	85.38
	LightGBM	95.98	85.84
	MLP	92.12	69.63
	DNN 5 layers	76.1	79.6
	PaPS Ensemble	98.39	95.23
CIC IDS-2017 Dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs).	Random Forest	93.44	91.19
	XGBoost	64.28	59.08
	LightGBM	94.35	92.82
	MLP	88.65	87.75
	Improved AdaBoost	81.83	90.01
	XAI Approach	94	90
	DNN 5 layers	93.1	89.4
	PaPS Ensemble	92.77	92.99
UNR IDD University of Nevada - Reno Intrusion Detection Dataset utilizes network port statistics for fine-grained analysis of intrusions.	Random Forest	97.53	97.79
	XGBoost	93.98	95.15
	LightGBM	97.53	97.79
	MLP	92.69	94.26
	Bagging Classifier	-	94
	PaPS Ensemble	99.73	99.73

Ranked #1 on Leaderboard on 5 AI Tasks

State of the Art Model with F1 above 99% for Sherlock Dataset