Analysis of Persistent Threat Detection Using Behavioral Analytics

Performance Analysis of Persistent Threat Detection Using Behavioral Analytics and Anomaly Detection

implementation plan:
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Step 1: Initially, We collect and load the data from ” DAPT2020 dataset ”

Step 2: Then, we preprocess the data using K Means-SMOTE (K-means clustering with Synthetic minority Oversampling Technique) method for Imbalance handling with Label encoding Modeling.

Step 3: Next, we perform a feature engineering process and then train the data using the ARM-ETC algorithm with Association Rule Mining and Extra Tree Classifier.

Step 4: Next, we analyze complex dynamic behaviors of APT malware using GP (Genetic Programming) algorithm.

Step 5: Next, we implement the IKC-HMM algorithm for detecting the anomalies in APT.

Step 6: Finally, we plot graph for the following metrics:

6.1: No. of Epochs vs. Accuracy (%)
6.2: No. of Epochs vs. Precision (%)
6.3: No. of Epochs vs. Recall (%)
6.4: No. of Epochs vs. F1-Score (%)
6.5: Confusion Matrix

Software Requirements:
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1. Development Tool: Python 3.11.4
2. Operating System: Windows-11(64-bit)

Dataset:
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Dataset link: https: www.kaggle.com/datasets/sowmyamyneni/dapt2020/data

Note :-
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

2) Please note that this implementation plan does not include any further steps after it is put into implementation.

3) If the plan satisfies your requirement, Please confirm with us.

4) Project based on Simulation only, not a real time project.

5) Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.

We perform with an Existing Reference 1: Advanced Persistent Threat Identification with Boosting and Explainable AI

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