Cyber Security Threat Detection Model and AI Techniques

Cyber Security Threat Detection Model Using Artificial Intelligence

Overview of Cybersecurity Challenges
  • Growing complexity in computer connectivity and applications necessitates robust cyber-security measures.
  • Increasing number of cyber threats requires effective threat detection mechanisms such as Intrusion Detection Systems (IDS).
  • Objective: Develop a data-driven IDS model using AI, particularly Machine Learning (ML) techniques.
Proposed System: Binary Grasshopper Optimized Twin Support Vector Machine (BGOTSVM)
  • Feature Ranking: The model ranks security features based on relevance before developing the IDS model, which helps in reducing computational cost and improving predictive performance.
  • Comparison: Experimental trials with other ML techniques (Decision Tree, Random Forest, Random Tree, Artificial Neural Network) validate the effectiveness of BGOTSVM.
  • Outcome: The proposed model outperforms conventional ML techniques in network intrusion detection.
Methods of Cybersecurity
  • Traditional Methods: Involves configuring security policies on firewalls; often inadequate against sophisticated cyber threats.
  • Automated Cyber Security: Utilizes AI and ML techniques to identify and respond to cyber threats.
Key concepts in Cybersecurity
  • Intrusion Detection System (IDS): Monitors network traffic for suspicious activity and issues alerts when potential threats are detected.
  • Cybersecurity Measures: Technologies, professionals, and procedures designed to safeguard against cyber attacks.
Suggested Approach
  1. Data Collection and Analysis: Dataset sourced from Kaggle containing over 25,000 entries of simulated cyber intrusions, providing normalized and anomaly classes.
  2. Data Preparation: Involves feature encoding (Label Encoding used instead of One Hot Encoding) and feature scaling (normalizing variable ranges).
  3. Feature Ranking: Use of the Gini Index for feature importance scoring, measuring how much each attribute contributes to reducing impurity in the model's predictions.
  4. Feature Selection: Selecting relevant features based on importance scores helps in refining the model and improving efficiency.
Twin Support Vector Machine (TSVM)
  • Mathematical Framework: Utilizes non-parallel hyperplanes to separate classes in an optimized manner through Quadratic Programming Problems (QPP).
  • Key equations used in TSVM:
    • Hyperplanes defined by constraints.
    • QPP optimization for classification, dependent on minimizing or maximizing performance metrics derived from feature values.
Grasshopper Optimization Algorithm (GOA)
  • Inspiration from Nature: Mimics grasshopper behavior, emphasizing exploration and exploitation in optimization processes.
  • Algorithm Formulation:
    • Models the position of grasshoppers influenced by social interactions, gravity, and wind factors.
    • Equations govern movement across binary search space adjusting over iterations for accuracy.
Results Evaluation
  • Performance Metrics Considered:
    • Accuracy: Defined as extAccuracy=TP+TNTP+TN+FP+FNext{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} (with True Positives, True Negatives, False Positives, and False Negatives).
    • Precision: extPrecision=TPTP+FPext{Precision} = \frac{TP}{TP + FP}
    • Recall: extRecall=TPTP+FNext{Recall} = \frac{TP}{TP + FN}
    • F-score: Fextscore=2imesextPrecisionimesextRecallextPrecision+extRecallF_{ ext{score}} = \frac{2 imes ext{Precision} imes ext{Recall}}{ ext{Precision} + ext{Recall}}
  • Comparison with conventional methods: BGOTSVM shows superior performance across all metrics, validating its efficacy as a robust IDS.
Future Research Recommendations
  • Enhance datasets related to cybersecurity for better feature representation.
  • Aim to automate security service implementations using the developed model.
References
  • A detailed list of studies cited for foundational and comparative analysis.