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Autonomous Strike UAVs for Counterterrorism Missions: Challenges and Preliminary Solutions

Meshari Aljohani, Ravi Mukkamalai, Stephen Olariu

TL;DR

The paper investigates enabling autonomous strike UAVs against high-value terrorists using a private blockchain, smart contracts, and ML. It develops a formal framework to model mission success as a sequence of interdependent tasks, derives analytical expressions for success probabilities, and demonstrates an ML-based decision aid via a Random Forest trained on synthetic training data. The approach integrates tamper-proof data (BBX), secure MC2-UAV communications, and civilian-casualty safeguards encoded in smart contracts, with explicit handling for time synchronization and dynamic mission changes. The work lays groundwork for secure, autonomous aerial strikes while acknowledging open issues in advanced ML techniques, graph-based reasoning, and mission-security concerns. Overall, it presents a convergence of ledger technology, autonomous control, and data-driven decision-making to enable next-generation UAV operation under strict safety constraints.

Abstract

Unmanned Aircraft Vehicles (UAVs) are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and preliminary solutions for successful implementation of an autonomous UAV mission. Specifically, we identify challenges that have to be overcome and propose possible technical solutions for the challenges identified. We also derive analytical expressions for the success probability of an autonomous UAV mission, and describe a machine learning model to train the UAV.

Autonomous Strike UAVs for Counterterrorism Missions: Challenges and Preliminary Solutions

TL;DR

The paper investigates enabling autonomous strike UAVs against high-value terrorists using a private blockchain, smart contracts, and ML. It develops a formal framework to model mission success as a sequence of interdependent tasks, derives analytical expressions for success probabilities, and demonstrates an ML-based decision aid via a Random Forest trained on synthetic training data. The approach integrates tamper-proof data (BBX), secure MC2-UAV communications, and civilian-casualty safeguards encoded in smart contracts, with explicit handling for time synchronization and dynamic mission changes. The work lays groundwork for secure, autonomous aerial strikes while acknowledging open issues in advanced ML techniques, graph-based reasoning, and mission-security concerns. Overall, it presents a convergence of ledger technology, autonomous control, and data-driven decision-making to enable next-generation UAV operation under strict safety constraints.

Abstract

Unmanned Aircraft Vehicles (UAVs) are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and preliminary solutions for successful implementation of an autonomous UAV mission. Specifically, we identify challenges that have to be overcome and propose possible technical solutions for the challenges identified. We also derive analytical expressions for the success probability of an autonomous UAV mission, and describe a machine learning model to train the UAV.
Paper Structure (32 sections, 18 equations, 5 figures, 1 table)

This paper contains 32 sections, 18 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction and motivation
  2. Related Work
  3. Background
  4. Smart Contracts
  5. Real-world Example of Using Smart Contracts
  6. Working scenario -- A high-level description
  7. Challenges
  8. Technical details: addressing the challenges
  9. Accurate UAV localization
  10. Enabling secure communication between the MC2 and the UAV
  11. Tamper-proof collection and storage of flight data
  12. Identifying and confirming the target
  13. Allowing dynamic mission changes
  14. Erasing BBX content upon capture
  15. Evaluating the probability of mission success
  16. ...and 17 more sections

Figures (5)

  • Figure 1: A comprehensive overview of our working scenario.
  • Figure 2: Comprehensive overview of UAV training mission data
  • Figure 3: Confusion matrices for five different classification models. (a) Random Forest model. (b) SVM (LibSVM) model. (c) AdaBoost model. (d) Naive Bayes model. (e) Bagging with Decision Trees model.
  • Figure 4: Receiver Operating Characteristic (ROC) curves for four different classification models.
  • Figure 5: Box Plot of Classification Model Cross-Validation Results. The distribution of accuracy scores from five-fold cross-validation for five different classification models is shown in this figure: AdaBoost, Random Forest, SVM (LibSVM), Naive Bayes, and Decision Tree-Based Bagging.