The first objective of this project is to implement a repulsion, orientation, attraction rule to mimic swarming. Agents have an estimate of a parameter (temp, pressure) and they apply the consensus protocol to update their own estimates. One of the agents in your swarm is maliciously broadcasting a bad estimate. But UMBC is renowned for cybersecurity, so the second objective of this project is to mitigate the influence of the compromised agent. Note that your swarm has no idea which agent has been compromised.

QUESTION

CMSC 471.3 Artificial Intelligence Project 2 Due: 13-APR-2020 20 points Resilient Swarming Boids rule is a widely-used technique for simulating collective motion. Based on spatial proximity, agents will either repulse, attract, and/or align their headings. But agents are susceptible to their swarming neighbors, and this project includes a compromised agent hiding in the swarm, perfectly implementing Boids rule. The first objective of this project is to implement a repulsion, orientation, attraction rule to mimic swarming. Agents have an estimate of a parameter (temp, pressure) and they apply the consensus protocol to update their own estimates. One of the agents in your swarm is maliciously broadcasting a bad estimate. But UMBC is renowned for cybersecurity, so the second objective of this project is to mitigate the influence of the compromised agent. Note that your swarm has no idea which agent has been compromised.

ANSWER

Resilient Swarming Boids: Mitigating Compromised Agents in Collective Motion

Introduction

In the realm of artificial intelligence, the Resilient Swarming Boids rule has emerged as a prominent technique for simulating collective motion. By leveraging spatial proximity, agents interact with each other through repulsion, orientation, and attraction, resulting in a mesmerizing emulation of swarming behavior. However, in the presence of a compromised agent hidden within the swarm, the integrity of the collective motion is threatened. In this essay, we will explore the objectives of a project that focuses on implementing the swarming behavior and mitigating the influence of the compromised agent. We will also emphasize UMBC’s reputation in cybersecurity, underscoring the importance of robustness in the face of such threats.

Objective 1

 Implementing Repulsion, Orientation, and Attraction Rule

The first objective of this project is to replicate the fundamental behavior of swarming by implementing the repulsion, orientation, and attraction rule. The collective motion of the agents relies on these three crucial components (Heras et al., 2019). Repulsion ensures that agents avoid collisions by actively moving away from each other when they come within a certain distance. Orientation enables agents to align their headings, thereby creating a sense of cohesion within the swarm. Lastly, attraction brings the agents closer together, reinforcing the concept of collective behavior. By combining these rules, the project aims to achieve a realistic and visually captivating simulation of swarming.

Objective 2

Mitigating the Influence of the Compromised Agent

The second objective of this project introduces a challenging scenario where one of the agents in the swarm has been compromised and is broadcasting a malicious estimate. The compromised agent poses a significant threat to the integrity and functionality of the swarm, potentially leading to chaotic or undesirable behavior. The project seeks to address this issue by mitigating the influence of the compromised agent without prior knowledge of its identity.

Mitigation Strategies

To overcome the challenge posed by the compromised agent, several mitigation strategies can be employed:

Consensus Protocol: The agents in the swarm can utilize a consensus protocol to update their own estimates of parameters such as temperature or pressure. This protocol ensures that agents continuously communicate and reach a consensus, averaging out the influence of any outliers, including the compromised agent. By employing consensus, the swarm can collectively adjust its behavior to maintain the integrity of the simulation (Council, 2020).

Robustness Measures: Implementing robustness measures can enhance the swarm’s resilience against compromised agents. Techniques such as redundancy, error detection, and error correction can be incorporated into the communication channels and data exchange between agents. These measures help identify and rectify discrepancies caused by the compromised agent, minimizing its impact on the overall behavior of the swarm.

Anomaly Detection: Anomaly detection algorithms can be utilized to identify abnormal behavior patterns exhibited by the compromised agent. By monitoring the behavior of each agent within the swarm, deviations from expected norms can be flagged as potential indicators of compromise. Once detected, appropriate actions can be taken to neutralize or isolate the compromised agent to minimize its influence.

UMBC’s Expertise in Cybersecurity

The significance of mitigating the influence of compromised agents within swarms cannot be overstated, especially in light of UMBC’s renowned expertise in cybersecurity. With a strong emphasis on research and education in this field, UMBC stands at the forefront of tackling emerging cybersecurity challenges (Golaszewski et al., 2020). Leveraging the knowledge and resources available at UMBC, the project can draw upon cutting-edge techniques, algorithms, and methodologies to fortify the swarm against compromised agents effectively.

Conclusion

The Resilient Swarming Boids rule offers a captivating approach to simulate collective motion. However, the presence of a compromised agent poses a significant threat to the integrity of the swarm’s behavior. This project aims to implement the core swarming rules while simultaneously mitigating the influence of the compromised agent. By utilizing consensus protocols, robustness measures, and anomaly detection techniques, the swarm can adapt and neutralize the impact of the compromised agent. UMBC’s expertise in cybersecurity provides a solid foundation for addressing these challenges, underscoring the importance of resilience in artificial intelligence systems. Through these efforts, the project seeks to achieve a robust and secure swarming simulation, demonstrating the power of AI in the face of potential threats.

References

Council, M. R. (2020, March 26). Report 12: The global impact of COVID-19 and strategies for mitigation and suppression. https://spiral.imperial.ac.uk/handle/10044/1/77735 

Golaszewski, E., Sherman, A. T., Oliva, L., Peterson, P. A., Bailey, M., Bohon, S., Bonyadi, C. J., Borror, C., Coleman, R. D., Flenner, J., Enamorado, E., Eren, M. E., Khan, M. M., Larbi, E., Marshall, K., Morgan, W. H., Mundy, L., Onana, G. N., Orr, S. G., . . . Suess, J. (2020). Project-based learning continues to inspire cybersecurity students. ACM Inroads, 11(2), 46–54. https://doi.org/10.1145/3386363 

Heras, F., Romero-Ferrero, F., Hinz, R. S., & De Polavieja, G. G. (2019). Deep attention networks reveal the rules of collective motion in zebrafish. PLOS Computational Biology, 15(9), e1007354. https://doi.org/10.1371/journal.pcbi.1007354