Trustworthy Vehicular CommunicationEmploying Multidimensional Diversificationfor Moving-target Defense

Ghourab, Esraa M.; Azab, Mohamed; Eltoweissy, Mohamed

doi:10.13052/jcsm2245-1439.821

Cited by 5 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although MTD has been applied in various forms under different systems or network environments [19]- [21], MTD techniques complying with the given characteristics of vehicular networks have not been well explored, particularly for in-vehicle networks. There have been a few MTD techniques developed for vehicular networks [19], [22], mobile adhoc networks (MANET) [23], and CAN environment [24]. However, no prior SDN-based MTD techniques have been developed to protect the in-vehicle SDN environment, which is studied in our work.…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

DESOLATER: Deep Reinforcement Learning-Based Resource Allocation and Moving Target Defense Deployment Framework

et al. 2021

View full text Add to dashboard Cite

The recent development of autonomous driving technologies has led to the proliferation of research on sensors and electronic equipment inside a vehicle. To deal with security concerns of in-vehicle networks, various deep learning (DL) and reinforcement learning (RL) have been developed to enhance invehicle security. However, the DL/RL agents are vulnerable to adversarial perturbation, where an attacker can perform a manipulation attack to interfere with the agent's operation. In this work, we aim to develop two key mechanisms to build secure in-vehicle networks: (1) RL-based proactive defense mechanism to achieve multiple objectives of minimizing system security vulnerabilities while maximizing service availability; and (2) a resilient RL method that allows an agent to operate in the presence of adversarial disturbances that neutralize the system security. To this end, we propose, DESOLATER (Drl-based rESOurce aLlocation And mTd dEployment fRamework), which is a multi-agent deep reinforcement learning (mDRL)-based network slicing technique that can help determine two key network management decisions: (1) link bandwidth allocation to meet quality-of-service (QoS) requirements; and (2) the frequency of triggering IP shuffling as a proactive defense mechanism not to hinder service availability by maintaining normal system operations. We also introduce an anomaly detection mechanism with a memory-based RL technique to enhance the resiliency of the RL agents in a partially observable environment under the situation that adversarial attackers manipulating observation information. Through extensive simulation experiments, we validate that the proposed robust mDRL algorithm can help the deployed proactive security mechanism achieve both security and network performance improvement in the presence of adversarial attacks.INDEX TERMS Deep reinforcement learning, in-vehicle network, moving target defense, network slicing, partial observability, software-defined networking.

show abstract

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

DESOLATER: Deep Reinforcement Learning-Based Resource Allocation and Moving Target Defense Deployment Framework

et al. 2021

View full text Add to dashboard Cite

show abstract

Spatiotemporal diversification by moving-target defense through benign employment of false-data injection for dynamic, secure cognitive radio network

Ghourab

Azab

Mansour

2019

Journal of Network and Computer Applications

View full text Add to dashboard Cite

Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

Cho

Sharma

Alavizadeh

et al. 2020

IEEE Commun. Surv. Tutorials

222

View full text Add to dashboard Cite

Reactive defense mechanisms, such as intrusion detection systems, have made significant efforts to secure a system or network for the last several decades. However, the nature of reactive security mechanisms has limitations because potential attackers cannot be prevented in advance. We are facing a reality with the proliferation of persistent, advanced, intelligent attacks while defenders are often way behind attackers in taking appropriate actions to thwart potential attackers. The concept of moving target defense (MTD) has emerged as a proactive defense mechanism aiming to prevent attacks. In this work, we conducted a comprehensive, in-depth survey to discuss the following aspects of MTD: key roles, design principles, classifications, common attacks, key methodologies, important algorithms, metrics, evaluation methods, and application domains. We discuss the pros and cons of all aspects of MTD surveyed in this work. Lastly, we highlight insights and lessons learned from this study and suggest future work directions. The aim of this paper is to provide the overall trends of MTD research in terms of critical aspects of defense systems for researchers who seek for developing proactive, adaptive MTD mechanisms.

show abstract

Trustworthy Vehicular CommunicationEmploying Multidimensional Diversificationfor Moving-target Defense

Cited by 5 publications

References 16 publications

DESOLATER: Deep Reinforcement Learning-Based Resource Allocation and Moving Target Defense Deployment Framework

DESOLATER: Deep Reinforcement Learning-Based Resource Allocation and Moving Target Defense Deployment Framework

Spatiotemporal diversification by moving-target defense through benign employment of false-data injection for dynamic, secure cognitive radio network

Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

Contact Info

Product

Resources

About