Related papers: Adaptive Learning for Moving Target defence: Enhan…

We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the interaction between an attacker and a defender as an optimal stopping game and let attack and defense strategies evolve…

The threats posed by evolving cyberattacks have led to increased research related to software systems that can self-protect. One topic in this domain is Moving Target Defense (MTD), which changes software characteristics in the protected…

This paper studies the deployment of joint moving target defense (MTD) and deception against multi-stage cyberattacks. Given the system equipped with MTD that randomizes between different configurations, we investigate how to allocate a…

Moving Target Defense (MTD) is a proactive security mechanism which changes the attack surface aiming to confuse attackers. Cloud computing leverages MTD techniques to enhance cloud security posture against cyber threats. While many MTD…

Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not…

Deep learning based visual sensing has achieved attractive accuracy but is shown vulnerable to adversarial example attacks. Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model…

Improper configurations in software systems often create vulnerabilities, leaving them open to exploitation. Static architectures exacerbate this issue by allowing misconfigurations to persist, providing adversaries with opportunities to…

To ensure the usefulness of Reinforcement Learning (RL) in real systems, it is crucial to ensure they are robust to noise and adversarial attacks. In adversarial RL, an external attacker has the power to manipulate the victim agent's…

Security is one of the most relevant concerns in cloud computing. With the evolution of cyber-security threats, developing innovative techniques to thwart attacks is of utmost importance. One recent method to improve cloud computing…

The proliferation of UAVs has enabled a wide range of mission-critical applications and is becoming a cornerstone of low-altitude networks, supporting smart cities, emergency response, and more. However, the open wireless environment,…

Moving Target Defense (MTD) is commonly formulated as a repeated security game to mitigate persistent threats. Although the strong Stackelberg equilibrium (SSE) characterizes the defender's optimal strategy in the leader-follower framework,…

Research in adversarial learning follows a cat and mouse game between attackers and defenders where attacks are proposed, they are mitigated by new defenses, and subsequently new attacks are proposed that break earlier defenses, and so on.…

We present a moving target defense strategy to reduce the impact of stealthy sensor attacks on feedback systems. The defender periodically and randomly switches between thresholds from a discrete set to increase the uncertainty for the…

Recently, moving target defence (MTD) has been proposed to thwart false data injection (FDI) attacks in power system state estimation by proactively triggering the distributed flexible AC transmission system (D-FACTS) devices. One of the…

Several moving target defenses (MTDs) to counter adversarial ML attacks have been proposed in recent years. MTDs claim to increase the difficulty for the attacker in conducting attacks by regularly changing certain elements of the defense,…

Machine learning (ML)-based detectors have been shown to be effective in detecting stealthy false data injection attacks (FDIAs) that can bypass conventional bad data detectors (BDDs) in power systems. However, ML models are also vulnerable…

Modern mission-critical systems (MCS) are increasingly softwarized and interconnected. As a result, their complexity increased, and so their vulnerability against cyber-attacks. The current adoption of virtualization and service-oriented…

With the boom of edge intelligence, its vulnerability to adversarial attacks becomes an urgent problem. The so-called adversarial example can fool a deep learning model on the edge node to misclassify. Due to the property of…

We study moving-target defense (MTD) that actively perturbs transmission line reactances to thwart stealthy false data injection (FDI) attacks against state estimation in a power grid. Prior work on this topic has proposed MTD based on…

The results of a learning process depend on the input data. There are cases in which an adversary can strategically tamper with the input data to affect the outcome of the learning process. While some datasets are difficult to attack, many…