9 citations · 12 across the 7 of their papers we have counts for
7 papers
Optimizing Sensor Allocation against Attackers with Uncertain Intentions: A Worst-Case Regret Minimization Approach
Haoxiang Ma, Shuo Han, Charles A. Kamhoua +1
This paper is concerned with the optimal allocation of detection resources (sensors) to mitigate multi-stage attacks, in the presence of the defender's uncertainty in the attacker'…
RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention
Ran Qin, Haoxiang Ma, Boyang Gao +1
Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive f…
Optimal Decoy Resource Allocation for Proactive Defense in Probabilistic Attack Graphs
Haoxiang Ma, Shuo Han, Nandi Leslie +2
This paper investigates the problem of synthesizing proactive defense systems in which the defender can allocate deceptive targets and modify the cost of actions for the attacker w…
Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes
Haoxiang Ma, Di Huang
In this paper, we focus on the problem of feature learning in the presence of scale imbalance for 6-DoF grasp detection and propose a novel approach to especially address the diffi…
Synthesis of Proactive Sensor Placement In Probabilistic Attack Graphs
Lening Li, Haoxiang Ma, Shuo Han +1
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 di…
Attention-Based Planning with Active Perception
Haoxiang Ma, Jie Fu
Attention control is a key cognitive ability for humans to select information relevant to the current task. This paper develops a computational model of attention and an algorithm…
Dynamic Hypergames for Synthesis of Deceptive Strategies with Temporal Logic Objectives
Lening Li, Haoxiang Ma, Abhishek N. Kulkarni +1
In this paper, we study the use of deception for strategic planning in adversarial environments. We model the interaction between the agent (player 1) and the adversary (player 2)…