Optimal control theory with economic applications
Optimal control theory with economic applications
Mobility modeling in wireless networks: categorization, smooth movement, and border effects
ACM SIGMOBILE Mobile Computing and Communications Review
Code red worm propagation modeling and analysis
Proceedings of the 9th ACM conference on Computer and communications security
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
IEEE Security and Privacy
Worm propagation modeling and analysis under dynamic quarantine defense
Proceedings of the 2003 ACM workshop on Rapid malcode
The message delay in mobile ad hoc networks
Performance Evaluation - Performance 2005
Computer Networks: The International Journal of Computer and Telecommunications Networking
StackGuard: automatic adaptive detection and prevention of buffer-overflow attacks
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Can you infect me now?: malware propagation in mobile phone networks
Proceedings of the 2007 ACM workshop on Recurring malcode
Encounter-based worms: Analysis and defense
Ad Hoc Networks
Propagation, detection and containment of mobile malware
Propagation, detection and containment of mobile malware
Towards an optimal malware defense system for DTN with heterogeneous devices
ACM SIGMOBILE Mobile Computing and Communications Review
Multiple mobile data offloading through delay tolerant networks
CHANTS '11 Proceedings of the 6th ACM workshop on Challenged networks
Maximum damage malware attack in mobile wireless networks
IEEE/ACM Transactions on Networking (TON)
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Malware attacks constitute a serious security risk that threatens to slow down the large scale proliferation of wireless applications. As a first step towards thwarting this security threat, we seek to quantify the maximum damage inflicted on the system owing to such outbreaks and identify the most vicious attacks. We represent the propagation of malware in a battery-constrained mobile wireless network by an epidemic model in which the worm can dynamically control the rate at which it kills the infected node and also the transmission range and/or the media scanning rate. At each moment of time, the worm at each node faces the following trade-offs: (i) using larger transmission range and media scanning rate to accelerate its spread at the cost of exhausting the battery and thereby reducing the overall infection propagation rate in the long run or (ii) killing the node to inflict a large cost on the network, however at the expense of loosing the chance of infecting more susceptible nodes at later times. We mathematically formulate the decision problems and utilize Pontryagin Maximum Principle from optimal control theory to quantify the damage that the malware can inflict on the network by deploying optimum decision rules. Next, we establish structural properties of the optimal strategy of the attacker over time. Specifically, we prove that it is optimal for the attacker to defer killing of the infective nodes in the propagation phase for a certain time and then start the slaughter with maximum effort. We also show that in the optimal attack policy, the battery resources are used according to a decreasing function of time, i.e., mostly during the initial phase of the outbreak. Finally, our numerical investigations reveal a framework for identifying intelligent defense strategies that can limit the damage by appropriately selecting network parameters.