I have also used network interdiction modeling to study the vulnerability of wireless networks. Although the field of network interdiction has produced a mature set of modeling and algorithmic tools, these models and algorithms are tailored for “wired” networks, such as supply chains and road networks, and not for wireless ones. Thus, there is a need to extend the concept of interdiction modeling to the wireless domain.

One of my former Ph.D. students has developed a Bender’s decomposition approach for optimally locating jamming devices during a flow jamming attack on a wireless communication network. Another student has studied how to compute the optimal placement of jamming devices within an array of access points as well as how to optimally design such an array that is subject to jamming attacks.

I have also developed a branch-and-cut approach for optimally locating jamming devices given that the wireless network is subject to protocol interference. I am also working on a variation of this approach for studying the version of the problem in which the network is subject to physical interference as well as a study of how directional antennas and limited battery capacity influences vulnerability to jamming. These studies give insight into how to protect wireless networks and how to disable malicious wireless networks. This work is funded by the U.S. Army Engineering Research and Development Center.

Selected Publications

  1. The wireless network jamming problem subject to protocol interference using directional antennas and with battery capacity constraints
    JD Huff, WB Leonard, HR Medal. (2022). International Journal of Critical Infrastructure Protection 39, 100572
    Publication Year: 2022-05-03 21:21:06
    [Link to Article]

    Wireless networks support the operation and maintenance of a variety of critical infrastructure, and keeping these networks functional in the face of adversarial adversity is a paramount concern of infrastructure managers. Supporting these networks’ continued operability requires a robust understanding of wireless-network functionality, including of the ways in which adversaries may seek to jam such networks using recently developed capabilities. However, past work on wireless network jamming subject to protocol interference has focused on using omnidirectional antennas for the target and the jamming attack nodes and has not considered battery-capacity impacts on the success of these jamming efforts. Based on a field test of an ad hoc network performed by Ramanathan et al. (2005) in which the authors found that directional antennas offer an “order-of-magnitude improvement in the capacity and connectivity of an ad hoc network,” the work in this field should be extended to include directional antennas. By incorporating directional antennas, analysts may more realistically model antennas present in everyday use. In addition, battery capacity of the wireless network nodes can impact the effectiveness of a jamming attack and should be considered. By considering battery capacity, researchers are sure to take into account real-world scenarios in which energy limitations might affect actual network performance. The mathematical model discussed in this paper demonstrates the way in which network jamming is affected by directional antennas, battery capacity, and node density to determine how these factors would impact a robust jamming attack. Particularly noteworthy results include the finding that high battery capacity can offer as much as half an order of magnitude of improvement in data transmission over lower battery capacity in certain cases. These results show that the model could be used to aid decision makers in understanding how to design a network that is robust against jamming attacks.

    2.
  2. A mixed-integer programming approach for optimizing flow jamming attacks
    Satish Vadlamani, Hugh Medal, David Schweitzer, Apurba Nandi, Burak Ekşioğlu. Vadlamani, S., Schweitzer, D., Medal, H., Nandi, A., & Eksioglu, B. (2018). A mixed-integer programming approach for locating jamming devices in a flow-jamming attack. Computers & Operations Research, 95, 83-96.
    Publication Year: 2018-08-01 00:00:00
    [PDF]   [Link to Article] The ubiquitous nature of wireless networks makes them increasingly prone to jamming attacks as such attacks become more sophisticated. In this paper, we seek to gain understanding about a particular type of jamming attack: the flow-jamming attack. Toward this end, we provide a mixed-integer programming model for optimizing the location of jamming devices for flow-jamming attacks. An accelerated Benders decomposition approach was used to solve the model. We solved the problem for two realistic networks and 12 randomly generated networks and found that the Benders approach was computationally faster than CPLEX for nearly all the problem instances, particularly for larger problems with 1000 binary variables. The experimental results show that optimally locating jamming devices can increase the impact of flow-jamming attacks. Specifically, as the number of possible locations increases the jammers' efficacy increases as well, but there is a clear point of diminishing returns. Also, adding lower-powered jammers to work in conjunction with higher powered jammers significantly increases overall efficacy in spite of the power difference.
  3. Allocating Protection Resources to Facilities When the Effect of Protection is Uncertain
    Hugh R. Medal, Edward A. Pohl, Manuel D. Rossetti. IIE Transactions 48(3), 220–234.
    Publication Year: 2016-01-10 00:00:00
    [PDF]   [Link to Article] We study a new facility protection problem in which one must allocate scarce protection resources to a set of facilities given that allocating resources to a facility only has a probabilistic effect on the facility’s post-disruption capacity. This study seeks to test three common assumptions made in the literature on modeling infrastructure systems subject to disruptions: 1) perfect protection, e.g., protecting an element makes it fail-proof, 2) binary protection, i.e., an element is either fully protected or unprotected, and 3) binary state, i.e., disrupted elements are fully operational or non-operational. We model this facility protection problem as a two-stage stochastic program with endogenous uncertainty. Because this stochastic program is non-convex we present a greedy algorithm and show that it has a worst-case performance of 0.63. However, empirical results indicate that the average performance is much better. In addition, experimental results indicate that the mean-value version of this model, in which parameters are set to their mean values, performs close to optimal. Results also indicate that the perfect and binary protection assumptions together significantly affect the performance of a model. On the other hand, the binary state assumption was found to have a smaller effect.
  4. The wireless network jamming problem subject to protocol interference
    Hugh R. Medal. Networks 67(2), 111–125
    Publication Year: 2016-01-05 00:00:00
    [PDF]   [Link to Article] We study the following questions related to wireless network security: Which jammer placement configuration during a jamming attack results in the largest degradation of network throughput? and Which network design strategies are most effective in mitigating a jamming attack? Although others have studied similar jammer placement problems, this article is the first to optimize network throughput subject to radio wave interference. We formulate this problem as a bi-level mixed-integer program, and solve it using a cutting plane approach that is able to solve networks with up to 81 transmitters, which is a typical size for studies in wireless network optimization. Experiments with the algorithm also yielded the following insights into wireless network jamming: (1) increasing the number of channels is the best strategy for designing a network that is robust against jamming attacks, and (2) increasing the range of the jammer is the best strategy for the attacker.

Funding

Using high-performance computing to simulate the vulnerability of wireless communication networks to jamming attacks

Agency: Engineer Research and Development Center (U.S. Army)
Researcher: Medal, H.R. (PI)
Amount: $106,310
Abstract:

The main goal of this research is to design new algorithms for use on HPC clusters that will enable stakeholders to rapidly and accurately simulate the effect that jamming attacks will have on the performance of a given wireless network design. This goal will be pursued through the following two objectives: 1) design new algorithms for rapidly and accurately computing the throughput of a wireless network under normal conditions (no jamming) and 2) design new algorithms for rapidly and accurately computing the throughput of a wireless network after a jamming attack. These two objectives will contribute to the public good by 1) providing network designers with tools for simulating the throughput of a hypothetical wireless network design and 2) providing designers with tools for simulating the security of a hypothetical design.

WD62 (HPC-based Sensor Analytics) Task 5: Modeling and Simulation of Wireless Sensor Networks

Agency: United States Army Tank Automotive and Armaments Command
Researcher: Medal, H.R. (PI)
Amount: $254,384
Abstract:

We propose to develop new tools for modeling the performance of sensor networks under a variety of environmental conditions (e.g., dust, sand, etc.). Our tools will model the following performance metrics for a given network: vulnerability, reliability, and power consumption / lifetime. Modeling these metrics will provide engineers with a tool for virtual prototyping that can quickly evaluate the performance of candidate network designs and new technologies. In addition, having models for all of these performance metrics will allow engineers to trade off these metrics when evaluating alternative designs. Our modeling approach will model the sensor network as a directed graph, allowing us to use state-of-the-art graph algorithms to model the routing and scheduling of packets through the network. We will also use geospatial data to represent environmental conditions, which will provide needed fidelity to our model. Our model with include functions for intelligent routing and scheduling, in which packets are routed and scheduled through the network in order to both avoid adverse conditions and maximize throughput.