Alan Kuhnle

Rate alteration attacks in smart grid

Smart Grid addresses the problem of existing power grids increasing complexity, growing demand and requirement for greater reliability, through two-way communication and automated residential load control among others. These features also makes the Smart Grid a target for a number of cyber attacks. In the paper, we study the problem of rate alteration attack (RAA) through fabrication of price messages which induces changes in load profiles of individual users and eventually causes major alteration in the load profile of the entire network. Combining with cascading failure, it ends up with a highly damaging attack. We prove that the problem is NP-Complete and provide its inapproximability. We devise two approaches for the problem, former deals with maximizing failure of lines with the given resource and then extending the effect with cascading failure while the later takes cascading potential into account while choosing the lines to fail. To get more insight into the impact of RAA, we also extend our algorithms to maximize number of node failures. Empirical results on both IEEE Bus data and real network help us evaluate our approaches under various settings of grid parameters.

Scalable bicriteria algorithms for the threshold activation problem in online social networks

We consider the Threshold Activation Problem (TAP): given social network G and positive threshold T, find a minimum-size seed set A that can trigger expected activation of at least T. We introduce the first scalable, parallelizable algorithm with performance guarantee for TAP suitable for datasets with millions of nodes and edges; we exploit the bicriteria nature of solutions to TAP to allow the user to control the running time versus accuracy of our algorithm through a parameter α ∊ (0, 1): given η > 0, with probability 1 − η our algorithm returns a solution A with expected activation greater than T — 2αΤ, and the size of the solution A is within factor 1−h 4αΤ + log(T) of the optimal size. The algorithm runs in time O (α⁻² log (n/η) (n + m)|A|), where n, m, refer to the number of nodes, edges in the network. The performance guarantee holds for the general triggering model of internal influence and also incorporates external influence, provided a certain condition is met on the cost-effectivity of seed selection.

Detecting misinformation in online social networks before it is too late

While online social networks provide access to a massive information source, they also enable wide dissemination of false or inaccurate content. Undesirable results caused by misinformation propagation make its timely detection very imperative. An important question is how many monitors are required to detect all misinformation cascades at their early stage. To answer this question, we define a Time Constrained Misinformation Detection (TCMD) problem. As we have proved, there is no polynomial time (1 - ε) ln n-approximation for the TCMD problem. The large number of independent misinformation cascades and heterogeneous delays make misinformation detection more challenging. Our approach includes stochastic programming and an O(ln(1 + n)) approximation algorithm for one-hop detection. This approach can provide a lower bound on the number of required monitors for general detection. Furthermore, we propose a network-compression based solution, whose effectiveness is validated by extensive experimental results.

Vulnerability of clustering under node failure in complex networks

Robustness in response to unexpected events is always desirable for real-world networks. To improve the robustness of any networked system, it is important to analyze vulnerability to external perturbation such as random failures or adversarial attacks occurring to elements of the network. In this paper, we study an emerging problem in assessing the robustness of complex networks: the vulnerability of the clustering of the network to the failure of network elements. Specifically, we identify vertices whose failures will critically damage the network by degrading its clustering, evaluated through the average clustering coefficient. This problem is important because any significant change made to the clustering, resulting from element-wise failures, could degrade network performance such as the ability for information to propagate in a social network. We formulate this vulnerability analysis as an optimization problem, prove its NP-completeness and non-monotonicity, and offer two algorithms to identify the vertices most important to clustering. Finally, we conduct comprehensive experiments in synthesized social networks generated by various well-known models as well as traces of real social networks. The empirical results over other competitive strategies show the efficacy of our proposed algorithms.

Online Algorithms for Optimal Resource Management in Dynamic D2D Communications

Device-to-device (D2D) communications has recently emerged as a promising technology for boosting the capacity of cellular systems. D2D enables direct communication between mobile devices over the cellular band without utilizing infrastructure nodes such as base stations, thereby reducing the load on cellular base stations and increasing network throughput through spatial reuse of radio resources. Hence it is important to optimally allocate these radio resources. Furthermore, since the composition of a cellular macro cell is highly dynamic, it is critical to adaptively update the resource allocation for D2D communications rather than recomputing it from scratch. In this work, we develop the first online algorithm, namely ODSRA, for dynamic resource allocation while maximizing spatial reuse. At the core of the resource allocation problem is the online set multicover problem, for which we present the first deterministic O (log n log m)-competitive online algorithm, where n is the number of elements, and m the number of sets. By simulation, we show the efficacy of ODSRA by analyzing network throughput and other metrics, obtaining a large improvement in running time over offline methods.

Are communities as strong as we think

Many complex systems, from World Wide Web and online social networks to mobile networks, exhibit community structure in which nodes can be grouped into densely interconnected communities. This special structure has been exploited extensively to design better solutions for many operations and applications such as routing in wireless networks, worm containment and interest prediction in social networks. The outcome of these solutions are sensitive to the network structures, which raises an important question: can communities be broken easily in a network? To answer this question, we introduce a density-based problem formulation for analyzing the vulnerability of communities. Our approach includes the NP-completeness and a O(log k) approximation algorithm for solving the problem where k is the number of communities to be broken. Additionally, we analyze the vulnerability of communities in the context of arbitrary community detection algorithms. The empirical results show that communities are vulnerable to edge removal and in some cases the removal of a small fraction of edges can break the community structure.

Pseudo-Separation for Assessment of Structural Vulnerability of a Network

Based upon the idea that network functionality is impaired if two nodes in a network are sufficiently separated in terms of a given metric, we introduce two combinatorial pseudocut problems generalizing the classical min-cut and multi-cut problems. We expect the pseudocut problems will find broad relevance to the study of network reliability. We comprehensively analyze the computational complexity of the pseudocut problems and provide three approximation algorithms for these problems. Motivated by applications in communication networks with strict Quality-of-Service (QoS) requirements, we demonstrate the utility of the pseudocut problems by proposing a targeted vulnerability assessment for the structure of communication networks using QoS metrics; we perform experimental evaluations of our proposed approximation algorithms in this context.

Price Modification Attack and Protection Scheme in Smart Grid

Smart grid addresses the problem of existing power grid’s increasing complexity, growing demand, and requirement for greater reliability through two-way communication and automated residential load control among others. These features also make the smart grid a target for a number of cyber attacks. In this paper, we study the problem of price modification attack (PMA) through fabrication of price messages, which induces changes in load profiles of individual users and eventually causes major alteration in the load profile of the entire network. Combining with cascading failure, it ends up with a highly damaging attack. We prove that the problem is nondeterministic polynomial-time-complete and provide its inapproximability. We devise two approaches for the problem, the former deals with maximizing failure of lines with the given resource and then extending the effect with cascading failure, while the later takes cascading potential into account while choosing the lines to fail. We formulate new protection strategy against PMA and this includes two new algorithms, namely bi-level programming with new branching method and an effective heuristic to improve the running time. Empirical results on both IEEE bus data and real network help us evaluate our approaches under various settings of grid parameters.

Network Resilience and the Length-Bounded Multicut Problem: Reaching the Dynamic Billion-Scale with Guarantees

Motivated by networked systems in which the functionality of the network depends on vertices in the network being within a bounded distance T of each other, we study the length-bounded multicut problem: given a set of pairs, find a minimum-size set of edges whose removal ensures the distance between each pair exceeds T . We introduce the first algorithms for this problem capable of scaling to massive networks with billions of edges and nodes: three highly scalable algorithms with worst-case performance ratios. Furthermore, one of our algorithms is fully dynamic, capable of updating its solution upon incremental vertex / edge additions or removals from the network while maintaining its performance ratio. Finally, we show that unless NP ⊆ BPP, there is no polynomial-time, approximation algorithm with performance ratio better than Omega (T), which matches the ratio of our dynamic algorithm up to a constant factor.

An Approximately Optimal Bot for Non-Submodular Social Reconnaissance

The explosive growth of Online Social Networks in recent years has led to many individuals relying on them to keep up with friends & family. This, in turn, makes them prime targets for malicious actors seeking to collect sensitive, personal data. Prior work has studied the ability of socialbots, i.e. bots which pretend to be humans on OSNs, to collect personal data by befriending real users. However, this prior work has been hampered by the assumption that the likelihood of users accepting friend requests from a bot is non-increasing -- a useful constraint for theoretical purposes but one contradicted by observational data. We address this limitation with a novel curvature based technique, showing that an adaptive greedy bot is approximately optimal within a factor of 1 - 1/e1/δ ~0.165. This theoretical contribution is supported by simulating the infiltration of the bot on OSN topologies. Counter-intuitively, we observe that when the bot is incentivized to befriend friends-of-friends of target users it out-performs a bot that focuses on befriending targets.