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Posts
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portfolio
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publications
Optimal Auditing on Smart-Grid Networks
Published in IEEE International Conference on Communications, IEEE ICC 2018, 2018
As the existing power grid becomes increasingly complex, the deployment of Smart Grids–which can significantly improve the stability and efficiency of power infrastructure has seen increasing interest. However, with new technology comes new security concerns. Recent work has shown that fabricating valid but malicious messages on a Smart Grid’s SCADA network can cause widespread power outages. Moreover, the large scale, complexity, and tight constraints of these networks makes deploying in-line detection systems insufficient. A common approach is to instead conduct whole-network audits by temporarily duplicating & forwarding all network traffic to a server dedicated to detecting malicious content. This is usually done by taking advantage of port-mirroring to duplicate the packets received with minimal overhead. However, the operation of these audits sees a number of challenges. For instance, each router used to collect traffic demands physical set-up — thus there is a real cost to needlessly high coverage. In this work, we consider the problem of efficiently finding the minimal set of routers in the SCADA network to use for auditing traffic. This efficiency is critical for enabling timely auditing. Similar versions of this problem have seen study. However, they suffer either from a severe mismatch w.r.t. the problem domain, or from serious scalability concerns. This motivates us to devise a novel (2+𝜃)(ln ∣𝑉 ∣+1) approximation algorithm for this problem with a 2-approximation for the special case of tree networks. We experimentally evaluate our solution and compare it to an optimal IP formulation, finding that it performs near-optimally on small networks and significantly outperforms heuristics in all cases.
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Vulnerability Assessment of Social-Smart Grid: An Algorithmic Approach
Published in IEEE Global Communications Conference, IEEE GLOBECOM 2019, 2019
Utility providers are gradually deploying social networks as a useful addition to the Smart Grid in order to help engage consumers in energy management and efficient usage. Besides its benefits, is there any negative impact to the Smart Grid? In this paper, we investigate the vulnerability of Smart Grid when integrating into social networks, where attackers utilize misinformation propagation in social network to alter electricity customer’s behavior with the goal of causing degradation to power infrastructure. Stand in both perspectives of power facility administrator and adversary, we model the vulnerability assessment of the system under an optimization problem, which enables us to provide theoretical analysis and behavior investigation of the system based on the complexity theory. As solving the problem is challenging, we propose heuristic solutions and show their efficiency on assessing the system’s vulnerability in the presence of misinformation attacks. Therefore, we conclude that misinformation attacks must be considered when developing the security model for Socially-enabled Smart Grid technology and planning mitigation techniques.
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Influenced Maximization at Community Level: A New Challenge with Non-submodularity
Published in IEEE International Conference on Distributed Computing Systems, IEEE ICDCS 2019, 2019
Motivated by various settings, we study a new Influence Maximization problem at the Community level (IMC) which aims at finding k users to maximize the benefit of influenced communities where a community is influenced iff the number of influenced users belong to this community exceeds its predefined threshold. In general, IMC objective function is not submodular nor supermodular, thereby making it very challenging to apply existing greedy solutions of the classic influence maximization (IM) where submodular function is required. Furthermore, the major challenge in the traditional methods for any related IM problem is the inefficiency in estimating the influence spread. IMC brings this difficulty to a higher level when considering influenced communities instead of influencing each individual user. In this paper, we propose different approximation algorithms for IMC: (1) Using Sandwich approach with a tight submodular function to bound the IMC objective function, (2) Activating the top-k influencing nodes found from network sampling. Furthermore, when the activated thresholds of communities are bounded by a constant, we propose an algorithm with performance guarantee tight to the inapproximability of IMC assuming the exponential time hypothesis. Each algorithm has its own strengths in a trade-off between effectiveness and running time, which are illustrated both in theory and comprehensive experimental evaluation.
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Partitioning Attacks on Bitcoin Network: Colliding Space, Time and Logic
Published in IEEE International Conference on Distributed Computing Systems, IEEE ICDCS 2019, 2019
Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted intermediary. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to network dynamics. We verify our observations by simulating attack scenarios and the implications of each attack on the Bitcoin . We conclude with suggested countermeasures.
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Optimal Transactions Placement for Scalable Blockchain Sharding
Published in IEEE International Conference on Distributed Computing Systems, IEEE ICDCS 2019, 2019
A major challenge in blockchain sharding protocols is that more than 95% transactions are cross-shard. Not only those cross-shard transactions degrade the system throughput but also double the confirmation time, and exhaust an already scarce network bandwidth. Are cross-shard transactions imminent for sharding schemes? In this paper, we propose a new sharding paradigm, called OptChain, in which cross-shard transactions are minimized, resulting in almost twice faster confirmation time and throughput. By treating transactions as a stream of nodes in an online graph, OptChain utilizes a lightweight and on-the-fly transaction placement method to group both related and soon-related transactions into the same shards. At the same time, OptChain maintains a temporal balance among shards to guarantee the high parallelism. Our comprehensive and large-scale simulation using Oversim P2P library confirms a significant boost in performance with up to 10 folds reduction in cross-shard transactions, more than twice reduction in confirmation time, and 50% increase in throughput. When combined with Omniledger sharding protocol, OptChain delivers a 6000 transactions per second throughput with 10.5s confirmation time.
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Network Resilience Assessment via QoS Degradation Metrics: An Algorithmic Approach
Published in ACM International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS 2019 , 2019
This paper focuses on network resilience to perturbation of edge weight. Other than connectivity, many network applications nowadays rely upon some measure of network distance between a pair of connected nodes. In these systems, a metric related to network functionality is associated to each edge. A pair of nodes only being functional if the weighted, shortest-path distance between the pair is below a given threshold T. Consequently, a natural question is on which degree the change of edge weights can damage the network functionality? With this motivation, we study a new problem, Quality of Service Degradation: given a set of pairs, find a minimum budget to increase the edge weights which ensures the distance between each pair exceeds T. We introduce four algorithms with theoretical performance guarantees for this problem. Each of them has its own strength in trade-off between effectiveness and running time, which are illustrated both in theory and comprehensive experimental evaluation.
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Streaming k-Submodular Maximization under Noise subject to Size Constraint
Published in Thirty-seventh International Conference on Machine Learning, ICML 2020, 2020
Maximizing on k-submodular functions subject to size constraint has received extensive attention recently. In this paper, we investigate a more realistic scenario of this problem that (1) obtaining exact evaluation of an objective function is impractical, instead, its noisy version is acquired; and (2) algorithms are required to take only one single pass over dataset, producing solutions in a timely manner. We propose two novel streaming algorithms, namely DStream and RStream, with their theoretical performance guarantees. We further demonstrate the efficiency of our algorithms in two applications in Influence Maximization and Sensor Placement, showing that our algorithms can return comparative results to state-of-the-art non-streaming methods while using a much fewer number of queries.
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Auditing on Smart-Grid With Dynamic Traffic Flows: An Algorithmic Approach
Published in IEEE Transactions on Smart Grid, 2020, 2020
The introduction of Smart Grid systems has raised many new security concerns. If an attacker can compromise components of the Smart Grid communication network, they can fabricate malicious messages to interfere with the grid and ultimately cause outages. One method to address this concern is to conduct network audits by logging network traffic into dedicated servers in order to detect malicious messages. This may be done by using switches’ or routers’ ability to duplicate packets they receive with minimal overhead. The question of how many and which switches/routers to select for this task naturally follows. This paper considers the problem of finding minimal set of routers/switches in a Smart Grid communication network to use for auditing traffic. Accordingly, we devise three algorithms: the first one is highly effective with an approximation ratio of (2+θ)(ln n + 1) . The second method is a highly scalable algorithm with a constant performance ratio. And the last one is a dynamic algorithm which can efficiently update its solution in response to changes to critical traffic. We experimentally evaluate our solutions and compare them to an optimal Integer Programming formulation, finding that they perform near-optimally and significantly outperform a simple heuristic in all cases.
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Minimum Robust Multi-Submodular Cover for Fairness
Published in Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, 2020
In this paper, we study a novel problem, Minimum Robust Multi-Submodular Cover for Fairness (MinRF), as follows: given a ground set; m monotone submodular functions f1,…,fm; m thresholds T1,…,Tm and a non-negative integer r, MinRF asks for the smallest set S such that under removal of any set X of size r, fi of S \ X is at least Ti for all i=1 to m. We prove that MinRF is inapproximable within (1−ϵ)ln m; and no algorithm, taking fewer than exponential number of queries in term of r, is able to output a feasible set to MinRF with high certainty. Three bicriteria approximation algorithms with performance guarantees are proposed: one for r=0, one for r=1, and one for general r. We further investigate our algorithms’ performance in two applications of MinRF, Information Propagation for Multiple Groups and Movie Recommendation for Multiple Users. Our algorithms have shown to outperform baseline heuristics in both solution quality and the number of queries in most cases.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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