Zizhan Zheng — Page 3

arXiv2016

Trust Exploitation and Attention Competition: A Game Theoretical Model

The proliferation of Social Network Sites (SNSs) has greatly reformed the way of information dissemination, but also provided a new venue for hosts with impure motivations to disseminate malicious information. Social trust is the basis for information dissemination in SNSs. Malicious hosts judiciously and dynamically make the balance between maintaining its social trust and selfishly maximizing its malicious gain over a long time-span. Studying the optimal response strategies for each malicious host could assist to design the best system maneuver so as to achieve the targeted level of overall malicious activities. In this paper, we propose an interaction-based social trust model, and formulate the maximization of long-term malicious gains of multiple competing hosts as a non-cooperative differential game. Through rigorous analysis, optimal response strategies are identified and the best system maneuver mechanism is presented. Extensive numerical studies further verify the analytical results.

cs.GT↗

arXiv2016

FlowIntent: Detecting Privacy Leakage from User Intention to Network Traffic Mapping

The exponential growth of mobile devices has raised concerns about sensitive data leakage. In this paper, we make the first attempt to identify suspicious location-related HTTP transmission flows from the user's perspective, by answering the question: Is the transmission user-intended? In contrast to previous network-level detection schemes that mainly rely on a given set of suspicious hostnames, our approach can better adapt to the fast growth of app market and the constantly evolving leakage patterns. On the other hand, compared to existing system-level detection schemes built upon program taint analysis, where all sensitive transmissions as treated as illegal, our approach better meets the user needs and is easier to deploy. In particular, our proof-of-concept implementation (FlowIntent) captures sensitive transmissions missed by TaintDroid, the state-of-the-art dynamic taint analysis system on Android platforms. Evaluation using 1002 location sharing instances collected from more than 20,000 apps shows that our approach achieves about 91% accuracy in detecting illegitimate location transmissions.

cs.CR↗

arXiv2016

The Impact of Stealthy Attacks on Smart Grid Performance: Tradeoffs and Implications

The smart grid is envisioned to significantly enhance the efficiency of energy consumption, by utilizing two-way communication channels between consumers and operators. For example, operators can opportunistically leverage the delay tolerance of energy demands in order to balance the energy load over time, and hence, reduce the total operational cost. This opportunity, however, comes with security threats, as the grid becomes more vulnerable to cyber-attacks. In this paper, we study the impact of such malicious cyber-attacks on the energy efficiency of the grid in a simplified setup. More precisely, we consider a simple model where the energy demands of the smart grid consumers are intercepted and altered by an active attacker before they arrive at the operator, who is equipped with limited intrusion detection capabilities. We formulate the resulting optimization problems faced by the operator and the attacker and propose several scheduling and attack strategies for both parties. Interestingly, our results show that, as opposed to facilitating cost reduction in the smart grid, increasing the delay tolerance of the energy demands potentially allows the attacker to force increased costs on the system. This highlights the need for carefully constructed and robust intrusion detection mechanisms at the operator.

math.OC↗

arXiv2015

Provably Delay Efficient Data Retrieving in Storage Clouds

One key requirement for storage clouds is to be able to retrieve data quickly. Recent system measurements have shown that the data retrieving delay in storage clouds is highly variable, which may result in a long latency tail. One crucial idea to improve the delay performance is to retrieve multiple data copies by using parallel downloading threads. However, how to optimally schedule these downloading threads to minimize the data retrieving delay remains to be an important open problem. In this paper, we develop low-complexity thread scheduling policies for several important classes of data downloading time distributions, and prove that these policies are either delay-optimal or within a constant gap from the optimum delay performance. These theoretical results hold for an arbitrary arrival process of read requests that may contain finite or infinite read requests, and for heterogeneous MDS storage codes that can support diverse storage redundancy and reliability requirements for different data files. Our numerical results show that the delay performance of the proposed policies is significantly better than that of First-Come- First-Served (FCFS) policies considered in prior work.

cs.DC↗

arXiv2014

Ensuring Predictable Contact Opportunity for Scalable Vehicular Internet Access On the Go

With increasing popularity of media enabled hand-helds and their integration with the in-vehicle entertainment systems, the need for high data-rate services for mobile users on the go is evident. This ever-increasing demand of data is constantly surpassing what cellular networks can economically support. Large-scale Wireless LANs (WLANs) can provide such a service, but they are expensive to deploy and maintain. Open WLAN access-points, on the other hand, need no new deployments, but can offer only opportunistic services, lacking any performance guarantees. In contrast, a carefully planned sparse deployment of roadside WiFi provides an economically scalable infrastructure with quality of service assurance to mobile users. In this paper, we present a new metric, called Contact Opportunity, to closely model the quality of data service that a mobile user might experience when driving through the system. We then present efficient deployment algorithms for minimizing the cost for ensuring a required level of contact opportunity. We further extend this concept and the deployment techniques to a more intuitive metric -- the average throughput -- by taking various dynamic elements into account. Simulations over a real road network and experimental results show that our approach achieves significantly better cost vs. throughput tradeoff in both the worst case and average case compared with some commonly used deployment algorithms.

cs.NI↗

arXiv2012

Maximizing System Throughput Using Cooperative Sensing in Multi-Channel Cognitive Radio Networks

In Cognitive Radio Networks (CRNs), unlicensed users are allowed to access the licensed spectrum when it is not currently being used by primary users (PUs). In this paper, we study the throughput maximization problem for a multi-channel CRN where each SU can only sense a limited number of channels. We show that this problem is strongly NP-hard, and propose an approximation algorithm with a factor at least $1/2μ$ where $μ\in [1,2]$ is a system parameter reflecting the sensing capability of SUs across channels and their sensing budgets. This performance guarantee is achieved by exploiting a nice structural property of the objective function and constructing a particular matching. Our numerical results demonstrate the advantage of our algorithm compared with both a random and a greedy sensing assignment algorithm.

cs.NI↗

arXiv2012

On the Efficiency-vs-Security Tradeoff in the Smart Grid

The smart grid is envisioned to significantly enhance the efficiency of energy consumption, by utilizing two-way communication channels between consumers and operators. For example, operators can opportunistically leverage the delay tolerance of energy demands in order to balance the energy load over time, and hence, reduce the total operational cost. This opportunity, however, comes with security threats, as the grid becomes more vulnerable to cyber-attacks. In this paper, we study the impact of such malicious cyber-attacks on the energy efficiency of the grid in a simplified setup. More precisely, we consider a simple model where the energy demands of the smart grid consumers are intercepted and altered by an active attacker before they arrive at the operator, who is equipped with limited intrusion detection capabilities. We formulate the resulting optimization problems faced by the operator and the attacker and propose several scheduling and attack strategies for both parties. Interestingly, our results show that, as opposed to facilitating cost reduction in the smart grid, increasing the delay tolerance of the energy demands potentially allows the attacker to force increased costs on the system. This highlights the need for carefully constructed and robust intrusion detection mechanisms at the operator.

cs.NI↗

arXiv2012

Maximizing Social Welfare in Operator-based Cognitive Radio Networks under Spectrum Uncertainty and Sensing Inaccuracy

In Cognitive Radio Networks (CRNs), secondary users (SUs) are allowed to opportunistically access the unused/under-utilized channels of primary users (PUs). To utilize spectrum resources efficiently, an auction scheme is often applied where an operator serves as an auctioneer and accepts spectrum requests from SUs. Most existing works on spectrum auctions assume that the operator has perfect knowledge of PU activities. In practice, however, it is more likely that the operator only has statistical information of the PU traffic when it is trading a spectrum hole, and it is acquiring more accurate information in real time. In this paper, we distinguish PU channels that are under the control of the operator, where accurate channel states are revealed in real-time, and channels that the operator acquires from PUs out of its control, where a sense-before-use paradigm has to be followed. Considering both spectrum uncertainty and sensing inaccuracy, we study the social welfare maximization problem for serving SUs with various levels of delay tolerance. We first model the problem as a finite horizon Markov decision process when the operator knows all spectrum requests in advance, and propose an optimal dynamic programming based algorithm. We then investigate the case when spectrum requests are submitted online, and propose a greedy algorithm that is 1/2-competitive for homogeneous channels and is comparable to the offline algorithm for more general settings. We further show that the online algorithm together with a payment scheme achieves incentive compatibility for the SUs while guaranteeing a non-negative revenue for the operator.

cs.NI↗

arXiv2011

Maximizing System Throughput by Cooperative Sensing in Cognitive Radio Networks

Cognitive Radio Networks allow unlicensed users to opportunistically access the licensed spectrum without causing disruptive interference to the primary users (PUs). One of the main challenges in CRNs is the ability to detect PU transmissions. Recent works have suggested the use of secondary user (SU) cooperation over individual sensing to improve sensing accuracy. In this paper, we consider a CRN consisting of a single PU and multiple SUs to study the problem of maximizing the total expected system throughput. We propose a Bayesian decision rule based algorithm to solve the problem optimally with a constant time complexity. To prioritize PU transmissions, we re-formulate the throughput maximization problem by adding a constraint on the PU throughput. The constrained optimization problem is shown to be NP-hard and solved via a greedy algorithm with pseudo-polynomial time complexity that achieves strictly greater than 1/2 of the optimal solution. We also investigate the case for which a constraint is put on the sensing time overhead, which limits the number of SUs that can participate in cooperative sensing. We reveal that the system throughput is monotonic over the number of SUs chosen for sensing. We illustrate the efficacy of the performance of our algorithms via a numerical investigation.

cs.NI↗