Andrew Newell

Self-deployment of sensors for maximized coverage in underwater acoustic sensor networks

While self-deployment/reconfiguration of terrestrial wireless sensor networks (WSNs) has been studied extensively, such self-organization has just started to receive attention for underwater acoustic sensor networks (UWSNs). Particularly, self-deployment of sensor nodes in UWSNs is challenging due to certain characteristics of UWSNs such as three dimensional (3-D) environment, restrictions on node movement and longer delays in communication. Given these characteristics, self-deployment of sensor nodes should not only ensure the necessary coverage but also guarantee the connectivity for data transmission as in the case of terrestrial WSNs. In this paper, we propose a distributed node deployment scheme which can increase the initial network coverage in an iterative basis. Assuming that the nodes are initially deployed at the bottom of the water and can only move in vertical direction in 3-D space, the idea is to relocate the nodes at different depths based on a local agreement in order to reduce the sensing overlaps among the neighboring nodes. The nodes continue to adjust their depths until there is no room for improving their coverage. We tune the parameters of the algorithm to also provide connectivity of the network with a surface station. We compared the coverage and connectivity performance of this distributed scheme with distributed/semi-distributed baseline schemes and centralized schemes which can provide optimal coverage/connectivity. We also provide several observations regarding the coverage/connectivity performance and message/travel/time complexity of the proposed approach.

Plagiarizing smartphone applications: attack strategies and defense techniques

In this paper, we show how an attacker can launch malware onto a large number of smartphone users by plagiarizing Android applications and by using elements of social engineering to increase infection rate. Our analysis of a dataset of 158,000 smartphone applications meta-information indicates that 29.4% of the applications are more likely to be plagiarized. We propose three detection schemes that rely on syntactic fingerprinting to detect plagiarized applications under different levels of obfuscation used by the attacker. Our analysis of 7,600 smartphone application binaries shows that our schemes detect all instances of plagiarism from a set of real-world malware incidents with 0.5% false positives and scale to millions of applications using only commodity servers.

Distributed collaborative camera actuation for redundant data elimination in wireless multimedia sensor networks

Given the high cost of processing and communicating the multimedia data in wireless multimedia sensor networks (WMSNs), it is important to reduce possible data redundancy. Therefore, camera sensors should only be actuated when an event is detected within their vicinity. In the meantime, the coverage of the event should not be compromised. In this paper, we propose a low-cost distributed actuation scheme which strives to turn on the least number of cameras to avoid possible redundancy in the multimedia data while still providing the necessary event coverage. The basic idea of this scheme is the collaboration of camera sensors that have heard from scalar sensors about an occurring event to minimize the possible coverage overlaps. This is done by either counting the number of scalar sensors or determining the event boundaries with scalar sensors. Through simulation, we show how the distributed scheme performs in terms of coverage under several centralized and random deployment schemes. We also compare the performance with the case when all the cameras in the vicinity are actuated and when blockages in the region exist.

Increasing network resiliency by optimally assigning diverse variants to routing nodes

Networks with homogeneous routing nodes are constantly at risk as any vulnerability found against a node could be used to compromise all nodes. Introducing diversity among nodes can be used to address this problem. With few variants, the choice of assignment of variants to nodes is critical to the overall network resiliency. We present the Diversity Assignment Problem (DAP), the assignment of variants to nodes in a network, and we show how to compute the optimal solution in medium-size networks. We also present a greedy approximation to DAP that scales well to large networks. Our solution shows that a high level of overall network resiliency can be obtained even from variants that are weak on their own. We provide a variation of our problem that matches the specific communication requirements of applications run over the network (e.g., Paxos and BFT). Also, we analyze the loss in resiliency when optimally assigning variants based on inaccurate information about compromises.

On the practicality of cryptographic defences against pollution attacks in wireless network coding

Numerous practical systems based on network coding have been proposed in recent years demonstrating the wide range of benefits of network coding, such as increased throughput, reliability, and energy efficiency. However, network coding systems are inherently vulnerable to a severe attack, known as packet pollution, which presents a key obstacle to the deployment of such systems. Several cryptographic schemes have been proposed to defend against pollution attacks. We conduct a detailed analysis and an experimental evaluation in a realistic wireless network coding setting of a set of representative cryptographic defenses against pollution attacks. Our analysis identifies severe limitations of asymmetric-based schemes, which impose high communication overhead by placing constraints on the basic network coding parameters and high computation overhead by relying on numerous operations over large fields. Our analysis also shows that symmetric cryptographic schemes, while having better performance than asymmetric cryptographic-based schemes, impose prohibitive overhead in the presence of multiple byzantine adversaries. We further evaluate these schemes by using a set of typical network coding system parameters on a realistic topology. Our experimental evaluation shows that all the schemes we compare induce a throughput degradation that negates the performance benefits of network coding in the presence of multiple colluding adversaries.

Split Null Keys: A null space based defense for pollution attacks in wireless network coding

Recent work in defending against pollution attacks for intra-flow network coding systems proposed a null spaces based algebraic approach which has a smaller computation cost than previous pollution defenses. The approach requires the source to distribute keys periodically, but in order to scale involves forwarder nodes in the creation of new keys and their distribution. As a result the key distribution is secure only in specific network topologies such as those created by large-scale peer to peer systems, and is not secure in wireless networks where such topologies do not exist. We propose Split Null Keys, which splits the keys such that only a small portion of the key is updated periodically. The small updates allow for a scalable key distribution scheme that does not involve forwarder nodes in creating keys and thus does not rely its security on constraints imposed on the network topology. We prove that our scheme is secure despite splitting the key and we show that when compared with existing defenses our scheme imposes lower communication and computation overhead, is resilient to colluding adversaries, and does not require time synchronization.

Node-Capture Resilient Key Establishment in Sensor Networks: Design Space and New Protocols

Key management is required for basic security services of confidentiality, integrity, and data source authentication. Wireless sensor networks (WSNs) are a challenging environment to provide such services due to the resource constraints and the increased likelihood of nodes to be captured. Various key management techniques were proposed that trade off resilience to node capture and overhead in terms of communication and memory. We identify the main factors influencing the design space of key management protocols for sensor networks and describe representative protocols that trade off the number of links established, communication overhead, and resilience to node capture. These trade-offs are due to using direct, pathbased, or multipath-based communication to establish secure links. We propose a new multipath protocol relying on an encoding scheme tailored for WSNs and analyze the effects of key pre-distribution on multipath key establishment. We provide extensive simulations to understand the trade-offs between resilience to node compromise and communication overhead under numerous network scenarios. This comparison highlights the trade-offs between these vastly different key management schemes. For the newer class of key management schemes—multipath based—we quantify experimentally the additional communication overhead required for extra paths and the improvement in resilience from using these paths.

Providing multi-perspective event coverage in wireless multimedia sensor networks

The increasing availability of low-cost battery-operated wireless cameras has motivated the deployment of large-scale Wireless Multimedia Sensor Networks (WMSNs) which can be leveraged for gathering disparate views of events from multiple perspectives. Such multi-perspective coverage not only provides better visual knowledge about the events but also helps reduce occlusions in many critical applications. Different than traditional k-coverage in Wireless Sensor Networks (WSNs), multi-perspective coverage computation considers the orientation of cameras in addition to their locations. In this paper, we first introduce a new metric which can measure multi-perspective coverage for a particular region from a given number of perspectives. Using this metric, we then propose camera placement techniques based on binary integer programming and heuristics to achieve full multi-perspective coverage with the least camera count. Finally, to be used as a baseline, we come up with a formula which can analytically compute the multi-perspective coverage for a given network of randomly placed cameras in a certain region. We evaluated the performance of these camera placement approaches (e.g., integer programming, heuristic and random) in terms of coverage and number of cameras needed under different number of perspectives.

Practical Intrusion-Tolerant Networks

As the Internet becomes an important part of the infrastructure our society depends on, it is crucial to construct networks that are able to work even when part of the network is compromised. This paper presents the first practical intrusion-tolerant network service, targeting high-value applications such as monitoring and control of global clouds and management of critical infrastructure for the power grid. We use an overlay approach to leverage the existing IP infrastructure while providing the required resiliency and timeliness. Our solution overcomes malicious attacks and compromises in both the underlying network infrastructure and in the overlay itself. We deploy and evaluate the intrusion-tolerant overlay implementation on a global cloud spanning East Asia, North America, and Europe, and make it publicly available.

On the Practicality of Integrity Attacks on Document-Level Sentiment Analysis

Sentiment analysis plays an important role in the way companies, organizations, or political campaigns are run, making it an attractive target for attacks. In integrity attacks an attacker influences the data used to train the sentiment analysis classification model in order to decrease its accuracy. Previous work did not consider practical constraints dictated by the characteristics of data generated by a sentiment analysis application and relied on synthetic or pre-processed datasets inspired by spam, intrusion detection, or handwritten digit recognition. We identify and demonstrate integrity attacks against document-level sentiment analysis that take into account such practical constraints. Our attacks, while inspired by existing work, require novel improvements to function in a realistic environment where a victim performs typical steps such as data cleaning, labeling, and feature extraction prior to training the classification model. We demonstrate the effectiveness of the attacks on three datasets -- two Twitter datasets and an Android dataset.