The advancement in Internet of Things (IoT) technology has transformed our daily lifestyle. Particularly, voice assistants such as Amazon's Alexa and Google Assistant are commonly deployed in households. These voice assistants enable users to interact with other devices in a smart home ecosystem. In this paper, we focus on two security issues that arise with the use of smart speakers, and present network flow fingerprinting methods to mitigate their impact. First, we concentrate on the misactivation of smart speakers in which spoken words unintentionally activate the device. This may lead to private user conversations being recorded and sent to the cloud without the user even noticing. To prevent such misactivation, we explore locality-sensitive hash-based machine learning approaches. Our evaluation results with the network traffic of four different smart speakers show that the proposed approach can achieve an area under the curve (i. e., AUC) of 93% to 99%. Secondly, we explore whether the voice commands of the device owner can be distinguished from other individuals based on the generated network traffic fingerprint without any analysis of the actual sound wave. Evaluation results with five different user voices show that we can achieve an average AUC of 72% to 81% by ensembling multiple machine learning models.

Engineered systems get smarter with computing capabilities, particularly through a multitude of Internet-of-Things (IoT) devices. IoT devices, however, are prone to be compromised as they are often resource limited and optimized for a certain task. They lack computing power for security software hence, they have become a major target of malicious activities. In order to secure a network, administrators may isolate vulnerable devices and limit traffic to a device based on its communication needs. In this article, we introduce a novel approach to identify an IoT device based on the locality-sensitive hash of its traffic flow. Different from previous studies that employ machine learning, the proposed approach does not require feature extraction from the data, operates in all states of the device (e.g., setup, idle, and active), and does not require to retrain the model when a new device type/version is introduced. The evaluation results on different data sets show that our approach achieves precision and recall above 90% on average and performs equally well compared to the state-of-the-art machine learning-based methods.

Edge computing has become a recent approach to bring computing resources closer to the end-user. While offline processing and aggregate data reside in the cloud, edge computing is promoted for latency-critical and bandwidth-hungry tasks. In this direction, it is crucial to quantify the expected latency reduction when edge servers are preferred over cloud locations. In this paper, we performed an extensive measurement to assess the latency characteristics of end-users with respect to the edge servers and cloud data centers. We also evaluated the impact of capacity limitations of edge servers on the latency under various user workloads. We measured latency from 8,456 end-users to 6,341 Akamai edge servers and 69 cloud locations. Measurements of latencies show that while 58% of end-users can reach a nearby edge server in less than 10 ms, only 29% of end-users obtain a similar latency from a nearby cloud location. Additionally, we observe that the latency distribution of end-users to edge servers follows a power-law distribution, which emphasizes the need for non-uniform server deployment and load balancing by an edge provider.

Widespread adoption of Internet of Things (IoT) devices increased the variety of devices connected to a network. These devices have become a primary target of cyber-attacks as they introduce unmanaged vulnerabilities into the network. On the other hand, most of the IoT devices have a limited set of functionalities. Thus, it is possible to formulate the expected traffic pattern of the device and employ the network traffic to detect anomalous activities. Existing IoT traffic anomaly detection systems require tuning the parameters of machine learning algorithms used for the traffic classification. In this paper, we introduce a novel approach to detect the anomalous IoT network traffic based on the locality-sensitive hash of the traffic flow. The proposed Locality Sensitive Anomaly Detection (LSAD) approach does not require feature extraction from the data. Evaluation on a dataset with fifteen attacks shows that it can detect anomalous flows with an accuracy above 97% and achieve the same or better performance than machine learning approaches.

Systems get smarter with computing capabilities, especially in the form of Internet of Things (IoT) devices. IoT devices are often resource-limited as they are optimized for a certain task. Hence, they are prone to be compromised and have become a target of malicious activities. Since IoT devices lack computing power for security software, network administrators need to isolate such devices and limit traffic to the device based on their communication needs. To this end, network administrators need to identify IoT devices when they join a network and detect anomalous traffic when they are compromised. In this paper, we introduce a novel approach to identify the IoT device based on the Nilsimsa hash of its traffic flow. Different from previous studies, the proposed approach does not require feature extraction from the data. In our evaluations, our approach has an average precision and recall of 93% and 90%, respectively.

Scientific applications generate large volumes of data that often needs to be moved between geographically distributed sites for collaboration or backup which has led to a significant increase in data transfer rates. As an increasing number of scientific applications are becoming sensitive to silent data corruption, end-to-end integrity verification has been proposed. It minimizes the likelihood of silent data corruption by comparing checksum of files at the source and the destination using secure hash algorithms such as MD5 and SHA1. In this paper, we investigate the robustness of existing end-to-end integrity verification approaches against silent data corruption and propose a Robust Integrity Verification Algorithm (RIVA) to enhance data integrity. Extensive experiments show that unlike existing solutions, RIVA is able to detect silent disk corruptions by invalidating file contents in page cache and reading them directly from disk. Since RIVA clears page cache and reads file contents directly from the disk, it incurs delay to execution time. However, by running transfer, cache invalidation, and checksum operations concurrently, RIVA is able to keep its overhead below 15% in most cases compared to the state-of-the-art solutions in exchange of increasing the robustness to silent data corruption. We also implemented dynamic transfer and checksum parallelism to overcome performance bottlenecks and observed more than 5x increase in RIVA's speed.

IoT devices have been gaining popularity and become integral to our daily life. These devices are prone to be compromised as well as any computing system, but lack computing capabilities for cybersecurity software. An important measure for protecting attacks on IoT devices is through isolation of such devices by restriction of communications to the device from firewall/gateway. To this end identification of the IoT device is valuable for network administration and security. In this paper, we introduce a system for automated classification of device characteristics, called System IDentifier (SysID), based on their network traffic. SysID uses any single packet that is originated from the device to detect its kind. We use genetic algorithm (GA) to determine relevant features in different protocol headers and then deploy various machine learning (ML) algorithms (i.e., DecisionTable, J48 Decision Trees, OneR, and PART) to classify host device types by analyzing features selected by GA. GA helps reduce classification complexity and increases its accuracy by eliminating noisy features from the data. SysID allows the ability to have a completely automated way of classifying IoT devices using their TCP/IP packets without expert input for classification. In an experimental study with 23 IoT devices, SysID identified the device type from a single packet with over 95% accuracy.

MOTIVATION: An important goal of cancer genomics initiatives is to provide the research community with the resources for the unbiased query of cancer mechanisms. Several excellent web platforms have been developed to enable the visual analyses of molecular alterations in cancers from these datasets. However, there are few tools to allow the researchers to mine these resources for mechanisms of cancer processes and their functional interactions in an intuitive unbiased manner. RESULTS: To address this need, we developed SEMA, a web platform for building and testing of models of cancer mechanisms from large multidimensional cancer genomics datasets. Unlike the existing tools for the analyses and query of these resources, SEMA is explicitly designed to enable exploratory and confirmatory analyses of complex cancer mechanisms through a suite of intuitive visual and statistical functionalities. Here, we present a case study of the functional mechanisms of TP53-mediated tumor suppression in various cancers, using SEMA, and identify its role in the regulation of cell cycle progression, DNA repair and signal transduction in different cancers.SEMA is a first-in-its-class web application designed to allow visual data mining and hypothesis testing from the multidimensional cancer datasets. The web application, an extensive tutorial and several video screencasts with case studies are freely available for academic use at https://sema.research.cchmc.org/. AVAILABILITY AND IMPLEMENTATION: SEMA is freely available at https://sema.research.cchmc.org. The web site also contains a detailed Tutorial (also in Supplementary Information), and a link to the YouTube channel for video screencasts of analyses, including the analyses presented here. The Shiny and JavaScript source codes have been deposited to GitHub: https://github.com/msolmazm/sema. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Internet Service Providers (ISPs) have different growth or decline patterns in terms of topological and economic characteristics. While some ISPs may have a significant correlation between their topological and business properties, others may not show such strong techno-economic dependence. In this paper, we examine the relationship between the network topology characteristics of the ISPs and their stock market values. We analyze data collected from two different datasets of inter-ISP relationships of various types, i.e., customer, provider and peer. Then, we correlate these topological relationships against the ISPs performance on the stock market. In particular, we examine the correlation between the stock market values as well as revenue of each major ISPs operating in the United States to their degree (i.e., the number of connections they have to other Autonomous Systems) and customer cone size (i.e., the number of customers they are serving).

Terrorist organizations have social networks that enable them to recruit and operate around the world. This paper presents a novel computational framework for derivation of optimal destabilization strategies against dynamic social networks of terrorists. We develop a game-theoretic model to capture the distributed and complex dynamics of terrorist organizations, and introduce a technique for estimation of such dynamics from incomplete snapshots of target networks. Furthermore, we propose a mechanism for devising the optimal sequence of actions that drive the internal dynamics of targeted organizations towards an arbitrary state of instability. The performance of this framework is evaluated on a model of the Al-Qaeda network in 2001, verifying the efficacy of our proposals for counter-terrorism applications.

Tremendous increase in the usage of mobile devices and interconnected social networking applications have brought privacy concerns for the end users. The privacy concerns in Online Social Networks (OSN) can be addressed with decentralized architectures where users directly interact with their peers. The distributed OSN platforms remove central authorities from the networked systems as users share their content only with intended peers through mobile devices. In a decentralized architecture, efficient sharing and timely access of objects play a vital role especially in interactive systems. In this study, we analyze content dissemination in decentralized OSNs. Measuring interactions in friend circle of 16 users red, which contain a total of 3,539 users, in detail, we analyze efficient approaches to locate online friends and exchange data efficiently and overhead with cloud-backed decentralized OSNs.

People with visual impairments have been surrounded with myths and misconceptions that have made it challenging for them to live as productive members of the society and have partly contributed in making the majority live in substandard living conditions. To shift the public's focus from being on the disability to be on the abilities of the visually impaired, non-profit organizations and government agencies have conducted a series of campaigns to spread awareness about visual impairments. Social media platforms, such as Twitter, has become the primary channel to diffuse information to the public. The goal of this paper is to analyze the use of Twitter as a social media platform to spread information during visual impairments awareness campaigns. We focused our analysis on five key concerns: (i) the characteristics of active users during the event, (ii) the major players in information dissemination, (iii) the tweets' common topics (iv) the reachability of information, and (v) the temporal tweeting behavior. We report the results of the campaigns along with the design of an effective communication strategy of a campaign.

Operating System (OS) detection significantly impacts network management and security. Current OS classification systems used by administrators use human-expert generated network signatures for classification. In this study, we investigate an automated approach for classifying host OS by analyzing the network packets generated by them without relying on human experts. While earlier approaches look for certain packets such as SYN packets, our approach is able to use any TCP/IP packet to determine the host systems' OS. We use genetic algorithms for feature subset selection in three machine learning algorithms (i.e., OneR, Random Forest and Decision Trees) to classify host OS by analyzing network packets. With the help of feature subset selection and machine learning, we can automatically detect the difference in network behaviors of OSs and also adapt to new OSs. Results show that the genetic algorithm significantly reduces the number of packet features to be analyzed while increasing the classification performance.

Social networking has influenced billions of users to interact and share information online with friends and family. As online interactions have become the norm and online platforms amassed user data, privacy concerns for the user data has increased. Decentralized architectures can provide better privacy to the users by removing the central authority but have performance issues in dissemination of the content. In this study, we present the Personal Online Social Network (POSN) app that implements a cloud-backed peer-to-peer decentralized OSN using mobile devices. In POSN, each user utilizes a storage cloud to store and distribute encrypted content to his/her friends. Direct key management allows the user to have fine-grained access control of the shared content, and protects the data from being accessed by third parties. The POSN app is available at https://github.com/posn/POSN-app.

Comprehensive analyses that aim to better understand the topology of real world networks have been an important research challenge. Internet topology measurement studies provide samples of the underlying network at various levels. Although router-level Internet topology measurement systems target low level Internet infrastructure, they primarily focus on the Layer-3 connectivity and ignore the underlying multi-access links. In this paper, in addition to the thoroughly studied degree distribution, we analyze the subnet and interface distributions of major Internet topology datasets. We also investigate the impact of the higher granularity modeling at link level versus router level modeling. Our analysis establishes a foundation for the Layer-2 Internet topology generation and introduces the link layer characteristics into the network modeling.

The air transportation industry has a great impact on the economy. In this paper, we analyze the air transportation network in the U.S. to better understand its characteristics. For this, we measure several complex network features including average shortest path, degree distribution, assortative mixing, clustering, betweenness centrality, and resiliency. We also analyzed changes over the previous two decades and compared the networks with earlier air transportation network studies. In our analysis, we did not observe considerable changes in the analyzed complex network features of the U.S. air transportation over the past two decade. Moreover, the U.S. air transportation network exhibits small world characteristics and has an assortative mixing by the degree similar to the earlier study on the world-wide air transportation network. However, the US air transportation network exhibits a partial power law degree distribution unlike the complete power law reported in earlier air transportation networks.

Funding from the government agencies has been the driving force for the research and educational institutions particularly in the United States. The government funds billions of dollars every year to lead research initiatives that will shape the future. In this paper, we analyze the funds distributed by the National Science Foundation (NSF), a major source of research funding in the States, to understand the collaboration patterns among researchers and institutions. Using complex network analysis, we interpret the collaboration patterns at researcher, institution and state levels by constructing the corresponding networks based on the number of grants collaborated. We further analyze the directorates to identify the differences in collaboration trends between disciplines.

The video game market is a complex system where the relationships between game titles and publishers is convoluted. While some publishers are responsible for a large number of titles others seem to be less successful. In an attempt to understand the characteristics and evolution of the video game industry, we analyze the market using social network analysis. For this purpose, we build a heterogenous network of the video game industry at different levels. Moreover, to interpret the behavior of the generations of game consoles, we build these networks for each console, i.e., PS3, Xbox 360 and Wii, and analyze the three generations they went through. We perform a comparative temporal analysis of the evolution of the industry by investigating 14 video game consoles as five generations. Finally, we try to provide insights and future directions regarding the video game market.

An important issues in cloud provisioned multi-tenant healthcare systems is the access control, which focuses on the protection of information against unauthorized access. As different tenants including hospitals, clinics, insurance companies, and pharmacies access the system, sensitive information should be provided only to authorized users and tenants. In this paper, we analyze the requirements of access control for healthcare multi-tenant cloud systems and propose to adapt Task-Role Based Access Control with constraints such as least privilege, separation of duty, delegation of tasks, and spatial and temporal access.

This paper focuses on deployment of smart meters in the power distribution systems to enhance the operation infrastructure. An important challenge in establishing a communication paradigm between the utilities and the customers is that customers are susceptible to privacy concerns. In this paper, we present a model to ensure the privacy and integrity of communicating parties within the smart grid by using smart meters as a gateway between intra- and inter-network communications. In particular, we utilize the smart meter as a firewall to manage incoming and outgoing traffic and mediate household devices based on the instructions from the electric utility. Moreover, third parties are introduced in our model such as service providers so that they can monitor and manage the contracted customers by using the existing communication infrastructure.

Social networking sites employ recommendation systems in contribution to providing better user experiences. The complexity in developing recommendation systems is largely due to the heterogeneous nature of social networks. This paper presents an approach to friend recommendation systems by using complex network theory, cognitive theory and a Pareto>optimal genetic algorithm in a two>step approach to provide quality, friend recommendations while simultaneously determining an individual's perception of friendship. Our research emphasizes that by combining network topology and genetic algorithms, better recommendations can be achieved compared to each individual counterpart. We test our approach on 1,200 Facebook users in which we observe the combined method to outper> form purely social or purely network>based approaches. Our preliminary results represent strong potential for developing link recommendation systems using this combined approach of personal interests and the underlying network.

It can be argued that, BGP, de-facto inter-domain routing protocol, provides fairly stable routes. Path stability is a desired product of that limited level of deterministic performance of BGP. To attain this performance level, BGP relies on keeping up-to-date (aggregated) global information by incurring the cost of control traffic and delayed convergence. In this work, we developed an Opportunistic Path-Vector (OPVR) protocol which provides nice trade-offs between path stability, routing scalability and path quality to enable flexible inter-domain level routing services. Our approach is to redefine routing problem as a set of smaller scale problems which can be solved locally without requiring a global coordination but local communication. We also provide guidelines on how to solve these localized routing problems efficiently. Our analysis show that our method provide a good compromise between scalability and opportunity through smartly randomized (non-deterministic) choices. Our experiments with OPVRs on Internet AS-level topology show us that OPVRs can provide non-deterministic, scalable path exploration mechanisms with reasonable control traffic cost.

Network management and automated configuration of large-scale networks is one of the crucial issues for Internet Service Providers (ISPs). Since wrong configurations might lead to an enormous amount of customer traffic to be lost, highly experienced network administrators are typically the ones who are trusted for the management and configuration of a running ISP network. We frame the management and experimentation of a network as a “game” for training network administrators without having to risk the network operation. The interactive environment treats the trainee network administrators as players of a game and tests them with various network failures or dynamics. To prototype the concept of “network management as a game”, we modified NS-2 to establish an interactive simulation engine and connected the modified engine to a graphical user interface for traffic animation and interactivity with the player. We present initial results from our game applied to a small set of players.

Approximating `real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission.

Automated control and management of large-scale physical systems is a challenging problem in a wide variety of applications including: power grids, transportation networks, and telecommunication networks. Such systems require (i) data collection, (ii) secure data transfer to processing centers, (iii) data processing, and (iv) timely decision making and control actions. These tasks are complicated by the vast amount of data, the distributed sources of data, and the need for efficient data communication. In addition, large physical systems are often subdivided into separately owned subsystems. This multi-owner structure imposes physical, economic, market, and political constraints on the data transfer. These divisions make systems vulnerable to potential coordinated attacks. Defending against such attacks requires the infrastructures to be more automated and self-healing. Motivated by the challenge of a more efficient, secure and robust power grid, which is less vulnerable to blackouts due to cascaded events, this paper discusses some of the fundamental problems in designing future cyber-physical systems.

Internet measurement studies utilize traceroute to collect path traces from the Internet. A router that does not respond to a traceroute query is referred to as an anonymous router and is represented by a '*' in the traceroute output. Anonymous router resolution refers to the task of identifying the occurrences of '*'s that belong to the same router in the underlying network. This task is an important step in building traceroute-based topology maps and obtaining an optimum solution is shown to be NP-complete. In this paper, we use a novel technique from graph data mining field to build an efficient solution. The results of our experiments on both synthetic and genuine topologies show a significant improvement in accuracy and effectiveness over the existing approaches.

Internet measurement studies require availability of representative topology maps. Depending on the map resolution (e.g., autonomous system level or router level), the procedure of collecting and processing an Internet topology map involves different tasks. In this paper, we present a new task, i.e., subnet inference, to advance the current state of the art in topology collection studies. Utilizing a technique to infer the subnet relations among the routers in the resulting topology map, we identify IP addresses that are connected over the same connection medium. We believe that the successful inclusion of subnet relations among the routers will yield topology maps that are closer, at the network layer, to the sampled segments of the Internet in router level topology measurement studies.

IP alias resolution is an important step in generating sample Internet topologies from collected path traces. Inaccuracies in IP alias resolution may significantly affect the characteristics of the resulting sample topologies. This in turn affects the accuracy of measurement results obtained using such topologies. Existing tools for alias resolution use an active probing approach. They induce significant traffic overhead into the network and critically depend on the participation of the routers. Recent studies have reported on the limited effectiveness of these approaches [1], [2]. In this paper, we present a novel approach, called Analytical Alias Resolver (AAR), for IP alias resolution. Given a set of path traces, AAR utilizes the common IP address assignment scheme to infer IP aliases from the collected path traces. AAR does not incur traffic overhead due to active probing for alias resolution. Our experimental evaluations on a set of collected Internet path traces show that, compared to existing approaches, AAR can detect significantly more number of IP aliases.

Summary form only given. Acyclic partitioning of VLSI circuits is studied under area/delay, 1-0 size and communication constraints. In this paper, we define the path-delay-fault emulation problem which adds a new constraint, viz. path count constraint, to partitioning problem. We present two algorithms to solve the problem. The first algorithm decomposes a circuit into entirely-fanout-free cones (EFFC), and clusters them into partitions. The second one finds an intermediate partitioning solution with the partitioning algorithm ignoring path count constraint. Later, it applies the first algorithm to the partitions which violate the path count constraint. We implemented the first algorithm and measured its efficiency in terms of the number of resulting partitions, cut-cost, and time cost for ISCAS85 benchmarks.

We devise one exact and one pessimistic path delay fault (PDF) grading algorithms for combinational circuits. The first algorithm, an extension to the basic grading algorithm of Padmanaban, Michael, and Tragoudas (2003), does not store all of the detected PDFs during the course of grading, and, as a further improvement, it utilizes compressed representation of PDFs. These two techniques yield a space-and-time efficient algorithm. To enable grading of circuits with exponential number of paths, a circuit is first partitioned into a set of subcircuits. The second algorithm efficiently calculates the coverage of partitioned circuits. The former algorithm results in 50%-70% reduction in space and a speedup from 1.6 to 2.48 in ISCAS85 benchmarks. The time complexity of the latter algorithm is O(N/sup 2/) subset operations per test vector where N is the number of nets in the circuit.