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Clearing Multiview Structure Graph from Inconsistencies

2018 Digital Image Computing: Techniques and Applications (DICTA), 2018

Dealing with repetitive patterns in images proves to be difficult in Multiview structure from motion. Previous work in the field suggests that this problem can be solved by clearing inconsistent rotations in the visual graph that represents pairwise relations between images. So we present a simple and rather effective algorithm, to clear the graph based on cycles. While trying to ...


A Cloud Provider’s View of EDNS Client-Subnet Adoption

2019 Network Traffic Measurement and Analysis Conference (TMA), 2019

Directing users to a nearby, high-performing front-end is core to the business of content delivery networks (CDNs). CDNs which use DNS to direct users to servers face the challenge of making decisions based at the LDNS-level, not based on the client's IP address, and, in many cases, an LDNS is not representative of the clients it serves. The EDNS Client ...


Automatic Whitelist Generation for SQL Queries Using Web Application Tests

2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), 2019

Stealing confidential information from a database has become a severe vulnerability issue for web applications. The attacks can be prevented by defining a whitelist of SQL queries issued by web applications and detecting queries not in list. For large-scale web applications, automated generation of the whitelist is conducted because manually defining numerous query patterns is impractical for developers. Conventional methods ...


Whitelisting Without Collisions for Centralized Scheduling in Wireless Industrial Networks

IEEE Internet of Things Journal, 2019

Industrial applications require more and more low-power operation and high- reliability (close to 100%). Since traditional low-power radio technologies are sensitive to external interference, many recent standards implement frequency hopping schemes. For instance, IEEE 802.15.4-2015 time slotted channel hopping (TSCH) relies on a deterministic schedule of data transmissions combined with a pseudo-random frequency hopping scheme to improve the reliability. Unfortunately, ...


An Adaptive Distributed Scheduling Algorithm for IEEE 802.15.4e TSCH Protocol

2019 3rd International Symposium on Autonomous Systems (ISAS), 2019

Industrial Wireless Sensor Networks (IWSNs) can be flexibly deployed in industries with low cost. Thus, the industrial production process integrated with IWSNs can get more information to improve the performance of control and management. Due to the harsh environments and stringent reliability requirements in industries, Time-Slotted Channel Hopping (TSCH) is introduced in IEEE 802.15.4e-based wireless networks to improve their certainty ...


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  • Clearing Multiview Structure Graph from Inconsistencies

    Dealing with repetitive patterns in images proves to be difficult in Multiview structure from motion. Previous work in the field suggests that this problem can be solved by clearing inconsistent rotations in the visual graph that represents pairwise relations between images. So we present a simple and rather effective algorithm, to clear the graph based on cycles. While trying to generate all cycles within the graph is computationally impossible in most cases, we choose to verify only the cycles that we need, and without relying on the spanning tree method because it puts a big emphasis on certain edges.

  • A Cloud Provider’s View of EDNS Client-Subnet Adoption

    Directing users to a nearby, high-performing front-end is core to the business of content delivery networks (CDNs). CDNs which use DNS to direct users to servers face the challenge of making decisions based at the LDNS-level, not based on the client's IP address, and, in many cases, an LDNS is not representative of the clients it serves. The EDNS Client Subnet specification provides a solution by embedding a portion of the client's IP address in the DNS query to help CDNs make better redirection decisions, but both the LDNS and authoritative resolver (CDN side) must support the standard. While there has been well-publicized adoption of Client Subnet by authoritative CDN resolvers, adoption rates across LDNSes are unknown. In this work, we examine Client Subnet adoption in LDNSes. We analyze DNS queries captured over one month from Microsoft's Azure Cloud platform. We find that adoption on the Internet is very low across ISPs but query volume is relatively high due to the popularity of public DNS services. We discover high network adoption rates in China and reveal that Chinese public DNS services deploy LDNSes deep into end-user networks.

  • Automatic Whitelist Generation for SQL Queries Using Web Application Tests

    Stealing confidential information from a database has become a severe vulnerability issue for web applications. The attacks can be prevented by defining a whitelist of SQL queries issued by web applications and detecting queries not in list. For large-scale web applications, automated generation of the whitelist is conducted because manually defining numerous query patterns is impractical for developers. Conventional methods for automated generation are unable to detect attacks immediately because of the long time required for collecting legitimate queries. Moreover, they require application-specific implementations that reduce the versatility of the methods. As described herein, we propose a method to generate a whitelist automatically using queries issued during web application tests. Our proposed method uses the queries generated during application tests. It is independent of specific applications, which yields improved timeliness against attacks and versatility for multiple applications.

  • Whitelisting Without Collisions for Centralized Scheduling in Wireless Industrial Networks

    Industrial applications require more and more low-power operation and high- reliability (close to 100%). Since traditional low-power radio technologies are sensitive to external interference, many recent standards implement frequency hopping schemes. For instance, IEEE 802.15.4-2015 time slotted channel hopping (TSCH) relies on a deterministic schedule of data transmissions combined with a pseudo-random frequency hopping scheme to improve the reliability. Unfortunately, specific radio channels keep on increasing the average number of retransmissions. Using a subset of the best radio channels (whitelisting) helps to improve the reliability, but may create collisions when used improperly. We here investigate the most accurate techniques to use only the best radio channels while still providing deterministic performance. We propose to group the links per timeslot, allocating them either to the same whitelist or even appropriately reordering them to avoid collisions. Finally, we evaluate the performance of the different whitelisting schemes using an experimental dataset from FIT IoT-LAB platform, proving the relevance of such approach to improve the reliability.

  • An Adaptive Distributed Scheduling Algorithm for IEEE 802.15.4e TSCH Protocol

    Industrial Wireless Sensor Networks (IWSNs) can be flexibly deployed in industries with low cost. Thus, the industrial production process integrated with IWSNs can get more information to improve the performance of control and management. Due to the harsh environments and stringent reliability requirements in industries, Time-Slotted Channel Hopping (TSCH) is introduced in IEEE 802.15.4e-based wireless networks to improve their certainty and reliability. However, there is no explicit scheduling mechanism which should be made according to the industrial production requirements and the wireless environments. In this paper, the structure of transmission sequence of the TSCH networks is proposed and the adaptive scheduling algorithm is key of the structure. The scheduling algorithm takes the difference between the number of packets in the queue and the number of scheduled cells as the error of the incremental PID controller which can schedule the suitable number of cells to transmit the packets timely. When the quality of channels changes, each node can execute an adaptive channel selection procedure to predict the difference of using different channels. As a result, each node can dynamically select suitable channels to use. Simulations validate the effectiveness of the proposed scheduling algorithm.

  • A Lightweight Trust-Based Security Architecture for RPL in Mobile IoT Networks

    Military communities have come to rely heavily on commercial off the shelf (COTS) standards and technologies for Internet of Things (IoT) operations. One of the major obstacles to military use of COTS IoT devices is the security of data transfer. In this paper, we successfully design and develop a lightweight, trust-based security architecture to support routing in a mobile IoT network. Specifically, we modify the RPL IoT routing algorithm using common security techniques, including a nonce identity value, timestamp, and network whitelist. Our approach allows RPL to select a routing path over a mobile IoT wireless network based on a computed node trust value and average received signal strength indicator (ARSSI) value across network members. We conducted simulations using the Cooja network simulator and Wireshark to validate the algorithm against stipulated threat models. We demonstrate that our algorithm can protect the network against Denial of Service (DoS) and Sybil based identity attacks. We also show that the control overhead required for our algorithm is less than 5% and that the packet delivery rate improves by nearly 10%.

  • An Evaluation of DGA Classifiers

    Domain Generation Algorithms (DGAs) are a popular technique used by contemporary malware for command-and-control (C&C;) purposes. Such malware utilizes DGAs to create a set of domain names that, when resolved, provide information necessary to establish a link to a C&C; server. Automated discovery of such domain names in real-time DNS traffic is critical for network security as it allows to detect infection, and, in some cases, take countermeasures to disrupt the communication and identify infected machines. Detection of the specific DGA malware family provides the administrator valuable information about the kind of infection and steps that need to be taken. In this paper we compare and evaluate machine learning methods that classify domain names as benign or DGA, and label the latter according to their malware family. Unlike previous work, we select data for test and training sets according to observation time and known seeds. This allows us to assess the robustness of the trained classifiers for detecting domains generated by the same families at a different time or when seeds change. Our study includes tree ensemble models based on human-engineered features and deep neural networks that learn features automatically from domain names. We find that all state-of-the-art classifiers are significantly better at catching domain names from malware families with a time-dependent seed compared to time-invariant DGAs. In addition, when applying the trained classifiers on a day of real traffic, we find that many domain names unjustifiably are flagged as malicious, thereby revealing the shortcomings of relying on a standard whitelist for training a production grade DGA detection system.

  • Decentralized IoT Security Gateway

    Due to the spread of Internet of Things (IoT) devices, IoT security management has become important for network administrators. However, security functions are difficult to implement into IoT devices. We thus propose a security system that controls anomaly traffic as a network function. This system is installed in a gateway close to an IoT device. It automatically generates the control rules for each IoT device with user support and improves the accuracy of information sharing between gateways. By distributing the gateway processing, the system prevents processing load and control authority from concentrating at the central server while reducing the cost of using the server. We also present the system's architecture and discuss the evaluation of its performance.

  • Safeguarding from abuse by IoT vendors: Edge messages verification of cloud-assisted equipment

    The fact that most IoT solutions are provided by 3rd-parties, along with the pervasiveness of the collected data, raises privacy and security concerns. There is a need to verify which data is being sent to the 3rd-party, as well as preventing those channels from becoming an exploitation avenue. We propose to use existing API definition languages to create contracts which define the data that can be transmitted, in what format, and with which constraints. To verify the compliance with these contracts, we propose a converging "Multi- Access Edge Computing" architecture which validates RESTalike API requests/responses against a Swagger schema. We deal with encrypted traffic using an SFC-enabled Man-in-the-Middle, allowing us to do verifications in "real-time". We devised a Proof of Concept and shown that we were able to detect (and stop) contract violations.

  • Malicious Domain Names Detection Algorithm Based on Lexical Analysis and Feature Quantification

    Malicious domain names usually refer to a series of illegal activities, posing threats to people’s privacy and property. Therefore, the problem of detecting malicious domain names has aroused widespread concerns. In this study, a malicious domain names detection algorithm based on lexical analysis and feature quantification is proposed. To achieve efficient and accurate detection, the method includes two phases. The first phase checks an observed domain name against a blacklist of known malicious uniform resource locator (URLs). The observed domain name is classified as being definitely malicious or potentially malicious based on its edit distances to the domain names on the blacklist. The second phase further evaluates a potential malicious domain name by its reputation value that represents its lexical feature and is calculated based on an N-gram model. The top 100,000 normal domain names in Alexa are used to obtain a whitelist substring set using the N-gram method in which each domain name excluding the top-level domain is segmented into substrings with the length of 3, 4, 5, 6 and 7. The weighted values of the substrings are calculated according to their occurrence counts in the whitelist substring set. A potential malicious domain name is segmented by the N-gram method and its reputation value is calculated based on the weighted values of its substrings. Finally, the potential malicious domain name is determined to be malicious or normal based on its reputation value. The effectiveness of the proposed detection method has been demonstrated by experiments on public available data.



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