Research

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26 pages, 3428 KiB

Open AccessArticle

A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment

by Niddal H. Imam and Vassilios G. Vassilakis

Robotics 2019, 8(3), 50; https://doi.org/10.3390/robotics8030050 - 04 Jul 2019

Cited by 19 | Viewed by 6469

Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine learning (ML) techniques have been widely used as a [...] Read more.

Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine learning (ML) techniques have been widely used as a tool to address many cybersecurity application problems (such as spam and malware detection). However, most of the proposed approaches do not consider the presence of adversaries that target the defense mechanism itself. Adversaries can launch sophisticated attacks to undermine deployed spam detectors either during training or the prediction (test) phase. Not considering these adversarial activities at the design stage makes OSNs’ spam detectors vulnerable to a range of adversarial attacks. Thus, this paper surveys the attacks against Twitter spam detectors in an adversarial environment, and a general taxonomy of potential adversarial attacks is presented using common frameworks from the literature. Examples of adversarial activities on Twitter that were discovered after observing Arabic trending hashtags are discussed in detail. A new type of spam tweet (adversarial spam tweet), which can be used to undermine a deployed classifier, is examined. In addition, possible countermeasures that could increase the robustness of Twitter spam detectors to such attacks are investigated. Full article

(This article belongs to the Special Issue Latest Artificial Intelligence Research Output 2018)

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22 pages, 3715 KiB

Open AccessArticle

Long-Term Adaptivity in Distributed Intelligent Systems: Study of ViaBots in a Simulated Environment

by Arturs Ardavs, Mara Pudane, Egons Lavendelis and Agris Nikitenko

Robotics 2019, 8(2), 25; https://doi.org/10.3390/robotics8020025 - 29 Mar 2019

Cited by 2 | Viewed by 5093

Abstract

This paper proposes a long-term adaptive distributed intelligent systems model which combines an organization theory and multi-agent paradigm—ViaBots. Currently, the need for adaptivity in autonomous intelligent systems becomes crucial due to the increase in the complexity and diversity of the tasks that autonomous [...] Read more.

This paper proposes a long-term adaptive distributed intelligent systems model which combines an organization theory and multi-agent paradigm—ViaBots. Currently, the need for adaptivity in autonomous intelligent systems becomes crucial due to the increase in the complexity and diversity of the tasks that autonomous robots are employed for. To deal with the design complexity of such systems within the ViaBots model, each part of the modeled system is designed as an autonomous agent and the entire model, as a multi-agent system. Based on the viable system model, which is widely used to ensure viability, (i.e., long-term autonomy of organizations), the ViaBots model defines the necessary roles a system must fulfill to be capable to adapt both to changes in its environment (like changes in the task) and changes within the system itself (like availability of a particular robot). Along with static role assignments, ViaBots propose a mechanism for role transition from one agent to another as one of the key elements of long term adaptivity. The model has been validated in a simulated environment using an example of a conveyor system. The simulated model enabled the multi-robot system to adapt to the quantity and characteristics of the available robots, as well as to the changes in the parts to be processed by the system. Full article

(This article belongs to the Special Issue Latest Artificial Intelligence Research Output 2018)

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Review

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29 pages, 1300 KiB

Open AccessReview

Heterogeneous Map Merging: State of the Art

by Ilze Andersone

Robotics 2019, 8(3), 74; https://doi.org/10.3390/robotics8030074 - 20 Aug 2019

Cited by 16 | Viewed by 7273

Abstract

Multi-robot mapping and environment modeling have several advantages that make it an attractive alternative to the mapping with a single robot: faster exploration, higher fault tolerance, richer data due to different sensors being used by different systems. However, the environment modeling with several [...] Read more.

Multi-robot mapping and environment modeling have several advantages that make it an attractive alternative to the mapping with a single robot: faster exploration, higher fault tolerance, richer data due to different sensors being used by different systems. However, the environment modeling with several robotic systems operating in the same area causes problems of higher-order—acquired knowledge fusion and synchronization over time, revealing the same environment properties using different sensors with different technical specifications. While the existing robot map and environment model merging techniques allow merging certain homogeneous maps, the possibility to use sensors of different physical nature and different mapping algorithms is limited. The resulting maps from robots with different specifications are heterogeneous, and even though some research on how to merge fundamentally different maps exists, it is limited to specific applications. This research reviews the state of the art in homogeneous and heterogeneous map merging and illustrates the main research challenges in the area. Six factors are identified that influence the outcome of map merging: (1) robotic platform hardware configurations, (2) map representation types, (3) mapping algorithms, (4) shared information between robots, (5) relative positioning information, (6) resulting global maps. Full article

(This article belongs to the Special Issue Latest Artificial Intelligence Research Output 2018)

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