2,520 research outputs found
Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accounts
So-called 'social bots' have garnered a lot of attention lately. Previous
research showed that they attempted to influence political events such as the
Brexit referendum and the US presidential elections. It remains, however,
somewhat unclear what exactly can be understood by the term 'social bot'. This
paper addresses the need to better understand the intentions of bots on social
media and to develop a shared understanding of how 'social' bots differ from
other types of bots. We thus describe a systematic review of publications that
researched bot accounts on social media. Based on the results of this
literature review, we propose a scheme for categorising bot accounts on social
media sites. Our scheme groups bot accounts by two dimensions - Imitation of
human behaviour and Intent.Comment: Accepted for publication in the Proceedings of the Australasian
Conference on Information Systems, 201
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
Classification hardness for supervised learners on 20 years of intrusion detection data
This article consolidates analysis of established (NSL-KDD) and new intrusion detection datasets (ISCXIDS2012, CICIDS2017, CICIDS2018) through the use of supervised machine learning (ML) algorithms. The uniformity in analysis procedure opens up the option to compare the obtained results. It also provides a stronger foundation for the conclusions about the efficacy of supervised learners on the main classification task in network security. This research is motivated in part to address the lack of adoption of these modern datasets. Starting with a broad scope that includes classification by algorithms from different families on both established and new datasets has been done to expand the existing foundation and reveal the most opportune avenues for further inquiry. After obtaining baseline results, the classification task was increased in difficulty, by reducing the available data to learn from, both horizontally and vertically. The data reduction has been included as a stress-test to verify if the very high baseline results hold up under increasingly harsh constraints. Ultimately, this work contains the most comprehensive set of results on the topic of intrusion detection through supervised machine learning. Researchers working on algorithmic improvements can compare their results to this collection, knowing that all results reported here were gathered through a uniform framework. This work's main contributions are the outstanding classification results on the current state of the art datasets for intrusion detection and the conclusion that these methods show remarkable resilience in classification performance even when aggressively reducing the amount of data to learn from
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