Using Agent-Based Modelling to Address Malicious Behavior on Social Media

In this study we create a platform for evaluating social media policies through simulation. We argue that social media policies need to be tested and refined before they can be successfully applied. We propose agent-based modelling (ABM) as a method for representing both malicious and legitimate social media agents, along with their key behaviors. Our two main research questions are as follows. 1. How do we build an agent-based model of a social media platform to address social media regulation? 2. How can an agent-based simulation approach be used to assess the effectiveness of social media policies? A preliminary agent-based model has been implemented (in Python), using the five human user types (‘amplifier’, ‘broadcaster’, ‘commentator’, ‘influential user’ and ‘viewer’) and two bot types (‘simple’ and ‘sophisticated’). During the simulation, a social media network of 100 agents is created and the agents\u27 behaviors are captured in this paper

Disruption and Deception in Crowdsourcing: Towards a Crowdsourcing Risk Framework

While crowdsourcing has become increasingly popular among organizations, it also has become increasingly susceptible to unethical and malicious activities. This paper discusses recent examples of disruptive and deceptive efforts on crowdsourcing sites, which impacted the confidentiality, integrity, and availability of the crowdsourcing efforts’ service, stakeholders, and data. From these examples, we derive an organizing framework of risk types associated with disruption and deception in crowdsourcing based on commonalities among incidents. The framework includes prank activities, the intentional placement of false information, hacking attempts, DDoS attacks, botnet attacks, privacy violation attempts, and data breaches. Finally, we discuss example controls that can assist in identifying and mitigating disruption and deception risks in crowdsourcing

Analysis of Malicious Behavior on Social Media Platforms Using Agent-Based Modeling

The following dissertation focuses on the problem of malicious accounts actions on social media. The dissertation proposes an artifact in a form of an agent-based model of Twitter network. The model is then deployed to investigate a policy intervention that aims at controlling malicious behavior on social media outlets. The main research question is: How can we best intervene in social networks to minimize the effects of malicious actors? The dissertation consists of four studies that are organized as follows. First study is designed as an exploratory investigation that deploys descriptive analysis and unsupervised methods to analyze the types of malicious accounts and their impact on information exchange on social media. Second study explores malicious behavior on social media and employs cluster analysis to capture different types of malicious accounts originating from Russia, Iran, Venezuela and Spain (Catalonia). Third study follows the design science research methodology (Hevner et al., 2004) and builds on the findings presented in the first part of the dissertation. It incorporates an extensive literature review to build an agent-based model (ABM) of a simulated Twitter network with defined malicious and legitimate agents. Fourth study deploys the defined agent-based model to test out a policy-based intervention that aim to prevent a proliferation of malicious content on a simulated social media network. The dissertation serves as a next step towards a better understanding of the impact of malicious actions on social media outlets. It proposes an artifact in the form of an agent-based model that simulates social media network environment. The proposed simulation captures the complexity of agent behaviors and models the dynamic interactions between the social media agents. On top of that, the dissertation tests out a sample social media policy intervention that targets malicious behavior

Analysis of Malicious Behavior on Social Media Platforms Using Agent-Based Modeling

The following dissertation focuses on the problem of malicious accounts actions on social media. The dissertation proposes an artifact in a form of an agent-based model of Twitter network. The model is then deployed to investigate a policy intervention that aims at controlling malicious behavior on social media outlets. The main research question is: How can we best intervene in social networks to minimize the effects of malicious actors? The dissertation consists of four studies that are organized as follows. First study is designed as an exploratory investigation that deploys descriptive analysis and unsupervised methods to analyze the types of malicious accounts and their impact on information exchange on social media. Second study explores malicious behavior on social media and employs cluster analysis to capture different types of malicious accounts originating from Russia, Iran, Venezuela and Spain (Catalonia). Third study follows the design science research methodology (Hevner et al., 2004) and builds on the findings presented in the first part of the dissertation. It incorporates an extensive literature review to build an agent-based model (ABM) of a simulated Twitter network with defined malicious and legitimate agents. Fourth study deploys the defined agent-based model to test out a policy-based intervention that aim to prevent a proliferation of malicious content on a simulated social media network. The dissertation serves as a next step towards a better understanding of the impact of malicious actions on social media outlets. It proposes an artifact in the form of an agent-based model that simulates social media network environment. The proposed simulation captures the complexity of agent behaviors and models the dynamic interactions between the social media agents. On top of that, the dissertation tests out a sample social media policy intervention that targets malicious behavior

Towards Understanding Malicious Actions on Twitter

In this study we investigate the characteristics of malicious account behaviors on Twitter based on the analysis of the published data archive. We investigate emergent behavior of malicious accounts that Twitter tagged as connected to state-backed information operations, identified as malicious and removed from the Twitter network. We focus on the analysis of four types of malicious accounts’ features: (1) Account reputation, (2) Account tweeting frequency, (3) Age of account and (4) Account activity score. With the use of descriptive statistics and unsupervised learning, we attempt to extend past research that defined behavioral patterns of malicious actors on Twitter. Our research contributes to the understanding of behavior of malicious actors and enriches current research in that area. In this paper we analyze the dataset published by Twitter in January 2019, which contains details on suspended malicious accounts’ activities initiated in Bangladesh. To the best of our knowledge, this article is the first effort to extend research on malicious account behavior based on labeled proprietary data on removed malicious accounts identified and released by Twitter itself

ROCKET SHIP OR BLIMP? – IMPLICATIONS OF MALICIOUS ACCOUNTS REMOVAL ON TWITTER

In this study we investigate how the removal of malicious accounts that follow legitimate accounts owned by popular people impacts the popularity of tweets posted by celebrities and politicians. Using retweet counts, we analyze to what extent malicious accounts contribute to amplification of tweets across the network. We organize tweets into three broad categories (Rocket Ship, Jet or Blimp) and investigate how the distribution of tweets is influenced by a cleanup of malicious accounts. To understand how the suspension of malicious accounts impacts the propagation of messages on Twitter, we conduct a descriptive statistical analysis of retweets of a total of 464 Donald Trump tweets. We find a statistically significant difference in the mean count of retweets and favorites before and after the malicious account removal. Preliminary results of our analysis show that the implications of Twitter’s cleanup initiatives, which targeted malicious accounts, are visible in the narrowing amplitudes of retweet values. However, the distribution of tweet categories based on the number of retweets remains unchanged

Detection and Classification of Attacks on IoT Networks

In this article we are analyzing how Industrial Internet of Things (IIoT) sensors and devices behave while they undergo an attack. Using data generated from a controlled experiment where attacks were carried out on a Secure Water Treatment (SWaT) system, we analyze the behavior of the sensors. We observe that the readings from the sensors are non-linear in nature and resemble ECG waveform output, which helps in identifying inconsistencies or anomalies in heartbeats of patients. Through the comparison of sensor behavior during an attack and under normal conditions we find a significant difference in the features of the waveforms. Also, we look at the contrasting behavior of sensors under two different kinds of attacks: physical and cyber. The findings of this research motivate an alternative approach for anomaly detection and real time assessment of cyber-attacks on IoT devices with the use of analytics