## 1 Introduction  
The recent Data Breach Investigations Report (DBIR) by Verizon reports that 34% of the reported data breaches were a result of internal actors' involvement and 2% of the data breaches were the result of a partner's involvement [1]. The report was based on an analysis of 41,686 security incidents, of which 2013 are confirmed data breaches. The previous yearly data breach reports, DBIR 2018 and DBIR 2017, show a data breach percentage involving internal actors of 28% and 25%, respectively [2, 3]. The insider threat has been on the rise and the latest DBIR reports by Verizon confirm the rapid increase of the problem. The term \"Data Breach\" indicates a confirmed data disclosure after the event of a security incident [1]. An internal actor, or insider in an organization, is a current or former employee, partner, contractor, consultant, temporary personnel, personnel from partners, subsidiaries, contractors and anyone else that has been granted access privilege in the organization's network or data [4, 5]. The Computer Emergency Readiness Team (CERT) National Insider Threat Center defines malicious insider as \"a current or former employee, contractor, or business partner\" who has authorized access to the organizational system and network resources and has intentionally exceeded or used that access in a manner that compromises the confidentiality, integrity and availability of the organization's data and information systems. An unintentional insider threat is an internal actor who has authorized access to organizational system and network resources and \"causes harm or substantially increases the probability of future serious harm of the confidentiality, integrity and availability of the organization's data and information systems\" [6]. The insider threat is summed up as a security threat which describes the intentional or unintentional privileged misuse by an internal actor that causes damage to an organization's asset. A survey conducted by the CERT National Threat Center and CSO Magazine revealed that 30% of the survey responders considered the damage caused by insider attacks more severe than the damage caused by outsider attacks [6]. Insider attacks include information system sabotage, theft of intellectual property, disclosure of confidential information, theft of trade secrets and espionage that leads organizations to financial losses and also negatively impacts their reputation and brand [6]. There is plenty of literature available on the mitigation of the insider threat problem that focuses on methods for detecting the insider threat. In this paper, we focus on the detection of insider threat using bio-inspired computing and utilizing machine learning. Bio-inspired computing is an emerging approach, inspired by biological evolution, to develop new models that provides a solution for complex optimization problems in a timely manner. The explosion of data in the digital era has created challenges which are difficult to approach with traditional and conventional optimization algorithms and led the scientific community to develop bio-inspired algorithms that can be applied as a solution. Swarm Intelligence is a family of bio-inspired algorithms. These algorithms have been proposed by researchers to solve optimization problems by obtaining near-optimal solutions [7]. The purpose of this paper is to approach the insider threat problem with a new model that utilizes algorithms inspired by nature and contributes to the insider threat domain research by exploring metaheuristic algorithms to solve feature selection optimization problems and improve the performance of machine-learning-based insider threat detection models. The performance improvement of these models will help organizations and governments to detect malicious insiders in time, and prevent severe damage. The rest of the paper is organized as follows. In Section 2, we review research related to the mitigation of insider threats. In Section 3, we present our methodology for the proposed approach.