Detection of Illegal Activity of an Employee in an Organisation

Insider cyber attacks represent a pervasive threat that continues to escalate in complexity and frequency, posing significant challenges for organizations globally.Perpetrated by individuals with privileged access to sensitive systems and information, insider attacks undermine the very foundations of trust and security within organizations.The ramifications of insider breaches extend far beyond mere financial losses, encompassing reputational damage, regulatory scrutiny, and erosion of customer confidence.These insidious attacks manifest in various forms, ranging from the clandestine installation of malware on user devices to orchestrated campaigns targeting critical infrastructure and corporate networks.The insidious nature of insider attacks amplifies the risk of data corruption, theft, and manipulation, exposing organizations to unprecedented vulnerabilities and liabilities.Moreover, the collateral damage inflicted upon unsuspecting users, who may fall victim to identity theft and financial fraud, Our proposed approach represents a pivotal step towards fortifying the defenses against insider threats, empowering organizations to preemptively thwart malicious activities and safeguard critical assets.Through the seamless integration of machine learning techniques, organizations can augment their cybersecurity arsenal with proactive threat intelligence capabilities, enabling real-time detection and response to emerging threats.Our system comprises two distinct servers: an original server housing genuine data and sensitive information, and a honeypot server strategically designed to entice potential attackers.The honeypot server serves as a decoy, containing dummy files intended to lure malicious insiders attempting to access unauthorized information.Meanwhile, as the attacker interacts with the honeypot server, the system discreetly sends alerts to the IT Administrator, enabling swift intervention and response to the security breach.The core objective of our model is to establish a proactive defense mechanism against insider threats, thereby safeguarding the integrity and confidentiality of organizational data.By hosting both original and decoy servers, our system not only detects suspicious activities but also provides valuable insights into the methods and motivations of malicious insiders.Through comprehensive tracking and monitoring, we aim to enhance accountability and deterrence, ultimately fortifying the resilience of organizational systems against insider attacks.In summary, our approach represents a pivotal advancement in combating insider threats by leveraging innovative technology and strategic deployment of decoy servers.By prioritizing early detection, swift intervention, and proactive mitigation, our model empowers organizations to mitigate risks, protect critical assets, and uphold the trust and integrity of their operations in an increasingly complex digital landscape.