Impact of Internet of Things Devices on Corporate Networks
Open Access DepositedImpact of Internet of Things Devices on Corporate NetworksThe rapid proliferation of Internet of Things (IoT) devices in corporate networks has revolutionized operational efficiency but also introduced complex security challenges. This research focused on developing an advanced anomaly detection algorithm that hybridizes the long short-term memory (LSTM) and isolation forest models to effectively monitor network traffic and identify anomalies indicative of cybersecurity threats specific to IoT devices. The study employed data sets from Kaggle encompassing intrusion profiles and a mix of malicious and benign traffic data. The standalone LSTM model was highly effective at identifying anomalies. However, the standalone isolation forest model exhibited substantial limitations, particularly with respect to its true positive rate. The hybrid model slightly outperformed the standalone LSTM model, particularly with respect to reducing false positives. In addition, a direct correlation was identified between the surge in IoT devices and heightened vulnerabilities within corporate networks, evidenced by an increase in malware incidents. The results underscore the urgent need for organizations to prioritize cybersecurity in the rapidly evolving landscape of IoT, contribute to a deeper understanding of the corresponding cybersecurity challenges, and provide a robust foundation for developing advanced anomaly detection techniques. The paper also outlines future research directions, including expanding the ability of these models to classify cyberattack types, implementing the models in real-world scenarios to validate their effectiveness across a variety of threat landscapes, and continuing to invest in developing advanced anomaly detection techniques.
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