Intrusion Detection through DCSYS Propagation Compared to Auto-encoders

Fatima Isiaka (Department of Computer Science, Nasarawa State University, Keffi, Nigeria)
Zainab Adamu (Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria)


In network settings, one of the major disadvantages that threaten the network protocols is the insecurity. In most cases, unscrupulous people or bad actors can access information through unsecured connections by planting software or what we call malicious software otherwise anomalies. The presence of anomalies is also one of the disadvantages, internet users are constantly plagued by virus on their system and get activated when a harmless link is clicked on, this a case of true benign detected as false. Deep learning is very adept at dealing with such cases, but sometimes it has its own faults when dealing benign cases. Here we tend to adopt a dynamic control system (DCSYS) that addresses data packets based on benign scenario to truly report on false benign and exclude anomalies. Its performance is compared with artificial neural network auto-encoders to define its predictive power. Results show that though physical systems can adapt securely, it can be used for network data packets to identify true benign cases.


Dynamic control system;Deep learning;Artificial neural network;Auto-encoders;Identify space model;Benign;Anomalies

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