  Attackers are now using sophisticated techniques, like polymorphism, to
change the attack pattern for each new attack. Thus, the detection of novel
attacks has become the biggest challenge for cyber experts and researchers.
Recently, anomaly and hybrid approaches are used for the detection of network
attacks. Detecting novel attacks, on the other hand, is a key enabler for a
wide range of IoT applications. Novel attacks can easily evade existing
signature-based detection methods and are extremely difficult to detect, even
going undetected for years. Existing machine learning models have also failed
to detect the attack and have a high rate of false positives. In this paper, a
rule-based deep neural network technique has been proposed as a framework for
addressing the problem of detecting novel attacks. The designed framework
significantly improves respective benchmark results, including the CICIDS 2017
dataset. The experimental results show that the proposed model keeps a good
balance between attack detection, untruthful positive rates, and untruthful
negative rates. For novel attacks, the model has an accuracy of more than 99%.
During the automatic interaction between network-devices (IoT), security and
privacy are the primary obstacles. Our proposed method can handle these
obstacles efficiently and finally identify, and classify the different levels
of threats.
