Advanced Persistent Threat (APT) attacks are a major concern for the
cybersecurity in digital world due to their advanced nature. Attackers are skilful to
cause maximal destruction for targeted cyber environment. These APT attacks are
also well funded by governments in many cases. The APT attacker can achieve his
hostile goals by obtaining information and gaining financial benefits regarding the
infrastructure of a network. It is highly important to study proper countermeasures to
detect these attacks as early as possible due to sophisticated methods. It is difficult to
detect this type of attack since the network may crash because of high traffic. Hence,
in this study, this research is to study the comparison between Multilayer
Perceptron and Naïve-Bayes of APT attack detection. Since the APT attack is
persistent and permanent presence in the victim system, so minimal false
positive rate (FPR) and high accuracy detection is required to detect the APT
attack detection. Besides, Multilayer Perceptron algorithm has high true
positive rate (TPR) in the detection of APT attack compared to Naïve Bayes
algorithm. This means that Multilayer Perceptron algorithm can detect APT
attack more accurately. Based on the result, it also can conclude that the lower
the false positive rate (FPR), the more accurate to detect APT attack. Lastly,
the research would also help to spread the awareness about the APT intrusion
where it possibly can cause huge damage to everyone
