… human behavior with computational intelligence, and it … machine learning steps in a
comprehensive manner to detect DDoS attacks, (2) designing the framework to include optimization …

… solution proposed was not optimal and there were scalability … machine learning methods
that are highly scalable. In order to conduct the performance evaluation, we outline a framework …

… However, this work merged all attacks into one label as the goal is to detect an attack … and
the UNSW-NB 2015 dataset) for performance evaluation, this work first studied the impact of …

… novel practical adversarial attack by formulating it as a bi-level optimization problem with a
… combinatorial optimization task (NP-complete). Hence, we resort to swarm intelligence algo…

… of various attacks using intelligent ML algorithms. ML can be explained as improving the
learning … In order to produce the optimal predictive model, this ML technique combines several …

… features of the attacks in the dataset. We used PSO which is a metaheuristic optimization
algorithm for … IT Tosin, and AN Babatunde, ‘‘Student’s performance analysis using decision tree …

… machine learning algorithms according to different attack … Then, the subset which may
have optimum performance from … , the evaluation criteria used for performance evaluation are …

… The learning performance evaluation can be shown in the … more effectively combine the
network anomaly-based IDS with an intelligent host-based IDS that works based on the log host […

… optimized using the Fruitfly algorithm. Using data from the NSL-KDD and UNSW-NB15
datasets, the evaluation … The performance evaluation demonstrated that the accuracy increased …

… an attack if it missed the first packets of the attack broadcast. … } is an essential task for
performance evaluation of an anomaly … as an optimization algorithm to find the optimal value of the …