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One of the most persistent challenges concerning network security is to build a model capable of detecting intrusions in network systems. The issue has been extensively addressed in uncountable researches and using various techniques, of which a commonly used technique is that based on detecting intrusions in contrast to normal network traffic and the classification of network packets as either normal or abnormal. However, the problem of improving the accuracy and efficiency of classification models remains open and yet to be resolved. This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. At first the model selects the suitable biases and weights utilizing a hybrid (ABC) and (DA). Next, the neural network is retrained using these ideal values in order for the intrusion detection model to be able to recognize new attacks. Ten other metaheuristic algorithms were adapted to train the neural network and their performances were compared with that of the proposed model. In addition, four types of intrusion detection evaluation datasets were applied to evaluate the proposed model in comparison to the others. The results of our experiments have demonstrated a significant improvement in inefficient network intrusion detection over other classification methods. INDEX TERMS Intrusion Detection System (IDS); Multilayer Perceptron (MLP); Metaheuristic Algorithm (MA); Artificial Bee Colony Algorithm (ABC); Dragonfly algorithm (DA)

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The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapperapproach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques. INDEX TERMSIntrusion detection system (IDS), bat algorithm (BAT), metaheuristic algorithm (MA), feature selection (FS), multi-objective optimization (MOO), multilayer perceptron (MLP)

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