# Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection
## 2 Literature Review  
DoS and DDoS assaults are a severe threat to many organizations because of their tremendous ability to bring down unprotected servers in a short period. Therefore, the prevention of DoS and DDoS attacks has been the subject of many research proposals. Current studies suggest some robust defensive frameworks from network breach attacks, which are briefly explained below. Detection and classification of DDoS attacks was studied by [PERSON] et al. [25], who incorporated two deep learning-based techniques using an auto encoder (AE) multi-layer perceptron (MLP). To perform feature extraction without human assistance, AE was implemented by the authors. Using the extracted features, various kinds of DDoS attacks were classified by MLP network. To assess the effectiveness of the suggested approach, large DDoS attack samples from CICDDoS2017 were extracted to access the accuracy metrics. [PERSON] et al. [26] studied a generative adversarial network (GAN)-based reliable detector for identifying cyberattacks. In this system, two distinct GAN-based models were implemented. The first generator produced benign instances that closely resembled benign samples from the dataset and the second generator was capable of producing DDoS cases that closely resembled those from the dataset. Moreover, the creation of a DNN classifier-based framework facilitated distinguishing between huge samples of DDoS and benign classes over structural similarity metrics. GANs are a powerful deep generative model trained with an adversarial procedure. GANs have undergone several modifications since they were first proposed to solve several different problems in different domains [27]. [PERSON] et al. [28] implemented a deep neural network (DNN)-based intrusion detection model to effectively classify dynamic network traffic outside the sandbox. The kernel consisted of a four-layer network, and each layer contained 136 neurons. To analyze the effectiveness of the suggested approach, numerous experiments were carried out with various hyperparameter combinations, and the results were compared with those of other shallow and deep ANN models. They used CICIDS2017 and NSL-KDD datasets with standard performance metrics for this assessment. They then created and tested 36 alternative DNN model combinations, each producing different outcomes. To identify unknown DDoS attacks, [PERSON] et al. [29] created a method that employed reconstruction error and distributed hidden layer features. The deep hierarchical reconstruction nets (DHRNet) structure was used in this research to recompile it with a 1D interconnected neural network using a spatial location constraint prototype loss function. A random gradient descent approximation-based one-class SVM (support vector machine) was implemented to identify the unidentified patterns in the following stage. The performance of this approach was assessed using the CICIDS2017 Friday Open Dataset. [PERSON] et al. [30] developed a system for detecting DDoS attacks by employing various machine learning and feature selection algorithms. The initial step involved selecting the most relevant attributes from the network-IoT datasets using dual machine learning (ML) approaches, namely correlative and mutual information random forest. Subsequently, the attack detection was carried out using an ensemble-weighted voting method and then scored with random forest (RF) algorithms. The performance of the system was assessed using evaluation metrics and coefficient metrics confirmed the higher true positives of the target class. [PERSON] et al. [31] utilized the UNSW-NB15 dataset to evaluate the effectiveness of machine learning algorithms for network intrusion detection. They compared the performance of various classifiers, including random forest, support vector machines, and neural networks, using a range of features extracted from the dataset. The results demonstrated that the random forest classifier outperformed other algorithms, achieving an accuracy of 95% and a low false-positive rate.