# LAN Intrusion Detection Using Convolutional Neural Networks
## 2 Literature Review  
In the current paper, [PERSON] et al. [20] proposed a network IDS for cloud settings that detects intrusions and assaults utilizing a Multi-layer Perceptron Neural Network and the Particle Swarm Optimization (PSO) algorithm. The PSO method was used to identify the optimal biases and weights of the MLP, and then trained utilizing training data and the obtained optimal weights. NIDS is implemented in the network, which is directly attached to the cloud's computing; meanwhile, other NIDSs were set up on the processing servers for maximum efficiency and security. Most of the NIDSs report incidents of attacks to a main processing server with a lot of database space, and the suggested system would use these data if needed. The results of optimizing the neural network using the PSO algorithm revealed a significant enhancement in the functionality of the MLP NIDS in order to detect attacks with greater precision and reducing time complexity. NSL-KDD and KDD CUP datasets were used to test the algorithms. The results revealed an increase in the accuracy of detecting unauthorized user security breaches. [PERSON] and [PERSON] [21] suggested a method based on the Fuzzy C Mean (FCM) algorithm in a cloud data center to recognize intrusion events from normal behavior and to safeguard network-accessed cloud services and resources that are vulnerable to numerous forms of threats and assaults. The purpose of the Fuzzy Clustering algorithm is to divide the dataset into two different clusters: assault and normal activity. The described module is separated into two parts: (1) training, in which the optimal cluster centers are produced, and (2) testing, in which cluster centers are produced with the help of the training stage to identify fresh, unseen samples. The FCM method was trained and evaluated using the KDD CUP 99 dataset. The system has a high detection rate and a low false positive alarm rate. [PERSON] et al. [22] show a distributed ML-based technique for cloud computing services. The method comprises five components: (1) network traffic of cloud data collector; (2) network traffic of data pre-processing; (3) anomaly detection; (4) network traffic synchronization; (5) assault traffic categorization. The suggested solution is intended to be installed in the cloud computing alongside the provider's edge networking devices. This enables the physical layer's edge network routers to capture incoming packets. The suggested approach is analysis using the public dataset CIDDS-001 in a series of experiments on the Google Cloud Platform. Compared with the normal Random Forest classifier, the results are adequate. The system had a 97% average accuracy, a 0.21% average false positive rate, and a 6.23 s average running time. BNID at the network layer in the cloud environment was proposed by [PERSON] et al. [23] as a flexible intrusion detection approach. The authors created a security architecture for deploying BNID at the cloud network node to protect cloud machines against network threats. At the network layer, BNID collects the behavior of virtual machines' communication, analyses it, and detects harmful network patterns. A cloud administrator investigation report is created. The actual results based on the dataset reveal that BNIDS outperforms the Decision Tree and Naive Bayes techniques. The attack dataset from our solution is validated using the Information Technology Operations Center (ITOC). BNID has an accuracy at 98.88% at a false positive rate of 1.57%, which is adequate. [PERSON] et al. [24] studied the use of a DNN, a type of DL model, to build an effective and flexible IDS for detecting and categorizing unexpected and surprising cyber attacks. Because of the ongoing change in network behavior and the rapid evolution of attacks, it is essential to analyze a large number of datasets collected over time utilizingdynamic and static approaches. The research aids in the identification of the optimal algorithm for predicting upcoming cyber attacks.