# LAN Intrusion Detection Using Convolutional Neural Networks
## 1 Introduction
Intrusion Detection Systems keep track of network activity and perform thorough network analyses. They may also detect potential assaults (anomalies) as well as unwanted network access. An IDS, in its most basic form, is computer software or program(s) that collects and evaluates numerous security criteria (metrics or parameters) pertaining to a network. When the term \"intrusion detection system\" is used, three major methodological ideas come to mind: misuse detection, anomaly detection, or a combination of the two. When there is a divergence from a previously specified network state, the IDS labels it as anomaly detection. Anomaly detection is detecting behavior that is considerably different from normal activity. Misuse detection, on the other hand, compares the attack behaviors used to break into systems with the user activity that has been recorded. It is important to remember that intrusions might come from outside the network (outsider assaults) or from authorized users who want more power (insider attacks). Intrusion might also be privileged users trying to abuse their power. Anomaly detection in computer networks and on the internet as a whole has been the subject of numerous notable papers by security experts. There are two types of IDS, based on signatures or anomalies, as shown in Figure 1. An IDS based on signatures identifies attacks using a huge database of known attack signatures. It can only detect known assaults and has no way of detecting novel ones. The IDS approach to anomaly detection creates a statistical model to characterize typical network flow. An anomaly is defined as network traffic that deviates from the model pattern, and such behavior is identified as an intrusion. An IDS is utilized to identify and categorize attacks or violations of security-related protocols at host- and network-level architecture. Depending on the invasive behavior, Intrusion Detection Systems are based on network or host. A network-based IDS (NIDS) is based on network activity. Network activity is gathered through network monitoring equipment such as switches, routers, and other network devices. A host-based IDS (HIDS) is utilized on local host computer activity. The HIDS operates with a variety of log files that are gathered by sensors from the local host computer. The NIDS monitors every packet in the network, whereas the HIDS relies on log files and user account information. The majority of companies utilize a combination of NIDS and HIDS. Convolution Neural Network is a subset of deep learning and is becoming a popular method of identifying sophisticated assaults with unusual patterns [11]. Deep learning gives automated techniques for extracting deep features. It provides a more accurate representation of data, allowing for the creation of better models. Intrusion Detection Systems benefit from the use of deep learning algorithms. Several researchers have focused on deep learning IDS techniques since the release of the KDD CUP 99 dataset in 1999. The Defense Advanced Research Projects Agency's (DARPA) KDD dataset is most often used for IDS evaluation. Denial of Service, Probe, User to Root, and Remote to Local are four categories of attack data in KDD. Deep learning algorithms are based on the structure, function and functioning of biological neurons in the human brain [12]. Natural language processing, medical research, speech recognition, picture recognition, aerospace and military, and other sectors have seen considerable success using DL. DL-based algorithms, in contrast to typical ML algorithms, deal with big datasets with a variety of attributes (inputs). Some of the traits are necessary to solve a specific categorization challenge, while others are unnecessary and redundant. Furthermore, datasets with a large number of feature vector dimensions are difficult to train and test. NSL-KDD dataset is a military network environment created with a set of attack and TCP/IP data to extend an Air Force LAN (Local Area Network).