# LAN Intrusion Detection Using Convolutional Neural Networks
## 1 Introduction  
The phrase \"threat intelligence\" was coined to help security practitioners and analysts spot cyber attacks on computer networks. CTI (Cyber Threat Intelligence) is described as the method of gathering information, implementing statistical analysis to understand security risks, and identifying risk malware and exploits kinds, as well as creating defense mechanisms [1]. CTI allows security analysts to collect data, detect threats, and respond quickly with a more effective defensive strategy to neutralize cyber attacks before they exploit system weaknesses. The research community has established ways to display real-time working and safeguarding of individual and organizational data in this respect. Because attackers utilize varied (and unique) methods to target the victim, identifying and neutralizing them is a difficult undertaking. Attackers seek either to acquire personal data such as passwords, bank details, and mobile information or to obtain access to a person's device in order to carry out criminal operations. A cyber attack begins with reconnaissance of the target and concludes with exploiting weaknesses to complete the intended task [2]. As a result, cyber security has become a serious worry, as more people who do not have access to network security technologies have become webmasters. Network attacks, such as DoS (Denial of Service), Injection Assaults, and Brute Force, threaten a number of cloud servers. Many cloud hosts have been hacked and have become botnets or Bitcoin Trojan malware vectors. One of the most effective strategies to lessen the security risk is to provide unified protection for renters who are unaware of cyber security. In today's world, security is a critical concern. Intruders have proliferated substantially in recent years as the world's internet technology offerings have developed [3]. Over time, computer networks have grown, contributing to economic development. According to the ISTR (Internet Security Threat Report), malware is found in one out of every thirteen web queries. Spam in e-mail has climbed to more than 55%, internet dangers have risen to 46%, and ransomware has grown to 46% [4]. Cybercrime and threats have evolved into a serious menace. Because of this expansion, the necessity ofnetwork security has increased. IDS aids in the detection of threats by examining packets recorded from the network [5]. Many risks exist, such as Denial of Service (DoS), which introduces undesirable traffic into a network and denies or disables legitimate users from accessing resources. Malicious software, known as malware, also takes advantage of susceptibility in computer network systems [5]. The defense system (Intrusion Detection System) can alert users or block attacks when detecting network intrusions. More and more CSPs are using IDS to protect their tenants' cloud servers [6]. As a result, intrusion detection systems are required in the network security industry to prevent attackers from accessing information. In order to identify probable intrusions, an IDS monitors both incoming and outgoing traffic and activities. In Intrusion Detection Systems, ML algorithms are also applied. The ML algorithm is a method of data analysis in which the process of an analytical model is automated [7]. IDS provides sophisticated tools and technology for a variety of areas, and it has grown rapidly in the past two decades [8]. Traditional Intrusion Detection Systems tend to focus on enhancing detection performance rather than timeliness, making it difficult to detect massive amounts of data traffic in a short period of time. As a result, attackers have a window of opportunity. Furthermore, in recent years, network assaults involving massive volumes of traffic data have become unexpectedly widespread [9]. As a result, technologies for the real-time analysis of huge data traffic on high-transmission networks are critical. However, an Intrusion Detection System that works in real time is still in the beginning phase of research and is insufficient to reach a conclusion [10].