Automatic network intrusion detection: Current techniques and open issues (original) (raw)
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A Survey of Signature Based & Statistical Based Intrusion Detection Techniques
— This paper presents a comprehensive survey of some modern and most popular intrusion detection techniques. It is unrealistic to prevent security breaches completely using the existing security technologies. Detecting the presence of intruder is very crucial for maintaining the network security. It is found that most of the current intrusion detection systems (IDSs) are signature based systems. The signature based intrusion detection system are based on matching a signature with the network details. Provided with the signatures or patterns they can detect many or all known attack patterns but they are of little use for as yet unknown attacks. Rate of false positives is close to nil but these types of systems are poor at detecting new attacks or variation of known attacks or attacks that can be masked as normal behavior. The other type of IDS i.e. Statistical Based Intrusion detection System (SBIDS) can overcome many of the aforementioned limitations of signature based intrusion detection systems. The statistical based intrusion detection systems performs better than signature based intrusion detection system for novelty detection i.e. detection of new attack is very important for intrusion detection system. Researchers have implemented various classification algorithms for intrusion detection.
A Signature-based Intrusion Detection System (IDS) helps in maintaining the integrity of data in a network controlled environment. Unfortunately, this type of IDS depends on predetermined intrusion patterns that are manually created. If the signature database of the Signature-based IDS is not updated, network attacks just pass through this type of IDS without being noticed. To avoid this, an Anomaly-based IDS is used in order to countercheck if a network traffic that is not detected by Signature-based IDS is a true malicious traffic or not. In doing so, the Anomalybased IDS might come up with several numbers of logs containing numerous network attacks which could possibly be a false positive. This is the reason why the Anomaly-based IDS is not perfect, it would readily alarm the system that a network traffic is an attack just because it is not on its baseline. In order to resolve the problem between these two IDSs, the goal is to correlate data between the logs of the Anomaly-based IDS and the packet that has been captured in order to determine if a network traffic is really malicious or not. With the supervision of a security expert, the malicious network traffic would be verified as malicious. Using machine learning, the researchers can identify which algorithm is better than the other algorithms in classifying if a certain network traffic is really malicious. Upon doing so, the creation of signatures would follow by basing the automated creation of signatures from the detected malicious traffic.
A Comparative Analysis of Machine Learning Approaches to Intrusion Detection
Journal of Xi'an University of Architecture & Technology, 2021
Network administrators use a Network Intrusion Detection System (NIDS) to detect network security breaches in their enterprises. However, designing a convenient and dynamic NIDS for unanticipated and unpredictable attacks poses numerous obstacles. Signature-based Intrusion Detection Systems (IDS) are currently insufficient to handle the hazards posed by zero-day attacks to networked systems. On the NSL-KDD dataset, we applied data mining techniques and compared their performance on metrics such as accuracy, precision, and recall.
Automated Signature Creator for a Signature Based Intrusion Detection System
A Signature-based Intrusion Detection System (IDS) helps in maintaining the integrity of data in a network controlled environment. Unfortunately, this type of IDS depends on predetermined intrusion patterns that are manually created. If the signature database of the Signature-based IDS is not updated, network attacks just pass through this type of IDS without being noticed. To avoid this, an Anomalybased IDS is used in order to countercheck if a network traffic that is not detected by Signature-based IDS is a true malicious traffic or not. In doing so, the Anomalybased IDS might come up with several numbers of logs containing numerous network attacks which could possibly be a false positive. This is the reason why the Anomaly-based IDS is not perfect, it would readily alarm the system that a network traffic is an attack just because it is not on its baseline. In order to resolve the problem between these two IDSs, the goal is to correlate data between the logs of the Anomaly-based IDS and the packet that has been captured in order to determine if a network traffic is really malicious or not. With the supervision of a security expert, the malicious network traffic would be verified as malicious. Using machine learning, the researchers can identify which algorithm is better than the other algorithms in classifying if a certain network traffic is really malicious. Upon doing so, the creation of signatures would follow by basing the automated creation of signatures from the detected malicious traffic.
A Review of Automated Intrusion Detection Models
The aim of this paper is to review the working of various automated signature generation models for detection of previously unknown network attacks and polymorphic worms, which is based on anomalous payload of the network packets. We discuss the various types of worms, how they try to evade detection from static signature detection systems and the ability of eight different automated models to detect these worms.
Machine Learning for Network Intrusion Detection: A Survey
"Network intrusion detection system (NIDS)" monitors traffic on a network looking for doubtful activity, which could be an attack or illegal activity. The intrusion detection techniques based upon data mining are generally plummet into one of two categories: misuse detection and anomaly detection. In misuse detection, each instance in a data set is labeled as 'normal' or 'intrusive' and a learning algorithm is trained over the labeled data. In this paper we will discuss about the steps involved in NIDS, further we will compare different techniques of NIDS based on accuracy parameter i.e. precision and recall.
A Signature-based Intrusion Detection System (IDS) helps in maintaining the integrity of data in a network controlled environment. Unfortunately, this type of IDS depends on predetermined intrusion patterns that are manually created. If the signature database of the Signature-based IDS is not updated, network attacks just pass through this type of IDS without being noticed. To avoid this, an Anomaly-based IDS is used in order to countercheck if a network traffic that is not detected by Signature-based IDS is a true malicious traffic or not. In doing so, the Anomaly-based IDS might come up with several numbers of logs containing numerous network attacks which could possibly be a false positive. This is the reason why the Anomaly-based IDS is not perfect, it would readily alarm the system that a network traffic is an attack just because it is not on its baseline. In order to resolve the problem between these two IDSs, the goal is to correlate data between the logs of the Anomaly-based...
Diverse Methods for Signature based Intrusion Detection Schemes Adopted
International Journal of Recent Technology and Engineering (IJRTE), 2020
Intrusion Detection Systems (IDS) is used as a tool to detect intrusions on IT networks, providing support in network monitoring to identify and avoid possible attacks. Most such approaches adopt Signature-based methods for detecting attacks which include matching the input event to predefined database signatures. Signature based intrusion detection acts as an adaptable device security safeguard technology. This paper discusses various Signature-based Intrusion Detection Systems and their advantages; given a set of signatures and basic patterns that estimate the relative importance of each intrusion detection system feature, system administrators may help identify cyber-attacks and threats to the network and Computer system. Eighty percent of incidents can be easily and promptly detected using signature-based detection methods if used as a precautionary phase for vulnerability detection and twenty percent rest by anomaly-based intrusion detection system that involves comparing defin...
A Model to Detect Network Intrusion Using Machine Learning
2021
The attacks on computer security are becoming global and it is a very important security threats issues to the cyberspace, that if an organization is not mindful of, important data will be accessed, modified, or deleted. Computer security attackers utilize the compulsions and security weaknesses in the computer network and data system to carry out an attack, which ideals the divulgence of system information and the capture of the privacy of users and threaten data availability or integrity. The proposed system aim in developing a model to detect network intrusion using machine learning algorithm. The dataset consists of different categories of intrusions stimulated in a military environment. The dataset consists of a raw TCP/IP data for a network by stimulating a typical US Air Force Lan. The dataset is made up of 41 Columns. The class column consists of two types, which are Normal Network Packets and Anomalous Network Packets. The dataset was preprocessed by converting some feature...
Characterizing Intelligent Intrusion Detection and Prevention Systems Using Data Mining
Advances in Secure Computing, Internet Services, and Applications
Intrusion Detection and Prevention Systems (IDPS) are being widely implemented to prevent suspicious threats in computer networks. Intrusion detection and prevention systems are security systems that are used to detect and prevent security threats to computer networks. In order to understand the security risks and IDPS, in this chapter, the authors make a quick review on classification of the IDPSs and categorize them in certain groups. Further, in order to improve accuracy and security, data mining techniques have been used to analyze audit data and extract features that can distinguish normal activities from intrusions. Experiments have been conducted for building efficient intrusion detection and prevention systems by combining online detection and offline data mining. During online data examination, real-time data are captured and are passed through a detection engine that uses a set of rules and parameters for analysis. During offline data mining, necessary knowledge is extracted about the process of intrusion.