Machine Learning Tutorial

What is Machine Learning? Machine Learning Life Cycle Python Anaconda setup Difference between ML/ AI/ Deep Learning Understanding different types of Machine Learning Data Pre-processing Supervised Machine Learning

ML Regression Algorithm

Linear Regression

ML Classification Algorithm

Introduction to ML Classification Algorithm Logistic Regression Support Vector Machine Decision Tree Naïve Bayes Random Forest

ML Clustering Algorithm

Introduction to ML Clustering Algorithm K-means Clustering Hierarchical Clustering

ML Association Rule learning Algorithm

Introduction to association Rule Learning Algorithm

Miscellaneous

Top 5 programming languages and their libraries for Machine Learning Basics Vectors in Linear Algebra in ML Decision Tree Algorithm in Machine Learning Bias and Variances in Machine Learning Machine Learning Projects for the Final Year Students Top Machine Learning Jobs Machine Learning Engineer Salary in Different Organisation Best Python Libraries for Machine Learning Regularization in Machine Learning Some Innovative Project Ideas in Machine Learning What is Cross Compiler Decoding in Communication Process IPv4 vs IPv6 Supernetting in Network Layer TCP Ports TCP vs UDP TCP Working of ARP Hands-on Machine Learning with Scikit-Learn, TensorFlow, and Keras Kaggle Machine Learning Project Machine Learning Gesture Recognition Machine Learning IDE Pattern Recognition and Machine Learning a MATLAB Companion Chi-Square Test in Machine Learning Heart Disease Prediction Using Machine Learning Machine Learning and Neural Networks Machine Learning for Audio Classification Standardization in Machine Learning Student Performance Prediction Using Machine Learning Data Visualization in Machine Learning How to avoid over fitting In Machine Learning Machine Learning in Education Machine Learning in Robotics Network intrusion Detection System using Machine Learning Backpropagation Algorithm in Machine Learning

Network intrusion Detection System using Machine Learning
2024-07-11 06:41:16

Network intrusion Detection System using Machine Learning

Introduction:

Security is one of the most important issues due to technology's quick growth and growing importance in today's linked society. Intrusion detection systems, or IDS, are regarded as one of the best systems available for identifying various types of assaults. With the use of machine learning (ML), we have attempted to provide a thorough analysis of network intrusion detection system (NIDS) methodologies in this work. The most current ML techniques and approaches used by NIDS, together with the common threats they may identify and their problems, have been highlighted based on our analysis of recent research.

Network intrusion detection system:

Digital technologies are currently being used more and more in every industry, but especially in business. One cannot overlook the internet's negative aspects in addition to its beneficial effects and continued rise in international communication. Attacks rise in tandem with the development of connected devices; threat actors are constantly a threat to networks. One of the most important things for every firm to do is to maintain security. The three most important foundations of network security.

The figure illustrates an average estimate of the financial damage resulting from cyberattacks, notwithstanding the difficulty in estimating the precise amount. One instance of this type of assault was the Distributed Denial of Services (DDOS)-based DYN cyberattack that occurred in 2016. The assault was directed at VPN filters and the systems run by DNS providers. Consequently, the malware was able to successfully infect around 500,000 Storage Area Network (SAN) devices and routers globally.

Difference between the IDS and NIDS

Establishing rules and designing security mechanisms—such as a firewall, virtual private network, authentication and authorization system, etc.—is the conventional method for protecting a computer network. These strategies, meanwhile, have been in place for a while, and attackers have discovered some methods to get around them. For the previously stated rationale, better protection and monitoring mechanisms were needed, and in the current landscape of network security, intrusion detection systems (IDS) are seen to be among the most popular and successful approaches. Generally speaking, intrusion detection systems (IDS) may be divided into two categories: host-based IDS and network-based IDS (NIDS and HIDS).

Table 1: Different between NIDS and HIDS

Type                Advantage        DisadvantageNIDSNIDS often resides in a network at a location where it can keep an eye on both inbound and outbound traffic.It is independent of the surroundings and does not affect the performance of the host.  IT has the potential to impact network capacity; furthermore, it is not capable of simultaneously detecting events taking place in several locations.Adversaries may render IDS unreliable by incapacitating its software.

It has restricted visibility on the host system and is unable to evaluate encrypted channel traffic.  HIDSIt is Locally host-based.Host visibility internally in the event of an attack, whether or not it is successful. These devices are capable of decrypting communications.  It must be installed on every server on a network, which might hinder system performance because these resource-intensive  

The two primary intrusion detection techniques for the IDS types listed in Table 1 are anomaly-based detection and signature-based detection. All incoming packets in signature-based detection are sent to a database with the signatures of known malicious patterns, sometimes called indicators of breaches. Network traffic is initially examined in anomaly-based detection to create a model of the network's typical characteristics. This model contains data on protocols, IP addresses, bandwidth use, and utilized ports. Next, any variation or abnormality in the network traffic is identified by comparing it with the predefined model. A growing number of manufacturers and security professionals view ML-based intrusion detection systems (IDS) as a way to address the increasing sophistication and difficulty of today's sophisticated assaults. It is customary for ML-based intrusion detection systems to be trained using pre-existing datasets from an emulated or simulated environment. Table 2 illustrates how differentiating an ML-based IDS's learning techniques may be.

Table 2: Types of ML Algorithms types:

Learning TypeDescriptionAlgorithmsSupervisedIn supervised learning, the desired outcome is predetermined. The algorithm is given the labeled dataset so that it can learn from it and build a model.
Classification and regression-based algorithms are two subcategories of supervised algorithms.Decision Trees,
· K-Nearest Neighbor
· Linear Regression
· Support Vector Machine
· Neural Networks  UnsupervisedIn unsupervised learning, the dataset is given without any labels. The algorithm self-learns and finds the concealed patterns to recognize each data type. The clustering algorithm, outlier detection, association rule mining, and autoencoders are further categories into which an unsupervised method may be divided.Apriori   -K-means clustering and other algorithms for mining association rules.  ReinforcedReinforced algorithms pick up knowledge through experiences and feedback techniques. It's possible to describe it as trial-and-error learning.  Q-Learning
· Deep Adversarial Networks
· Temporal Difference  Semi-SupervisedAlgorithms for semi-supervised learning employ both labeled and unlabeled data for learning. These methods are applied when it is difficult to label data or extract valuable characteristics (e.g., CT scans and MRIs, when professionals can identify some spots to indicate a medical issue but may not recognize every sign).  Generative models
· Low-density separation
· Graph-based methods
· Heuristic approaches  

The two categories of Classical Learning and Deep Learning may be used to further categorize the highlighted machine learning algorithms and methodologies in Table 2. Neural Network (NN) based techniques have become more popular as ML-based solutions have decreased recently. However, with recent technological improvements, computational needs are not a big worry. Generally, DNN-based techniques come with a high computational cost.

Framework of Machine Learning for IDS and NIDS

Hackers are using more advanced techniques to breach networks and systems in an attempt to obtain illegal access as the usage of technology grows. As a result, IDS is employed to identify such hostile attempts. It is necessary to properly analyze physical and logical intrusion detection systems (IDS) to identify intrusions based on attack signatures or by monitoring network traffic behavior. IDS are deployed in networks by topology and planning. The figure displays the fundamental features of the IDS.

But among IDS, network-specific attack detection and analysis is the specialty of network intrusion detection systems or NIDS. To view the whole network, NIDS is typically placed in conjunction with an aggregation switch. Figure shows a typical IDS implementation.

If malicious activity is detected, the NIDS notifies the administrator and creates event logs. The hybrid NIDS technique has gained popularity recently because of its successful detection. A hybrid network intrusion detection system combines anomaly- and signature-based detection. An overview of signature, anomaly, and hybrid NIDS is provided in Table 3.

Table 3: NIDS threat detection approaches

NIDSDescriptionsignature-oriented

The primary concept of signature-based intrusion detection systems is similar to that of virus scanners, making them suitable only for known threats. Signature-based intrusion detection systems look for patterns that are known to be hostile.

based on anomalies

These IDS detect alterations in typical network traffic patterns. It functions by locating authorized and known network traffic.

Mixed

All that hybrid IDS is is a combination of anomaly-based and signature-based protection. Due to the advantages and disadvantages of these two strategies.

What are the benefits of NIDS?

1. Intelligent Threat Identification:

Real-time detection of suspicious or malicious actions is possible thanks to NIDS, which continually monitors network traffic. This lessens the chance of data breaches, illegal access, and other security-related events.

2. Signature-Based Detection:

To detect known threats, NIDS employ preset attack signatures. The system sends out an alert when network traffic and a signature match. This method works effectively for identifying known assaults.

3. Anomaly-Based Detection:

To find abnormalities, NIDS also examines network activity. The system sounds a warning whenever it detects unusual activity (such as unexpected traffic patterns). This assists in locating novel threats and zero-day assaults.

4. Shorter Response Time:

NIDS allows for quicker issue response by delivering real-time notifications. Security personnel can minimize possible damage by acting quickly to neutralize risks.

5. Scalability:

NIDS can manage massive networks and keep an eye on several hosts at once. Scalability like this is necessary for enterprise-grade security.

6. Accounting and Compliance:

By keeping track of network activity and audit records, NIDS assist businesses in adhering to regulatory standards. For compliance with regulations like GDPR, HIPAA, and PCI DSS, this is essential.

7. Visibility:

Network intrusion detection systems (NIDS) give administrators insight into network traffic, enabling them to spot bottlenecks, maximize efficiency, and comprehend network utilization.

What are the challenges of implementing ML in NIDS?

There are many obstacles to overcome when integrating machine learning (ML) with network intrusion detection systems (NIDS):

  1. Packet Processing Efficiency: Given the large amounts of network traffic, ML-based NIDS needs to process packets effectively. Precise threat detection relies on effective packet processing1.
  2. Adversarial Attacks: An adversarial attack can be launched against ML models that are utilized in NIDS. These attacks change the input data to trick the model, which might result in false positives or false negatives2.
  3. Distribution Shifts: The performance of ML models can be impacted by network behavior or data distribution modifications. NIDS needs to adjust to changing threats and dynamic environments.
  4. Class Imbalances: Class imbalances in datasets used to train machine learning models can impact the precision of detection. When specific classes predominate in the training data, overfitting may result.
  5. Contant Evolution of Threats: As attack methods and strategies change, NIDS has to be updated and flexible.

Conclusion:

Network intrusion detection systems (NIDS) are greatly improved by machine learning since it make intelligent threat identification and response possible in real-time. ML-based NIDS provides scalable and reliable solutions for contemporary cybersecurity demands, despite obstacles including adversarial assaults, increasing threats, and packet processing efficiency. NIDS is vital to ensuring network security and compliance because it combines anomaly- and signature-based detection techniques with the latest developments in machine learning algorithms.

← Prev Next →