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A novel time efficient learning-based approach for smart intrusion detection system

Abstract

Background

The ever increasing sophistication of intrusion approaches has led to the dire necessity for developing Intrusion Detection Systems with optimal efficacy. However, existing Intrusion Detection Systems have been developed using outdated attack datasets, with more focus on prediction accuracy and less on prediction latency. The smart Intrusion Detection System framework evolution looks forward to designing and deploying security systems that use various parameters for analyzing current and dynamic traffic trends and are highly time-efficient in predicting intrusions.

Aims

This paper proposes a novel approach for a time-efficient and smart Intrusion Detection System.

Method

Herein, we propose a Hybrid Feature Selection approach that aims to reduce the prediction latency without affecting attack prediction performance by lowering the model's complexity. Light Gradient Boosting Machine (LightGBM), a fast gradient boosting framework, is used to build the model on the latest CIC-IDS 2018 dataset.

Results

The proposed feature selection reduces the prediction latency ranging from 44.52% to 2.25% and the model building time ranging from 52.68% to 17.94% in various algorithms on the CIC-IDS 2018 dataset. The proposed model with hybrid feature selection and LightGBM gives 97.73% accuracy, 96% sensitivity, 99.3% precision rate, and comparatively low prediction latency. The proposed model successfully achieved a raise of 1.5% in accuracy rate and 3% precision rate over the existing model. An in-depth analysis of network parameters is also performed, which gives a deep insight into the variation of network parameters during the benign and malicious sessions.

Introduction

The spread and susceptibility of cyberspace have necessitated its perpetual appraisal in terms of security. The world has witnessed enormous cyber-attacks such as data breaches that exposed millions of credit and debit card information to attackers, cyber warfare, corporate espionage, Internet of Things (IoT) attacks, social engineering attacks, crypto-jacking attacks, etc. All successful attacks exploited the prevalent vulnerabilities in the systems. Besides other security mechanisms like firewalls, secure information storage, authentication, and authorization techniques, an Intrusion Detection System (IDS) is also strongly recommended [1].

Some Intrusion Detection Systems observe network activity and warn if there is any suspicious event, while others also perform actions after detecting threats. IDSs are broadly classified into two categories, anomaly, and misuse, based on their detection criterion [2]. An anomaly-based IDS detects network intrusions by scrutinizing system activities and categorizing them as either normal or malicious. Most IDSs operate on misuse detection techniques i.e. looking and alarming for 'known patterns' of the detrimental activity. Although accurate, this kind of IDS limits itself by looking up the list of recognized attacks. Its primary disadvantage is that it will not be effective for protection from any new attack whose signatures are not previously integrated. This leaves a major security gap in the system that can be easily exploited by an attacker to fool the IDS. There is a dire need to upgrade such an IDS frequently to detect new attack signatures and the already known ones.

Machine learning and deep learning are commonly used techniques to integrate an IDS with intelligence, allowing easy detection of all kinds of attacks, thus safeguarding the systems from all sorts of threats [3]. However, to build an efficient machine learning model for intrusion detection, selecting the right dataset is key. Various machine learning techniques that exist today are applied to publicly available IDS datasets, namely, DARPA [4], KDD 99 [5], KOYOTO [6], and NSLKDD [7]. The major drawback of the designed systems using the above datasets is that these datasets are old and do not reflect modern-day traffic trends.

Furthermore, many researchers have proposed machine learning models for IDS, with most of them considering accuracy as the most critical metric for evaluating the proposed models. However, accuracy alone is insufficient to analyze a system's performance because IDSs make predictions in real-time. Besides accuracy, evaluating an IDS on the time it may take to make a prediction (a.k.a. prediction latency) is also essential. However, while increasing the prediction accuracy, most researchers have not measured its impact on prediction latency. Moreover, along with accuracy and prediction latency, a high-performing IDS should have a high true positive prediction rate. False-positive (misclassified as an attack) and false negative (misclassified as benign) cannot be treated equally. While false positives can result in additional system resources, false negatives can debilitate the entire system. Thus, along with accuracy, recall rate and prediction latency are very important for evaluating an IDS model's performance.

This paper proposes a novel machine learning approach to implement a fast and precise IDS using the latest CIC-IDS 2018 dataset to overcome the above research gaps. The major contributions of the paper are as follows.

  • A realistic IDS that can effectively detect the majority of modern-day attacks.

  • A hybrid feature selection approach optimized for low prediction latency.

  • An IDS model using proposed Hybrid Feature Selection and the fast Light GBM machine learning algorithm gives promising results with better accuracy, recall rate, and low prediction latency.

  • Deep insight into the comparison between the network parameters observed during the benign and the malicious sessions.

The rest of the paper is organized as follows—section “Related work” reviews existing literature on intrusion detection using machine learning. Section “Research methodology” discusses the methodology for the research including an extensive description of data preparation steps. Section “Results and analysis” discusses the results. The paper concludes with a summary of the findings in Sect. “Conclusion”.

Related work

Machine learning techniques [8] are widely used for building Intrusion Detection Systems. In this context, classification refers to the process of using machine learning algorithms to identify normal versus malicious activity within a dataset, representing network traffic, for designing an anomaly-based IDS. Zhou et al. [9] proposed an intelligent Intrusion Detection System based on feature selection and ensemble classifier. They proposed the CFS-BA Ensemble method for multi-attack classification that uses correlation for feature selection, then the ensemble classifier based on c4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) with Average of Probabilities (AOP) rule. They claimed that the classifier gives 99% accuracy for NSL KDD and CIC-IDS 2017 Dataset. The major drawback of the proposed system is that the author has not evaluated the proposed model in terms of time efficiency. Saleh et al. [10] suggested a hybrid Intrusion Detection System based on prioritized k-Nearest Neighbors (kNN) and optimized Support Vector Machines (SVM) classifiers on three intrusion detection datasets: KDD Cup99, NSL-KDD, and koyotto 2006 + datasets. This hybrid Intrusion Detection System uses the Naïve Bayes feature selection method for dimensionality reduction of the data set and an optimized SVM for outlier detection. A prioritized kNN classifier is then used for classification. The proposed method comprises 4 modules (i) Data pre-processing Module (DPM), (ii) Feature Selection Module (FSM), (iii) Outlier Rejection Module (ORM), and (iv) Decision-Making Module (DMM). The model uses feature effect identification and mutual effect identification to select relevant features based on accuracy, and training, and testing time. The major drawback of the suggested model is that old datasets were used by the author for evaluating the performance of the given model, these datasets do not reflect modern traffic patterns. Further, there are better time-efficient machine learning algorithms than those presented by the author.

The majority of the intrusion detection datasets are skewed, so many researchers have proposed techniques to balance the dataset to enhance the detection rate. Karatas et al. [11] proposed a model that used SMOTE oversampling technique to balance the skewed classes in the CIC-IDS2018 dataset. The samples of the skewed classes are increased proportionately to the average sample size. Using this technique, they claim to have achieved an accuracy of 99% using RF, Decision Tree (DT), Adaboost, K Nearest Neighbor (KNN), Gradient Boosting (GB), and Linear Discriminant Analysis (LDA). Techniques such as the Genetic Algorithm (GA) are also widely used in intrusion detection models. Though the author claims to have achieved 99% accuracy rate but, the suggested model is not evaluated for time-based metrics. Aslahi-Shahri et al. [12] proposed a model that used the Genetic Algorithm- Support Vector Machine (GA-SVM) feature selection method. A hybrid algorithm is used for feature selection. The GA divides the selected features into three priorities. These features are further used for classification. With this approach, the authors claim to have achieved a true positive value of 0.973 on the KDD cup99 dataset. The proposed model is evaluated on the KDD cup99 dataset which fails to reflect the modern-day traffic patterns.

Many deep learning approaches are also proposed for building an efficient IDS. Lin et al. [13] proposed a dynamic anomaly detection system that used Long Short-Term Memory (LSTM) and Attention Mechanism techniques to train the neural network. They have used the latest CIC-IDS 2018 dataset. Though the proposed approach by the author achieved an accuracy rate of 96.2% but the model is not evaluated for time efficiency. Kanimozhi V, Prem Jacob [14] proposed an Artificial Neural Network model for detecting Botnet attacks using the CIC-IDS 2018 dataset. They claimed to have achieved an accuracy score of 99.97%. And an average area under ROC (Receiver Operator Characteristic) curve 0.999. Though the model achieved high accuracy score of 99.97% but it is only for botnet detection. Moreover, the model is not evaluated in terms of time efficiency. Ma et al. [15] proposed SCDNN, based on Spectral Clustering (SC) and Deep Neural Network (DNN) algorithms. The proposed method was evaluated using the KDD-Cup99 and NSL-KDD datasets and a sensor network. The authors claimed that their proposed approach outperforms Backpropagation Neural Network (BPNN), SVM, RF, and Bayes tree models in attack detection accuracy. The disadvantage of the SCDNN is that its weight parameters and DNN layer thresholds must be tuned, and the clusters' k and parameters must be determined empirically rather than theoretically. Furthermore, the model is tested using outdated datasets, and the suggested model's time efficiency is not measured. Ferrag et al. [16] compared several deep learning techniques. Performance of DNN, Recurrent Neural Network, Restricted Boltzmann Machine, Deep Belief Networks, Convolutional Neural Networks, Deep Boltzmann Machine, and Deep autoencoders was compared on the latest CIC-IDS 2018 dataset. The experiment was carried out on just 5% of the complete dataset. The imbalance concerns in the skewed dataset were not addressed using any approach. Furthermore, the deep learning models were only assessed for recall rate and accuracy. Additional metrics like precision rate and F-Measure were missing. Atefinia and Ahmadi [17] proposed a DNN comprising a feed-forward module, a restricted Boltzmann machine, and two Recurrent Neural Networks. The model was trained on the latest CIC-IDS 2018 dataset. No technique was used to balance the highly skewed CIC-IDS 2018 dataset. The results of the proposed approach on some of the attack categories in the CIC-IDS dataset were also missing. Vinayakumar et al. [18] proposed distributed deep learning model for attack detection in network-based intrusion detection system (NIDS) and host-based intrusion detection system (HIDS). The authors evaluated the efficacy of various machine learning algorithms and DNNs on various NIDS and HIDS datasets. A scalable hybrid intrusion detection framework called Scale-Hybrid-IDS-AlertNet (SHIA) was proposed by the author. The proposed framework is used to process a large amount of network-level and host-level events to identify various malicious characteristics and send appropriate alerts to the network administrator. In the given approach the highly skewed CIC-IDS 2018 dataset was not balanced using any method. The findings of the suggested method on several of the attack categories were also missing.

Advance learning algorithms such as Particle Swarm Optimization (PSO) and Extreme Learning are also used by some of the authors to enhance the efficiency of the Intrusion Detection Systems. Roshan et al. [19] proposed adaptive and online network intrusion detection systems using Clustering and Extreme Machine Learning. The proposed Intrusion Detection System consists of three parts, the Clustering Manager, the Decision-Maker, and the Update Manager. The Clustering Manager is used to cluster the training data, and the Decision-Maker is used to evaluate the clustering decisions and provide a correction proposal to the update manager. The suggested system is tested on the NSL-KDD dataset, which is now obsolete. Ali et al. [20] proposed PSO-FLN, a fast learning model (FLN) based PSO. The performance of the proposed model is evaluated using the KDD99 dataset. The author claimed that the proposed approach outperformed various learning approaches. The suggested approach was unable to detect all forms of attacks and the model's time efficiency was also not assessed.. Aburomman and Ibne Reaz [21] proposed the Support Vector machine—K Nearest Neighbor—particle swarm optimization (SVM-KNN-PSO) ensemble method for intrusion detection. They proposed an ensemble-based approach using experts. Each expert consists of five binary classifiers. The expert's opinion is considered for every class. The voting is repeated for every observation for each classifier in the expert. Weighted majority voting is used to combine the results from various experts. The suggested model is tested using the KDD99 dataset, which does not reflect the current traffic trends.

Jin et al. [22] proposed a real-time intrusion detection system based on a parallel intrusion detection mechanism and LightGBM. The proposed model uses two approaches to achieve time efficiency without compromising the attack detection accuracy. Firstly, a light gradient boosting machine (LightGBM) is used as the intrusion detection algorithm. Secondly, parallel intrusion detection is used to effectively analyze traffic data. Swift IDS is based on parallel intrusion detection algorithms that have communication and coordination overheads. Moreover, the proposed model is stable with a network speed of up to 1.26 Gbps.

The above research analysis is summarized in Table 1.

Table 1 Survey of various Intrusion Detection System approaches

As listed in the above table, the majority of the research in this field is on old datasets that do not reflect modern-day attacks. Many researchers have considered accuracy as the most important metric to evaluate the performance of IDS, whereas sensitivity is a better metric as the impact of false positives and false negatives on IDS varies significantly [23]. The majority of the previous research in this field has not evaluated the prediction time of classifying a request as benign or malicious. Delays in the classification process can significantly affect the system's performance and hamper the user experience. To overcome the above shortcomings in this field's previous research, we propose a machine learning model that can detect modern-day attacks with a high attack detection rate and a low prediction latency.

Research methodology

The study follows the standard procedure in machine learning: (1) data collection, (2) data preparation, and (3) training a model on the data and evaluating model performance.

Data collection

CIC-IDS 2018 dataset was generated by Communications Security Establishment (CSE) & the Canadian Institute for Cybersecurity (CIC). The KDD CUP 99 and NSL-KDD datasets list a few attack categories. In contrast, CIC-IDS 2018 dataset lists a new range of attacks generated from real-world traffic features such as Distributed Denial of Service, Denial of Service, Brute Force, XSS, SQL Injection, Botnet, Web Attack, and Infiltration. This dataset is comprehensive, and it overcomes all the shortcomings of the previously used suboptimal datasets. CIC-IDS 2018 is a massive dataset that reflects 14 modern-day attacks having more than 16 million rows and 80 features [24]. Some of the features are listed in Table 2. The complete list of the features is given in Appendix 1.

Table 2 Some of the features in the CIC-IDS 2018 Dataset

The CIC-IDS2018 dataset is a mix of malicious and benign traffic. The distribution of various attacks in the dataset is given in Fig. 1. The different attack categories present in the dataset are listed in Table 3.

Fig. 1
figure1

Distribution of different categories of attacks in the dataset

Table 3 The attacks in the dataset are broadly classified into 5 categories

Data preparation

Data preprocessing

In this initial stage, the dataset was pre-processed. Data wrangling was performed on the complete dataset to prepare the data for further computation. The dataset was then relabeled into just two classes: attack and benign. The null values were dropped from the dataset, reducing the count from 16.2 million to 16.1 million. Four columns, Timestamp, IP address, Flow Id, and Source Port were dropped from the dataset. The timestamp column recorded the attack time, whereas the IP address recorded the IP address of the source and the destination machine. The trained models should not be biased against the IP address or time of the attack, so both the columns are dropped. The Source port column that had the port no of the source machine from where the attack is originated is also dropped.

As CIC-IDS 2018 dataset was highly skewed with 2,746,934 attacks and 13,390,235 benign samples. Normal traffic data in the dataset is under-sampled to balance the dataset that decreases the imbalance to an acceptable level, as given in Table 4. Further, the outliers of the dataset are removed using the Isolation Forest technique using 0.1 contamination. After outlier removal, the dataset is reduced to 5.5 million samples.

Table 4 Attack and benign samples before and after data preprocessing

Feature selection and dimensionality reduction

The basic and the most important step to build a machine learning model is feature selection. The main objective of feature selection is to select relevant features that can contribute to make the right prediction [9]. Feature selection and reduction of the undesired features in a dataset is one of the most important factors that affect the efficiency of a classifier [25]. Unnecessary features in a model can not only decrease the accuracy but can also increase the prediction time. Therefore, feature selection is a vital step while designing a machine learning model as the dropping of important features as well as the inclusion of unnecessary features can affect the system's performance.

There are numerous methods and techniques to minimize the data size [26]. Feature selection can be mainly classified into filter method, wrapper method, embedded method. Filter methods use statistical tests like Fisher Score, Correlation, ANOVA (Analysis of Variance). The wrapper method is based on the performance of the model on the dataset. The wrapper method includes Forward Selection, Backward Elimination, and Exhaustive Feature Selection techniques. The embedded approach combines the Filter and the Wrapper method by performing feature selection and classification simultaneously. Besides the various feature selection techniques, dimensionality reduction can be used for high dimensional datasets for reducing the number of inputs to the model. In this section, we propose a novel hybrid approach using Random Forest and Principal Component Analysis (PCA) to minimize the data set size while retaining important information.

Minimizing prediction latency using Hybrid Feature selection with Random Forests and PCA

An Artificial Intelligence(AI) model is useful only if it can be readily deployed in the real world. Predictions can be made both online and offline depending upon the application's context where the AI model is used. In synchronous online real-time predictions where the sequence of further steps depends on the model's prediction, time is also a very important parameter as prediction latency can significantly hamper the overall performance of the system. Prediction latency can be reduced at two levels: the model level and the serving level. At the model level, the latency can be decreased by reducing the number of input features or lowering the model's complexity. At the serving level, the prediction lag can be reduced by caching the predictions. As intrusion detection requires forecasts in real-time, the proposed method aims at reducing the prediction lag at the model level. The number of features selected has a considerable impact on the execution time [27]. So, to reduce the prediction lag, hybrid feature selection is proposed that decreases the no of input features while retaining the important information.

In this hybrid approach, first features are selected using Random Forests, and then dimensionality reduction is applied using PCA. The proposed method uses Random Forest for feature selection in the first step as Random Forest gives the highest accuracy as given in Table 9 followed by dimensionality reduction using PCA as PCA gave the least prediction latency. Feature selection for dimensionality reduction is applied to get better results by removing irrelevant features and redundant information [23]. The proposed hybrid approach is faster in comparison to both Random Forests and PCA used individually. The approach follows the steps below.

  1. 1.

    Select the relevant features using Random Forests.

  2. 2.

    Compute principal components for the selected features in step 1 using PCA.

  3. 3.

    Select the topmost significant principal components for further training the model.

Feature selection using Random Forests

Many feature selection techniques are available but our proposed method using Random Forest as a part of the hybrid feature selection process due to the high accuracy obtained using this technique as listed in Table 9. Random Forests technique uses a collection of decision tree classifiers. Each tree in the forest is built by training it on a bootstrapped sample from the original dataset. The split attribute in the individual trees is purely random and divides the dataset further into two classes based on impurity. Gini index or Information gain/entropy are used as impurity measures to split the data. While training the tree, it can be calculated how much each feature in the dataset affected the tree's impurity. Feature importance is calculated based on the aggregate impurity measure of each feature in a tree in the forest.

By using this method on the dataset, 37 most important features were extracted. These features are listed in Table 5.

Table 5 Selected features using Random Forests feature selection in the CIC-IDS 2018 Dataset

Dimensionality Reduction using Principal Component Analysis (PCA)

To solve the problem of high dimensional datasets, the dimensionality reduction technique [28] is used. PCA technique is used to compress the massive dataset features into a smaller subspace maintaining all the valuable information. PCA intends to discover the direction of max variance in high-dimensional information, and it reduces it into another subspace with equivalent or fewer measurements than the first one. Supposing that x is an eigenvector of the covariance matrix of PCA, the feature extraction result, for x, of an arbitrary sample vector a is

$${\text{z}} = {\text{a}} ^{T} x = \sum\nolimits_{i = 1}^{N} {a_{i} x_{i} }$$
(1)

where x = \(\left[ { x_{1 \ldots } x_{N} } \right] ^{T}\), a = \(\left[ { a_{1 \ldots } a_{N} } \right] ^{T}\) and N is the dimensionality of sample vectors [29].

Steps for using PCA for dimensionality reduction are as follows:

  1. 1.

    Scaling The Continous Values

    It's essential to scale the continuous variables before calculating PCA as the method is quite sensitive to variance of values in different variables.

  2. 2.

    Calculate The Covariance Matrix

    The covariance matrix is calculated to identify any relationship between the variables

  3. 3.

    Calculate The Eigenvalues And Eigen Vectors To Find The Principal Components

    Eigenvalues and eigenvectors are computed from the covariance matrix to calculate the given information's principal components.

  4. 4.

    Feature Vector

    Based on the eigenvalues and eigenvectors calculated in step 4, the most significant principal components are selected for further processing.

PCA is applied on the dataset with 37 features selected after using the Random Forests approach. In this hybrid approach, 99.9% explained variance was given by 24 individual principal components. Whereas, when only PCA was applied to the entire CIC-IDS 2018 dataset, 40 significant principal components gave 99% variance.

Thus, the number of principal components dropped from 40 principal components to 24 principal components. This hybrid approach reduces the number of principal components by 40% in comparison to using just PCA. In comparison to Random Forests feature selection, the input to the model is reduced from 37 features to 24 principal components, giving a decrease of 36.35%.

Training the classifier

For AI-based IDS to detect abnormal traffic trends, the system can be trained using machine learning algorithms. The literature offers various machine learning approaches. In this paper, five machine learning algorithms, namely, Random Forests, Extra Trees, XGBoost, KNN, Histogram Gradient Boosting, KNN, and LightGBM, are compared for Accuracy, Recall, Sensitivity, Specificity, F-Measure, model Building Time, and the Prediction Latency. The following paragraphs briefly discuss the machine learning algorithms.

Random Forest

Random Forests(RF) was propounded by Breiman [30]. RF is an ensemble of decision tree classifiers where each tree contributes its vote to predict the result. Random forests are an effective tool in prediction. Random Forests do not overfit the data as per the law of large numbers. Random forest is built by combining the results of decision trees in the forest. Each decision tree is trained on a randomly selected column of a bootstrapped subset of the original dataset. The model is then cross-validated using the out-of-bag samples.

Extra trees

Extra trees are Extremely Randomized Trees that use ensemble techniques to aggregate numerous decor-related trees in the forest for the final output [31]. It is different from other tree-based ensembles as the cut points for splitting the nodes are selected absolutely randomly, and trees are not grown on a bootstrapped sample instead on the complete learning sample. Extra trees are computationally more efficient than Random Forest.

XGB classifier

XGB is Extreme Gradient Boosting, basically, a tree-based ensemble that uses Gradient Boosting for enhancing speed and performance. XGB optimizes the Gradient Boosting by Tree Pruning, Regularization, Parallel Processing, and handling missing values to avoid overfitting. It uses parallel tree learning based on a Sparsity-Aware algorithm and a Cache-Aware block structure for tree learning. It supports Gradient Boosting, Stochastic Gradient Boosting, and Regularized Gradient Boosting with L1 and L2 regularization. XGB computes much faster with lesser resources, 10× faster than the scikit library [32].

LightGBM

LightGBM is a boosting framework proposed by Microsoft in 2017. This framework features improved performance, speed, and power than Xgboost. Under the hood, Microsoft's new model is a collection of multiple Decision Trees. Its method to calculate the variation gain is different from other Gradient Boosting Decision tree models. LightGBM's method occurs under strong and weak learners (big and small gradients, gi). For arranging the training instances, the absolute value of their gradients is used, organized in descending order. LightGBM uses Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) for faster processing and is 20 times faster in comparison to Gradient Boosting Decision tree with the same Accuracy [33].

Histogram based gradient boosting

Gradient Boosting is an ensemble of decision trees but is slow for training the models. But the training process of the trees can be significantly enhanced by binning the continuous input variables. Gradient boosting that uses the binning technique to speed up the model training are histogram-based Gradient Boosting ensembles. It is inspired by LightGBM by Microsoft and is currently available in the scikit library.

k- nearest neighbor (KNN)

kNN [10] is a supervised machine learning algorithm. A class in the kNN classifier is predicted based on the most frequent classes among the k neighbors. The k nearest neighbors are selected based on the distance between the data point and the original samples in the dataset. Euclidean, Manhattan, or Minkowski functions may be used for calculating the distance between points as given in the equations below.

$${\text{Euclidean}} = \sqrt {\sum\nolimits_{p = 1}^{N} {\left( {x_{p} - y_{p} } \right)^{2} } }$$
(2)
$${\text{Manhattan}} = \sum\nolimits_{p = 1}^{N} {\left| {x_{p} - y_{p} } \right|}$$
(3)
$${\text{Minkowski}} = \left( {\sum\nolimits_{p = 1}^{N} {\left( {\left| {x_{p} - y_{p} } \right|} \right)^{k} } } \right)^{\frac{1}{k}}$$
(4)

N is the dataset's size, p is an integer with positive values, and xp and yp are the data coordinates.

Evaluation metrics

The proposed system's performance is evaluated using metrics: Accuracy, Precision, Recall, F- Measure, model Training Time, and Prediction Latency. These metrics are calculated with the following Eqs. (5, 6, 7, 8, 9) using different negative and positive cases as TP—True positive, TN—True Negative, FP—False Positive, N—alse Negative.

Accuracy is the percentage of samples correctly predicted as benign and attack.

$$Accuracy = \frac{TP + TN}{{TP + FN + TN + FP}}$$
(5)

Sensitivity is the percentage of samples correctly classified as attacks out of all the attack samples.

$$Senstivity = \frac{TP}{{TP + FN}}$$
(6)

Specificity is the percentage of samples correctly classified as benign out of all the benign samples.

$${\text{Specificity}} = \frac{TN}{{TN + FP}}$$
(7)

Precision is the percentage of correctly classified samples of attacks out of all the samples classified as attacks.

$$Percision = \frac{TP}{{TP + FP}}$$
(8)

F-Measure is the harmonic mean of both precision and recall.

$$F - Measure = 2*\frac{Precision*Senstivity }{{Precision + Senstivity}}$$
(9)

Security managers tend to eliminate false negatives in a system for greater security and improved results, even if false positives are increased [23]. False positives result in the use of additional resources, whereas false negatives can debilitate the entire system. Prediction latency for classification is also of paramount importance in real-time Intrusion Detection systems as delay in classification can greatly hamper the user experience. Thus, for building an efficient IDS, it is essential to strike the right balance of attack prediction efficiency and prediction latency.

Model building time and prediction latency are calculated for all the models. The time is calculated by finding the difference between the start and the end time on the server while training and testing the classifier in each fold during tenfold cross-validation. The time is calculated for the training and testing of 552,345 samples in each fold.

$${\text{Prediction latency}}\, = \,{\text{Average time taken to predict the class for test set in each fold}}{.}$$
$${\text{Training time}}\, = \,{\text{Average time taken to train the model per fold}}{.}$$

Prediction performance and the prediction latency of the trained models using different feature selection techniques and machine learning algorithms are compared in Sect. “Result and analysis”.

Experimental setup

All the experiments are conducted on the latest CIC-IDS 2018 dataset. k-fold Cross-Validation with the value of k as 10 was used to reduce the uncertainty of the findings due to the random generation of training and testing samples. Each fold was further divided into training and testing samples. The proposed system was implemented using the Scikit Library in Python. All the experiments are performed on the cloud using AWS. The cloud instance configuration used for the experimentation is 32 vcpu(Virtual CPU) and 256 GB RAM. Complete experiment environment details are given in Table 6.

Table 6 Experiment environment configuration

Results and analysis

Descriptive analysis of CIC-IDS 2018 dataset

In this section an in-depth analysis of features of the dataset was performed. This paper lists a comparison of 10 features. This analysis has given a deep insight into how malicious and benign traffic varies in various network attacks.

Feature analysis

  1. a)

    Destination port The destination port is the port no of the target machine. Figure 2 exhibits the port numbers used during various attacks. It is observed that port 80 is the most attacked port as approximately 65% of attacks were made on this port. 98% of BOT attacks were on port 8080. 75% of Brute Force attacks were performed on port 80, 20% on port 500, and remaining on various other ports. 97% of the Brute Force XSS sessions were performed on Port 80. All DoS, DDoS, FTP Bruteforce, and SQL Injection attacks were performed on port no 80. All SSH Brute Force attacks used port no 22. Infiltration attacks were performed using various ports. The top 10 ports used during the infiltration attacks are listed in Fig. 3. Maximum infiltration attacks were performed on port no 53.

  2. b)

    Table 7 describes salient features identified in the CIC-IDS 2018 dataset, where Q1-First quartile- the lowest 25% of the values, Q2-Second quartile- values between 25.1% and 50%(median), Q3-Third quartile: values between 51 and 75% (above the median), Q4-The top 25% of values

Fig. 2
figure2

Ports used during various attacks

Fig. 3
figure3

Ports used in Infiltration attacks

Table 7 Analysis of salient Features in CIC-IDS 2018 Dataset

Key analytical outcomes of feature analysis based on CIC-IDS 2018 dataset are as follows:

  1. 1)

    65% of attackers used port no 80 to perform the attack

  2. 2)

    Benign sessions have a higher rate of flow Bytes/s in comparison to malicious sessions.

  3. 3)

    50% of attack sessions in the dataset had 0 flow bytes/s

  4. 4)

    50% of the malicious sessions had empty packets sent to the server

  5. 5)

    In 50% of the malicious sessions, no packet was sent back from the server to the attacker

  6. 6)

    The average number of packets in a sub-flow in the forward and backward direction is zero

  7. 7)

    The number of bytes sent in the initial window in the forward direction on average is higher in attack sessions in comparison to benign sessions

Results justifying reduced prediction latency using hybrid feature selection technique

In addition to improving optimization metrics, reducing the models' prediction latency is particularly necessary for machine learning models to be deployed in the real-time environment. The previous research done in this field is based on just optimizing metrics, whereas reducing the prediction latency in real-time is missing [34]. This paper proposes a hybrid feature selection technique using Random Forests and PCA that lowers the prediction latency of different machine learning algorithms. In this approach, first, essential features are calculated using Random Forests feature selection. Then, Principal components are computed for the selected features in the first step. Finally, significant principal components are selected for building the model. This hybrid approach reduces the complexity of the model by lowering the number of inputs to the model. With the hybrid approach, 24 principal components gave 99.9% variance. Whereas 40 principal components gave 99.9% variance using PCA. This reduction in the input features to the model reduces the time taken by the model. The proposed approach is evaluated for prediction latency using various machine learning algorithms such as Random Forests, Extra Trees, XGBoost, Histogram Based Gradient Descent, LightGBM, kNN. Table 8 lists the model training time and the prediction latency. The calculated results are the mean of the values computed in each fold during k-fold cross-validation.

Table 8 Performance classification for the proposed Hybrid Feature Selection with tenfold validation

From the results in Table 8, LightGBM is the fastest model with a prediction time of 1.38008 s using the proposed hybrid feature selection approach. As shown in Table 8, the proposed Hybrid Feature selection is more rapid than models using just PCA. The bold values indicate the reduction in model training time and prediction time using the proposed approach. The prediction latency is decreased from 44.52% to 2.25%, and the model building time from 52.68% to 17.94% in various algorithms using the proposed hybrid approach.

Analysis of the results in comparison to other feature selection methods

To further evaluate our proposed approach, it is compared with well-known feature selection methods, namely (Random Forests, Logistic Regression using regularization, LightGBM, and PCA by experimenting on the CIC-IDS 2018 dataset. These methods were selected due to their popular utilization in the domain. We evaluate the results using six metrics: Prediction Latency, Accuracy, Recall, Precision, Specificity, and F-Measure. Table 9 shows the performance of our proposed hybrid feature selection approach with other feature selection methods.

Table 9 Performance of Hybrid Feature Selection with other feature selection/dimensionality reduction techniques

As evident from the above table, the hybrid feature selection outperforms other feature selection techniques with the least prediction latency and prediction performance as good as the other techniques. While observing Prediction Latency and model Building Time, the worst feature selection method in this context is the Logistics Regression with L2.

Analysis of the proposed model with the other machine learning algorithms.

To evaluate our proposed model's performance, experiments were conducted using different machine learning algorithms, namely, Random Forests, Extra Trees, XGBoost, Histogram Gradient Boosting and kNN, and LightGBM on CIC-IDS 2018 dataset. It is observed that dimensionality reduction techniques are faster in comparison to the other feature selection methods as shown in Table 9. All the machine learning algorithms are compared using PCA and the proposed Hybrid Feature selection method. Table 10 presents the attack detection performance comparing the prediction latency and optimizing metrics and for various machine learning algorithms.

Table 10 Comparison of the Proposed model with other machine learning algorithms

As shown in Table 9, LightGBM and Histogram Gradient Boosting outperform other classifiers with the highest accuracy rate of 97.72 and 97.8% respectively. Whereas taking into consideration Prediction Latency, LightGBM is 87% more time-efficient than Histogram Based Boosting. A balanced model with high predictive performance and low latency rate is considered to be the best. So, considering both recall rate and prediction latency LightGBM using the proposed hybrid feature selection is the best model with a 97.7% accuracy rate, 99.3% precision rate, and 96% recall rate with a low prediction latency.

Comparative analysis of the proposed model and the recently cited work

Jin et al. [22] proposed Swift IDS based on parallel intrusion detection technique and time-efficient LightGBM model for attack classification. Though the proposed model is time efficient but parallel processing of different phases is subjected to communication and coordination overheads. Parallel intrusion detection is achieved by dividing the intrusion detection cycle into four phases namely the data acquisition phase, data preprocessing phase, decision-making phase, and response phase. The first and the second data preprocessing phases can start simultaneously without waiting for the first decision phase to end. In general with intervals ranging from t2 to tN, the Nth data acquisition phase, the (N-1)th data preprocessing phase, and the (N-2)th decision-making phase can be executed parallelly. Further to ensure the stability of the parallel intrusion detection system the conditions T1 > T2 and T1 > T3 should be fulfilled. Where T1, T2, and T3 respectively is the time taken by data acquisition, data preprocessing, and decision-making. Thus, the stability of the proposed model is dependent upon the speed of the network. On 8 core physical machines, the proposed model is stable with a network speed up to 1.26 Gbps. A comparison of our proposed approach and swift IDS is done in Table 11.

Table 11 Comparison of the proposed model with existing Swift IDS
  1. a.

    Ferrag [16]

    On the CIC-IDS 2018 dataset, Ferrag [16] examined several deep learning models intrusion detection. The experiment was conducted on only 5% of the whole dataset, resulting in a relatively limited number of attack occurrences. Furthermore, while all the models were assessed for attack detection accuracy, other performance measures such as Precision rate, F-Measure were missing. A comparison is made between the deep discriminative models DNN, RNN, and CNN and the proposed approach in the table. Prediction latency and accuracy were evaluated for deep learning approaches with hyperparameters listed in Table 12. For comparing the model’s performance, the prediction latency was evaluated on the AWS cloud by replicating the proposed models as per the given parameters by the author [16] using the configuration listed in Table 5.

    Table 12 List of hyperparameters for Deep Learning models

    As per the results by Ferrag [16], the model with the above hyperparameters resulted in the lowest training time. The deep learning model with these hyperparameters is taken as the base model for comparison with our proposed approach as time complexity increases with more hidden nodes and a lesser learning rate.

    It is evident from the above results in Table 13 that our proposed model outperforms the Deep Neural Network approaches as proposed by Ferrag [16]. The time complexity of the proposed approach is much lower in comparison to deep learning models with a higher accuracy rate.

    Table 13 Comparison of the proposed model with existing Deep learning model [16]
  2. b.

    Leevy [35]

    Leevy [35] explored the impact of ensemble feature selection on the performance of seven classifiers: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), Catboost, LightGBM, and XGBoost using the CSE-CIC-IDS2018 dataset. Using the proposed approach, Light GBM outperforms all the other classifiers using the ensemble feature selection technique. A comparison is made between this approach and our proposed approach in the Table 14. For comparing the model performance, the time was evaluated on the AWS cloud by replicating the proposed models as per the given parameters by the author using AWS configuration listed in Table 5.

    Table 14 Comparison of the proposed model with Ensemble Feature Selection + Light GBM [35]

    As evident from the results in Table 14 our proposed approach outperformed the given model both in terms of accuracy and prediction latency.

  3. c.

    Lin et al.[13]

    To further measure our proposed system's performance, a comparison is made between our work and recent work (Lin et al. [13]) in Table 15. For evaluating the model’s performance, the prediction time was computed on the AWS cloud by replicating the proposed models as per the given parameters by the author using the configuration listed in Table 5.

    Table 15 Comparison of the proposed model with Lin et al. [13]

The proposed model and the referenced model in Table 9 have used the CIC-IDS 2018 dataset. As listed in Table 15, the proposed model outperformed the Deep Learning method using LSTM in terms of Sensitivity, Accuracy as well as Prediction Latency. The methodology used in both models is presented in Table 16.

Table 16 Comparison of the methodology of the proposed model with the deep learning model by Lin et al. [13]

The compared model is trained on 2 million samples, whereas our proposed model is trained on 5.5 million samples. The proposed model has achieved a surge of 1.5% in Accuracy rate in comparison to the existing model, and there is a huge improvement in Prediction time.

Based on the above results our proposed model using Hybrid Feature Selection and LightGBM ensures a high prediction rate with low prediction latency resulting in improved user experience and security with faster, precise detections.

Conclusions

Our research's main objective is an intelligent IDS with a feasible balance of high attack detection rate and low prediction latency. In this paper, the latest CIC-IDS 2018 dataset is used that truly reflects modern-day traffic. We proposed a novel hybrid approach for time-efficient feature selection that reduces the prediction latency of the model. In this approach, first, the features are selected using Random Forests, then dimensionality reduction using PCA is applied to the selected essential features. This reduces the dataset considerably while retaining vital information. This approach lessens the prediction latency by reducing the model's complexity due to a lesser number of the model's inputs. The proposed method reduces the prediction latency from 44.52% to 2.25%, and the model building time from 52.68% to 17.94% in various algorithms on the CIC-IDS 2018 dataset. Further, LightGBM, a fast gradient boosting framework, is used for attack classification. The resulting model is more accurate with a better attack detection rate, lower lag. It has a 97.73% accuracy rate, and it outperforms the results obtained in previous research in this field. In addition to having a high accuracy rate, the proposed model offers low prediction latency. Besides this, an in-depth analysis of the network parameters of benign and malicious sessions on the CIC-IDS 2018 dataset was made. Key findings of the analysis were (1) 65% of attackers used port no 80 to perform the attack. (2) Benign sessions have a higher flow duration compared to malicious sessions. (3) 50% of attack sessions in the dataset had 0 flow bytes/s (4) 50% of the malicious sessions had empty packets sent to the server. (5) In 50% of the hostile sessions, no packet was sent back from the server to the attacker.

In our future work, we plan to work on a multiclassification dataset to classify individual attacks correctly.

Availability of data and materials

The dataset analyzed during the current study is available at https://www.unb.ca/cic/datasets/ids-2018.html.

Abbreviations

LightGBM:

Light Gradient Boosting Machine

IDS:

Intrusion Detection System

RF:

Random Forest

Forest PA:

Forest by Penalizing Attributes

AOP:

Average of Probabilities

DT:

Decision Tree

kNN:

K Nearest Neighbor

GB:

Gradient Boosting

LDA:

Linear Discriminant Analysis

SMOTE:

Synthetic Minority Oversampling Technique

GA-SVM:

Genetic Algorithm- Support Vector Machine

PSO:

Particle Swarm optimization

LSTM:

Long Short-Term Memory

ROC:

Receiver Operator Characteristic

ANOVA:

Analysis of Variance

PCA:

Principal Component Analysis

GOSS:

Gradient-based One-Side Sampling

DNN:

Deep Neural Network

RNN:

Reccurent Neural network

CNN:

Convulational Neural Network

BPNN:

Backpropagation Neural Network

SC:

Spectral Clustering

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SS did the experimentation. GS analyzed and interpreted the data. KKC was a major contributor in writing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Sugandh Seth.

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Appendix 1

Appendix 1

See Table 17.

Table 17  List of features in the CIC-IDS 2018 Dataset

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Seth, S., Singh, G. & Kaur Chahal, K. A novel time efficient learning-based approach for smart intrusion detection system. J Big Data 8, 111 (2021). https://doi.org/10.1186/s40537-021-00498-8

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Keywords

  • Intrusion Detection System
  • Realistic
  • Responsive
  • Imbalanced Dataset
  • Machine Learning
  • Prediction latency
  • Time-Efficient
  • Hybrid Feature Selection
  • CIC-IDS-2018