Machine Learning Models Applied in Cybersecurity: Comparison
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Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

  • cybersecurity
  • machine learning
  • malware detection
  • intrusion detection system
  • spam classification

1. Performance Comparison of Machine Learning Models Applied in Cybersecurity

Researchers are investigating machine learning techniques to detect different cybercrimes in cybersecurity. We have provided a detailed discussion of various cyber threats in Section 2. Furthermore, we have briefly presented an overview of frequently used security datasets in Section 2. This section provides a comprehensive survey of each ML model applied to deal with different cyber threats. Subsequent lines will explain the description of each column in Table 1, Table 2, Table 3, Table 4, Table 5 and Table 6. The ML technique columns describe the considered machine learning model. We have considered six ML models for this study: random forest, support vector machine, naïve Bayes, decision tree, artificial neural network, and deep belief network.

Table 1. Evaluation of SVM in Cybersecurity.

ML Technique Domain Dataset Reference Year Approach/Domain Results
Accuracy Precision Recall

Table 3. Evaluation of DBN in Cybersecurity.

ML Technique Domain Dataset Reference ReferenceYear Approach/Domain
SVM IDS NSL-KDD [1] 2019 Anomaly-Based 89.70%    
[
17
]
2014
SMS Spam
98.61%
98.60%
98.60%
Spambase [18
93.60%
[22] 2020 Spam Tweets 98.88%   94.47%

Table 2. Evaluation of Decision Tree in Cybersecurity.

ML Technique Domain Dataset Reference Year Approach/Domain Results
Results
Year Approach/Domain Accuracy Precision Recall
Accuracy Precision Recall
Results
Accuracy Precision Recall
Decision Tree

Table 5. Evaluation of Random Forest in Cybersecurity.

ML Technique Domain Dataset Reference Year Approach/Domain Results
IDS KDD [23
DBN]
Accuracy Precision Recall
ANNIDS KDD2018 Misuse-Based 99.96%   [  42] 2015 Anomaly-Based 97.50% IDS NSL-KDD [   
[2] 2016 Anomaly-Based
52] 2019 Anomaly-Based 94.50% - 98.89% -  
- [2468] 2019

Table 6. Evaluation of Naïve Bayes in Cybersecurity.

ML Technique Domain Dataset Reference Year Approach/Domain Results
Accuracy Precision Recall
]
2005 [Hybrid-Based 4399.85% 99.70% ]98.10% 2015 Hybrid-Based 96.70% 97.90%  
[3] 2014 Hybrid-Based 82.37%
Naïve Bayes IDS DARPA [79] 2010 Anomaly-Based 91.60%   61.60%
[80] 2007
74%
[82%
Misuse-Based 99.90% 99.04% 99.50% 25] 2017 Hybrid-Based 86.29%   78%
NSL-KDD [44] 2017 Anomaly-Based
NSL-KDD [29Anomaly-Based ]90.40% 88.60% 201595.10% 92.50%   Misuse-Based 81.66% DARPA [4] 2007 Hybrid-Based
NSL-KDD69.80% [26  ] 2014 
2015Anomaly-Based 99.64%   Anomaly-Based 80% -  80%
[69] 2019 Hybrid-Based 75.30% 81.40% 75.30% [5] 2014 Anomaly-Based 95.11%   -
[27] 2017 Hybrid-Based [90.30% 23] 2018 Misuse-Based 99.82%91.15%
[- 70]90.31% 2017 Hybrid-Based 97.10%-
    KDD CUP99 [6] 2011 Hybrid-Based 95.72%    
[7
[28] 2019 Hybrid-Based 93.40%     KDD CUP99 [] 2015 Hybrid-Based
KDD CUP9996.08% [
KDD CUP9955]29- ] 2015 
Misuse-Based 95.09%     [8] 2014 Hybrid-Based 99.30% -  
[30] 2016 Hybrid-Based 99.62%     Malware Custom Dataset [9] 2019 Static 95.17% 95.57%
[3195%
] 2018 Hybrid-Based 92.87% 99.90%   [10] 2018 Static 89.91% 88.84%  
Malware Custom [32] 2016 2017 Static 84.7%    ] 2015 Email Spam 79.50% 79.02% 68.67%
Static 99.90% 99.40%   [11] 2018 Dynamic 96.27% 96.16% 93.71%
95.30% Malware Dataset [34] 2014 Static   97.90% 96.70%
[53] 2014 Anomaly-Based 97.53% - -
[4] 2014 Hybrid-Based 97.06% - - [45 99.45% 99.20%
DARPA99.70%
[54] ISCX Dataset [46] 2015 Misuse-Based 99.18% - -
Malware DLL [47] 2008 Static 89.90% 87.40% 98.80%
[2009 68Anomaly-Based - ]97.89% 98.94%
2019 Anomaly-Based 96.30% 99.80%   Custom [56][ 2012
[7148] 2016 Anomaly-Based ] 2016Static 62.90%89.03% 83% 98.18%
- -
Anomaly-Based - 98.10% 98.10% [48] 2016 Dynamic Malware VX Heavens [71% 5778.08% ] 2012 Hybrid59.09% 88.89% 88.89% -
[70] 2017 Hybrid-Based 98.10% - - [58] 2012 Static 92.19% - - Malware Dataset [12] 2017 Static 94.37%    
[35] 2013 Static 92.34% - 93%
[36] 2013 Dynamic 88.47%    
SMOTE [37
[48] 2016 Hybrid 96.76% 95.77% 97.84%
KDD CUP99 [49] 2015 Hybrid 91.40% - 95.34%
Spam TARASSUL [50] 2016 Email Spam 96.40% 95.31% 93.59%
[50] 2016 Email Spam 97.50% 98.39% 98.02% [13] 2013 Dynamic 95%    
Enron [45] 2016 Email Spam 95.86% 96.49% 95.61% [14] 2015 Dynamic 97.10%    
Enron
   
[81] 2012 Anomaly-Based 36% Malware Custom Dataset [9] 2019 Static 98.63% 98.58% 98.69% [59] 2013 Static 88.31% - -
[11] 2018 Dynamic 96.34% 96.59% 93.46% Enron [60] 2018 Dynamic
35% 80%
[81] 2012 Anomaly-Based 99% 83% 78.90% Malware Dataset [72] 2016 Dynamic 96.14%    
[34] 2014 Hybrid  Image Spam 93.70%
Random Forest IDS KDD [66] 2019
82.79%
87%
94%
Anomaly-Based 99.95%   99.95%
KDD CUP99 [82] 2004 Anomaly-Based 99.27%    
[80] 2007 Anomaly-Based   96% 99.80%
[79] 2018 Signature-Based 99.72%   100%
Malware VX Heaven [83] 2015 Static 88.80%    
NSL-KDD [84] 2013 Hybrid 99.50%     -
[-
85] 2007 Hybrid 99%     Comodo [61] 2016 Static
Malware Dataset92.02% [35- 96.50% ] 2013 Hybrid97.30% 89.81%
- 90% [73] 2017 Hybrid 91.40% 89.80% 91.10% Spambase [63] 2016 Email Spam 91%
[67] 2016 Anomaly-Based 88.65% - -
Spam Spam-Archive [
[86] 2015 Hybrid 95.90% 95.90% 95.90% [16] 2007 Email Spam 97.43% 94.94% 96.47% VirusShare [74] 2009- - [15] 2016 Static 91% ]84.74%
94.62% Static 95.60%100%
2018 Dynamic 92.82%    
[
[34] 64] 2018 Email Spam 92.41% 92.40% 92.40%
[
NSL-KDD [62] 2011 201496%  
Hybrid   97.50% 67.40% Spambase [ Spam51] 2018 Email Spam 89.20% SMS Collection96% [17] 2014 SMS Spam  97.18% 97.30% 97.20%
Spam SMS Collection [17] 2014 SMS Spam 97.52% 97.50% 97.50% [16] [2007 3765Static 96.92% ] ]201892.74% 97.27%
Dynamic 95.75%    
[51] 2018 Email Spam 201390.69% 97% Hybrid 93.71%
Spambase [95% -
Twitter Dataset
Spambase [75] 2013 19Email Spam 99.54% ]    2011 Email Spam  99.46% 99.66% 98.46% [38] 2012 [20]Static 96.62%    
2018 Spam Tweets 91.18% 91.80% 91.18%
[
76
]
2010
[
18
]
Email Spam
95.43%
 
 
2015 Email Spam 76.24% 70.59% 72.05% Spam SMS Collection [17] 2014 [41] 2013 Email Spam 93.89%
[19] 2011 Email Spam 96.90% 93.12%
[33] Spam SMS CollectionSMS Spam
[87] 201596.60% 96.50% 96.60% Email Spam95.87% 94.10%
84% 89% 78% 95%
Enron [15] 2016 Twitter Dataset [77]Email Spam 96% 98% 94% 2011 Spam Tweets 95% 95.70% 95.70%
Twitter Dataset [41] 2013 Spam Tweets 92% Twitter Dataset [20
91.60% 91.4% [15] [] 782018 Spam Tweets 2016 Email Spam98% 94%
] ] 2016
[20]Spam Tweets Spambase [39] 2014 Email Spam 92.08% 91.51% 88.08%
2019 Anomaly-Based 96%93.14% 92.91% 93.14%
96.20% 98.60% 75.50%
2018 Spam Tweets 92.06% 91.69% 91.96% [21] 2015 Spam Tweets 95.20%   [20] 2018 Spam Tweets 93.43% [40] 2014 Email Spam 94.27%   91.02%
[41] 2013 Email Spam 92.34% 93.90% 93.50%

Table 4. Evaluation of ANN in Cybersecurity.

ML Technique Domain Dataset
93.25%
93.43%

We focus on three critical cyber threats, namely intrusion detection, spam detection and malware detection. The domain columns state the significant cybersecurity threats considered for this review. The reference number and year columns depict the citation number of each article and published year, respectively. The values of approach or sub-domain columns are different for each cyber threat. IDS domain has three values that are anomaly-based, signature/misuse-based and hybrid-based. Malware has three further sub-classifications that are static, dynamic and hybrid. In the case of spam, sub-domains correspond to the medium in which the authors tried to identify the spam such as image, video, email, SMS and tweets. A description of each sub-domain/approach has been provided in Section 2. Finally, the result attribute presents the evaluation of each classifier applied in a particular sub-domain of cyber threat on a specific dataset and provided in the cited paper mentioned in the reference column.

2. Support Vector Machine

The principle superiority of support vector machine (SVM) is that it produces the most successful results for cybersecurity tasks. SVM distributes each data class on both sides of the hyperplane. SVM separates the classes based on the notation to the margin. Support vector points are those points that lie on the border of the hyperplane. The major drawback of the support vector machine is that it consumes an immense amount of space and time. SVM requires data trained on different time intervals to produce better results for a dynamic dataset [88].

SVM showed an accuracy of 99.30% with KDD Cup 99 dataset for IDS [8]. 96.92% is the best reported accuracy for malware detection using Enron dataset [16] and 96.90% with Spambase to classify spam emails [19]. The best reported recall for SVM to detect intrusion is 82% [3], malware is 100% [15], and spam is 98.60% [17]. SVM has obtained best precision while detecting the intrusion is 74% [24], malware is 96.16% [11], and spam is 98.60% [17]. A detailed performance comparison of SVM to various cyber threats on the frequently used dataset is presented in Table 1.

3. Decision Tree

Decision tree (DT) belongs to the category of supervised machine learning. DT consists of a path and two nodes: root/intermediate and leaf. Root or intermediate node presents an attribute that followed a path that corresponds to the possible value of an attribute. Leaf node represents the final decision/classification class. A decision tree is used to find the best immediate node by following the if-then rule [89]. Further, 99.96% is the reported accuracy of DT while detecting the anomaly-based IDS with KDD dataset [23]. With standard SMOTE dataset, DT shows an outstanding accuracy of 96.62% for malware detection [38]. With the Enron dataset, DT correctly classified ham emails with an accuracy of 96% [15]. The best reported recall for DT to detect intrusion is 98.10% [24], malware is 96.70% [34], and spam is 96.60% [17]. DT has obtained best precision while detecting the intrusion is 99.70% [24], malware is 99.40% [32], and spam is 98% [15]. A detailed performance comparison of decision tree to various cyber threats on the frequently used dataset is presented in Table 2.

4. Deep Belief Network

A deep belief network (DBN) consists of various middle layers of restricted Boltzmann machine (RBM) organized greedily. Every layer communicates with the layers behind it and the layers ahead of it. There is no lateral communication between the nodes within a layer. Every layer serves as both an input layer and an output layer, except the first and the last layers. The last layer functions as a classifier. The primary purpose of a deep belief network is image clustering and image recognition. It deals with motion capture data. Deep belief network has shown the accuracy of 97.50% for IDS [42], 91.40% for malware detection [90] and 97.43% for spam detection [91] with KDD, KDD CUP99, and Spambase datasets, respectively. The best reported recall for DBN to detect intrusion is 99.70% [45], malware is 98.80% [47], and spam is 98.02% [50]. DBN obtained the best precision while detecting the intrusion is 99.20% [45], malware is 95.77% [48], and spam is 98.39% [50]. A detailed performance comparison of DBN to various cyber threats on the frequently used dataset is presented in Table 3.

5. Artificial Neural Network

An artificial neural network (ANN) classier consists of hidden neuron input and output layers and performs in two stages. The first stage is called feedforward. In this stage, each hidden layer receives some input nodes and based on the input layer and activation function, the error is calculated. In the second stage, namely feedback stage, the error is sent back to the input layer and process is continued in iterations until the correct result is gained [60]. The artificial neural network showed an accuracy of 97.53% for IDS [53], 92.19% for malware detection [58], and 92.41% for spam detection with NSL-KDD, VX Heavens, and Spambase datasets, respectively. The best reported recall for ANN to detect an intrusion is 98.94% [55], and spam is 94% [62]. ANN has obtained best precision while detecting the intrusion is 97.89% [55], malware is 88.89% [57], and spam is 95% [65]. A detailed performance comparison of ANN to various cyber threats on the frequently used dataset is presented in Table 7.

6. Random Forest

Random forest (RF) follows through the task by combing different predictions generated by joining different decision trees. RF raised a hypothesis to obtain a result [91]. RF falls under the category of ensemble learning. RF also termed as random decision forest. RF is considered as an improved version of CART that is a sub-type of a decision tree.

RF has shown an accuracy of 99.95% with IDS [66], 95.60% with malware detection [74] and 99.54% for spam detection [75] with KDD, VirusShare, and Spambase datasets, respectively. The best reported recall for RF to detect intrusion is 99.95% [66], malware is 97.30% [34], and spam is 97.20% [17]. RF obtained the best precision while detecting the intrusion is 99.80% [68], malware is 98.58% [9], and spam is 98.60% [78]. A detailed performance comparison of RF to various cyber threats on the frequently used dataset is presented in Table 5.

7. Naïve Bayes

The major limitation for Naïve Bayes (NB) classifier is that it assumes that every attribute is independent, and none of the attributes has a relationship with each other. This state of independence is technically impossible in cyberspace. Hidden NB is an advanced form of Naïve Bayes, and it gives 99.6% accuracy [92]. Naïve Bayes showed an accuracy of 99.90% with DARPA dataset for IDS [80]. 99.50% is the best reported accuracy for malware detection using NSL-KDD dataset [86]. With Spambase dataset, Naïve Bayes showed considerable accuracy of 96.46 % to classify spam or ham email [19]. The best reported recall for NB to detect intrusion is 100% [79], malware is 95.90% [86], and spam is 98.46% [19]. NB obtained the best precision while detecting the intrusion is 99.04% [80], malware is 97.50% [34], and spam is 99.66% [19]. A detailed performance comparison of NB to various cyber threats on the frequently used dataset is presented in Table 6.

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