Machine Learning Models Applied in Cybersecurity: History
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Subjects: Computer Science, Information Systems
Contributor:
Kamran Shaukat
,
Suhuai Luo
,
Vijay Varadharajan
,
Ibrahim A. Hameed
,
Shan Chen
,
Dongxi Liu
,
Jiaming Li

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

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 4, Table 5, Table 6, Table 7, Table 8 and Table 9. 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 4. Evaluation of SVM in Cybersecurity.
ML Technique Domain Dataset Reference Year Approach/Domain Results
Accuracy Precision Recall
SVM IDS NSL-KDD [92] 2019 Anomaly-Based 89.70%    
[93] 2016 Anomaly-Based 98.89% -  
[87] 2014 Hybrid-Based 82.37% 74% 82%
DARPA [94] 2007 Hybrid-Based 69.80%    
[95] 2014 Anomaly-Based 95.11%   -
KDD CUP99 [96] 2011 Hybrid-Based 95.72%    
[97] 2015 Hybrid-Based 96.08% -  
[84] 2014 Hybrid-Based 99.30% -  
Malware Custom Dataset [98] 2019 Static 95.17% 95.57% 95%
[99] 2018 Static 89.91% 88.84%  
[91] 2018 Dynamic 96.27% 96.16% 93.71%
Malware Dataset [100] 2017 Static 94.37%    
[101] 2013 Dynamic 95%    
[102] 2015 Dynamic 97.10%    
Enron [88] 2016 Static 91% 84.74% 100%
[85] 2007 Static 96.92% 92.74% 97.27%
Spam SMS Collection [89] 2014 SMS Spam 98.61% 98.60% 98.60%
Spambase [103] 2015 Email Spam 79.50% 79.02% 68.67%
[86] 2011 Email Spam 96.90% 93.12% 95%
Twitter Dataset [104] 2018 Spam Tweets 93.14% 92.91% 93.14%
[29] 2015 Spam Tweets 95.20%   93.60%
[105] 2020 Spam Tweets 98.88%   94.47%
Table 5. Evaluation of Decision Tree in Cybersecurity.
ML Technique Domain Dataset Reference Year Approach/Domain Results
Accuracy Precision Recall
Decision Tree IDS KDD [107] 2018 Misuse-Based 99.96%    
[90] 2005 Hybrid-Based 99.85% 99.70% 98.10%
[111] 2017 Hybrid-Based 86.29%   78%
NSL-KDD [112] 2014 Anomaly-Based 99.64%    
[113] 2017 Hybrid-Based 90.30% 91.15% 90.31%
[114] 2019 Hybrid-Based 93.40%    
KDD CUP99 [115] 2015 Misuse-Based 95.09%    
[116] 2016 Hybrid-Based 99.62%    
[117] 2018 Hybrid-Based 92.87% 99.90%  
Malware Custom [110] 2016 Static 99.90% 99.40%  
[118] 2017 Static 84.7%    
Malware Dataset [109] 2014 Static   97.90% 96.70%
[119] 2013 Static 92.34% - 93%
[120] 2013 Dynamic 88.47%    
SMOTE [121] 2018 Dynamic 92.82%    
[121] 2018 Dynamic 95.75%    
[108] 2012 Static 96.62%    
Spam SMS Collection [89] 2014 SMS Spam 96.60% 96.50% 96.60%
Enron [88] 2016 Email Spam 96% 98% 94%
[88] 2016 Email Spam 96% 98% 94%
Spambase [122] 2014 Email Spam 92.08% 91.51% 88.08%
[123] 2014 Email Spam 94.27%   91.02%
[124] 2013 Email Spam 92.34% 93.90% 93.50%
Table 6. Evaluation of DBN in Cybersecurity.
ML Technique Domain Dataset Reference Year Approach/Domain Results
Accuracy Precision Recall
DBN IDS KDD [125] 2015 Anomaly-Based 97.50%    
[132] 2015 Hybrid-Based 96.70% 97.90%  
NSL-KDD [51] 2017 Anomaly-Based 90.40% 88.60% 95.30%
[128] 2019 Anomaly-Based 99.45% 99.20% 99.70%
ISCX Dataset [133] 2015 Misuse-Based 99.18% - -
Malware DLL [129] 2008 Static 89.90% 87.40% 98.80%
Custom [131] 2016 Static 89.03% 83% 98.18%
[131] 2016 Dynamic 71% 78.08% 59.09%
[131] 2016 Hybrid 96.76% 95.77% 97.84%
KDD CUP99 [134] 2015 Hybrid 91.40% - 95.34%
Spam TARASSUL [130] 2016 Email Spam 96.40% 95.31% 93.59%
[130] 2016 Email Spam 97.50% 98.39% 98.02%
Enron [128] 2016 Email Spam 95.86% 96.49% 95.61%
[85] 2007 Email Spam 97.43% 94.94% 96.47%
Spambase [135] 2018 Email Spam 89.20% 96%  
[135] 2018 Email Spam 90.69% 97%  
Table 7. Evaluation of ANN in Cybersecurity.
ML Technique Domain Dataset Reference Year Approach/Domain Results
Accuracy Precision Recall
ANN IDS NSL-KDD [143] 2019 Anomaly-Based 94.50% - -
[137] 2014 Anomaly-Based 97.53% - -
[94] 2014 Hybrid-Based 97.06% - -
DARPA [45] 2015 Anomaly-Based 80% - 80%
[107] 2018 Misuse-Based 99.82% - -
KDD CUP99 [139] 2009 Anomaly-Based - 97.89% 98.94%
[144] 2012 Anomaly-Based 62.90% - -
Malware VX Heavens [141] 2012 Hybrid 88.89% 88.89% -
[138] 2012 Static 92.19% - -
[145] 2013 Static 88.31% - -
Enron [136] 2018 Dynamic 82.79% - -
Comodo [146] 2016 Static 92.02% - -
Spam Spam-Archive [140] 2011 Image Spam 93.70% 87% 94%
Spambase [147] 2016 Email Spam 91% - -
[148] 2018 Email Spam 92.41% 92.40% 92.40%
[142] 2013 Hybrid 93.71% 95% -
Twitter Dataset [104] 2018 Spam Tweets 91.18% 91.80% 91.18%
Table 8. Evaluation of Random Forest in Cybersecurity.
ML Technique Domain Dataset Reference Year Approach/Domain Results
Accuracy Precision Recall
Random Forest IDS KDD [149] 2019 Anomaly-Based 99.95%   99.95%
[154] 2016 Anomaly-Based 88.65% - 94.62%
NSL-KDD [152] 2019 Anomaly-Based 95.10% 92.50%  
[155] 2019 Hybrid-Based 75.30% 81.40% 75.30%
[156] 2017 Hybrid-Based 97.10%    
KDD CUP99 [152] 2019 Anomaly-Based 96.30% 99.80%  
[157] 2016 Anomaly-Based - 98.10% 98.10%
[156] 2017 Hybrid-Based 98.10% - -
Malware Custom Dataset [98] 2019 Static 98.63% 98.58% 98.69%
[91] 2018 Dynamic 96.34% 96.59% 93.46%
Malware Dataset [158] 2016 Dynamic 96.14%    
[109] 2014 Hybrid   96.50% 97.30%
[159] 2017 Hybrid 91.40% 89.80% 91.10%
VirusShare [150] 2009 Static 95.60% 96%  
Spam SMS Collection [89] 2014 SMS Spam 97.18% 97.30% 97.20%
Spambase [151] 2013 Email Spam 99.54%    
[160] 2010 Email Spam 95.43%    
[124] 2013 Email Spam 93.89% 95.87% 94.10%
Twitter Dataset [161] 2011 Spam Tweets 95% 95.70% 95.70%
[153] 2016 Spam Tweets 96.20% 98.60% 75.50%
[104] 2018 Spam Tweets 93.43% 93.25% 93.43%
Table 9. Evaluation of Naïve Bayes in Cybersecurity.
ML Technique Domain Dataset Reference Year Approach/Domain Results
Accuracy Precision Recall
Naïve Bayes IDS DARPA [33] 2010 Anomaly-Based 91.60%   61.60%
[163] 2007 Misuse-Based 99.90% 99.04% 99.50%
NSL-KDD [115] 2015 Misuse-Based 81.66%    
[165] 2012 Anomaly-Based 36% 35% 80%
[165] 2012 Anomaly-Based 99% 83% 78.90%
KDD CUP99 [166] 2004 Anomaly-Based 99.27%    
[163] 2007 Anomaly-Based   96% 99.80%
[33] 2018 Signature-Based 99.72%   100%
Malware VX Heaven [167] 2015 Static 88.80%    
NSL-KDD [168] 2013 Hybrid 99.50%    
[169] 2007 Hybrid 99%    
Malware Dataset [119] 2013 Hybrid 89.81% - 90%
[164] 2015 Hybrid 95.90% 95.90% 95.90%
[109] 2014 Hybrid   97.50% 67.40%
Spam SMS Collection [89] 2014 SMS Spam 97.52% 97.50% 97.50%
Spambase [86] 2011 Email Spam 99.46% 99.66% 98.46%
[103] 2015 Email Spam 76.24% 70.59% 72.05%
[170] 2015 Email Spam 84% 89% 78%
Twitter Dataset [124] 2013 Spam Tweets 92% 91.60% 91.4%
[104] 2018 Spam Tweets 92.06% 91.69% 91.96%
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.

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 [83].
 
SVM showed an accuracy of 99.30% with KDD Cup 99 dataset for IDS [84]. 96.92% is the best reported accuracy for malware detection using Enron dataset [85] and 96.90% with Spambase to classify spam emails [86]. The best reported recall for SVM to detect intrusion is 82% [87], malware is 100% [88], and spam is 98.60% [89]. SVM has obtained best precision while detecting the intrusion is 74% [90], malware is 96.16% [91], and spam is 98.60% [89]. A detailed performance comparison of SVM to various cyber threats on the frequently used dataset is presented in Table 4.

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 [106]. Further, 99.96% is the reported accuracy of DT while detecting the anomaly-based IDS with KDD dataset [107]. With standard SMOTE dataset, DT shows an outstanding accuracy of 96.62% for malware detection [108]. With the Enron dataset, DT correctly classified ham emails with an accuracy of 96% [88]. The best reported recall for DT to detect intrusion is 98.10% [90], malware is 96.70% [109], and spam is 96.60% [89]. DT has obtained best precision while detecting the intrusion is 99.70% [90], malware is 99.40% [110], and spam is 98% [88]. A detailed performance comparison of decision tree to various cyber threats on the frequently used dataset is presented in Table 5.

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 [125], 91.40% for malware detection [126] and 97.43% for spam detection [127] with KDD, KDD CUP99, and Spambase datasets, respectively. The best reported recall for DBN to detect intrusion is 99.70% [128], malware is 98.80% [129], and spam is 98.02% [130]. DBN obtained the best precision while detecting the intrusion is 99.20% [128], malware is 95.77% [131], and spam is 98.39% [130]. A detailed performance comparison of DBN to various cyber threats on the frequently used dataset is presented in Table 6.

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 [136]. The artificial neural network showed an accuracy of 97.53% for IDS [137], 92.19% for malware detection [138], 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% [139], and spam is 94% [140]. ANN has obtained best precision while detecting the intrusion is 97.89% [139], malware is 88.89% [141], and spam is 95% [142]. A detailed performance comparison of ANN to various cyber threats on the frequently used dataset is presented in Table 7.

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 [127]. 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 [149], 95.60% with malware detection [150] and 99.54% for spam detection [151] with KDD, VirusShare, and Spambase datasets, respectively. The best reported recall for RF to detect intrusion is 99.95% [149], malware is 97.30% [109], and spam is 97.20% [89]. RF obtained the best precision while detecting the intrusion is 99.80% [152], malware is 98.58% [98], and spam is 98.60% [153]. A detailed performance comparison of RF to various cyber threats on the frequently used dataset is presented in Table 8.

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 [162]. Naïve Bayes showed an accuracy of 99.90% with DARPA dataset for IDS [163]. 99.50% is the best reported accuracy for malware detection using NSL-KDD dataset [164]. With Spambase dataset, Naïve Bayes showed considerable accuracy of 96.46 % to classify spam or ham email [86]. The best reported recall for NB to detect intrusion is 100% [33], malware is 95.90% [164], and spam is 98.46% [86]. NB obtained the best precision while detecting the intrusion is 99.04% [163], malware is 97.50% [109], and spam is 99.66% [86]. A detailed performance comparison of NB to various cyber threats on the frequently used dataset is presented in Table 9.
 
 
  • Iyer, S.S.; Rajagopal, S. Applications of Machine Learning in Cyber Security Domain. In Handbook of Research on Machine and Deep Learning Applications for Cyber Security; IGI Global: Hershey, PA, USA, 2020; pp. 64–82. [Google Scholar]
  • Saxena, H.; Richariya, V. Intrusion detection in KDD99 dataset using SVM-PSO and feature reduction with information gain. Int. J. Comput. Appl. 2014, 98, 25–29. [Google Scholar] [CrossRef]
  • Tzortzis, G.; Likas, A. Deep belief networks for spam filtering. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), Patras, Greece, 29–31 October 2007; pp. 306–309. [Google Scholar]
  • Awad, W.; ELseuofi, S. Machine learning methods for spam e-mail classification. Int. J. Comput. Sci. Inf. Technol. 2011, 3, 173–184. [Google Scholar] [CrossRef]
  • Pervez, M.S.; Farid, D.M. Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs. In Proceedings of the 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), Dhaka, Bangladesh, 18–20 December 2014; pp. 1–6. [Google Scholar]
  • Khan, Z.; Qamar, U. Text Mining Approach to Detect Spam in Emails. In Proceedings of the International Conference on Innovations in Intelligent Systems and Computing Technologies (ICIISCT2016), Las Piñas, Philippines, 24–26 February 2016; p. 45. [Google Scholar]
  • Najadat, H.; Abdulla, N.; Abooraig, R.; Nawasrah, S. Mobile sms spam filtering based on mixing classifiers. Int. J. Adv. Comput. Res. 2014, 1, 1–7. [Google Scholar]
  • Stein, G.; Chen, B.; Wu, A.S.; Hua, K.A. Decision tree classifier for network intrusion detection with GA-based feature selection. In Proceedings of the 43rd Annual Southeast Regional Conference-Volume 2; ACM: New York, NY, USA, 2005; pp. 136–141. [Google Scholar]
  • Feng, P.; Ma, J.; Sun, C.; Xu, X.; Ma, Y.J.I.A. A Novel Dynamic Android Malware Detection System With Ensemble Learning. IEEE Access 2018, 6, 30996–31011. [Google Scholar] [CrossRef]
  • Lee, J.; Kim, J.; Kim, I.; Han, K. Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles. IEEE Access 2019, 7, 165607–165626. [Google Scholar] [CrossRef]
  • Sharma, R.K.; Kalita, H.K.; Borah, P. Analysis of machine learning techniques based intrusion detection systems. In Proceedings of the 3rd International Conference on Advanced Computing, Networking and Informatics; Springer: Berlin/Heidelberg, Germany, 2015; pp. 485–493. [Google Scholar]
  • Khan, L.; Awad, M.; Thuraisingham, B. A new intrusion detection system using support vector machines and hierarchical clustering. VLDB J. 2007, 16, 507–521. [Google Scholar] [CrossRef]
  • Kokila, R.; Selvi, S.T.; Govindarajan, K. DDoS detection and analysis in SDN-based environment using support vector machine classifier. In Proceedings of the 2014 Sixth International Conference on Advanced Computing (ICoAC), Chennai, India, 17–19 Decmber 2014; pp. 205–210. [Google Scholar]
  • Horng, S.-J.; Su, M.-Y.; Chen, Y.-H.; Kao, T.-W.; Chen, R.-J.; Lai, J.-L.; Perkasa, C.D. A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Syst. Appl. 2011, 38, 306–313. [Google Scholar] [CrossRef]
  • Masduki, B.W.; Ramli, K.; Saputra, F.A.; Sugiarto, D. Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS). In Proceedings of the 2015 International Conference on Quality in Research (QiR), Lombok, Indonesia, 10–13 August 2015; pp. 56–64. [Google Scholar]
  • Naz, S.; Singh, D.K. Review of Machine Learning Methods for Windows Malware Detection. In Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 6–8 July 2019; pp. 1–6. [Google Scholar]
  • Zhu, H.-J.; Jiang, T.-H.; Ma, B.; You, Z.-H.; Shi, W.-L.; Cheng, L.J.N.C. HEMD: A highly efficient random forest-based malware detection framework for Android. Neural Comput. Appl. 2018, 30, 3353–3361. [Google Scholar] [CrossRef]
  • Cheng, Y.; Fan, W.; Huang, W.; An, J. A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine. In Proceedings of the IOP Conference Series: Materials Science and Engineering, Sanya, China, 12–15 November 2019; p. 012124. [Google Scholar]
  • Mohaisen, A.; Alrawi, O. Unveiling zeus: Automated classification of malware samples. In Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 13–17 May 2013; pp. 829–832. [Google Scholar]
  • Shijo, P.; Salim, A.J.P.C.S. Integrated static and dynamic analysis for malware detection. Procedia Comput. Sci. 2015, 46, 804–811. [Google Scholar] [CrossRef]
  • Karthika, R.; Visalakshi, P.J.W.T.C. A hybrid ACO based feature selection method for email spam classification. WSEAS Trans. Comput. 2015, 14, 171–177. [Google Scholar]
  • Jain, G.; Sharma, M.; Agarwal, B. Spam detection on social media using semantic convolutional neural network. Int. J. Knowl. Discov. Bioinform. 2018, 8, 12–26. [Google Scholar] [CrossRef]
  • Sagar, R.; Jhaveri, R.; Borrego, C.J.E. Applications in Security and Evasions in Machine Learning: A Survey. Electronics 2020, 9, 97. [Google Scholar] [CrossRef]
  • Quinlan, J.R. C4. 5: Programs for Machine Learning; Elsevier: Amsterdam, The Netherlands, 2014. [Google Scholar]
  • Mishra, P.; Varadharajan, V.; Tupakula, U.; Pilli, E.S. Tutorials. A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun. Surv. Tutor. 2018, 21, 686–728. [Google Scholar] [CrossRef]
  • Kavzoglu, T.; Colkesen, I. The effects of training set size for performance of support vector machines and decision trees. In Proceedings of the 10th international symposium on spatial accuracy assessment in natural resources and environmental sciences, Florianópolis, Brazil, 10–13 July 2012; p. 1013. [Google Scholar]
  • Salehi, Z.; Sami, A.; Ghiasi, M.J.C.F. Using feature generation from API calls for malware detection. Security 2014, 2014, 9–18. [Google Scholar] [CrossRef]
  • Jamil, Q.; Shah, M.A. Analysis of machine learning solutions to detect malware in android. In Proceedings of the 2016 Sixth International Conference on Innovative Computing Technology (INTECH), Dublin, Ireland, 24–26 August 2016; pp. 226–232. [Google Scholar]
  • Kevric, J.; Jukic, S.; Subasi, A.J.N.C. An effective combining classifier approach using tree algorithms for network intrusion detection. Applications 2017, 28, 1051–1058. [Google Scholar] [CrossRef]
  • Gaikwad, D.; Thool, R.C. Intrusion detection system using ripple down rule learner and genetic algorithm. Int. J. Comput. Sci. Inf. Technol. 2014, 5, 6976–6980. [Google Scholar]
  • Ingre, B.; Yadav, A.; Soni, A.K. Decision tree based intrusion detection system for NSL-KDD dataset. In Proceedings of the International Conference on Information and Communication Technology for Intelligent Systems, Ahmedabad, India, 15–16 May 2020; pp. 207–218. [Google Scholar]
  • Ahmim, A.; Maglaras, L.; Ferrag, M.A.; Derdour, M.; Janicke, H. A novel hierarchical intrusion detection system based on decision tree and rules-based models. In Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece, 29–31 May 2019; pp. 228–233. [Google Scholar]
  • Relan, N.G.; Patil, D.R. Implementation of network intrusion detection system using variant of decision tree algorithm. In Proceedings of the 2015 International Conference on Nascent Technologies in the Engineering Field (ICNTE), Navi Mumbai, India, 9–10 January 2015; pp. 1–5. [Google Scholar]
  • Goeschel, K. Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis. In Proceedings of the SoutheastCon 2016, Norfolk, VA, USA, 30 March–3 April 2016; pp. 1–6. [Google Scholar]
  • Malik, A.J.; Khan, F.A. A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection. Clust. Comput. 2018, 21, 667–680. [Google Scholar] [CrossRef]
  • Moon, D.; Im, H.; Kim, I.; Park, J.H. DTB-IDS: An intrusion detection system based on decision tree using behavior analysis for preventing APT attacks. J. Supercomput. 2017, 73, 2881–2895. [Google Scholar] [CrossRef]
  • Santos, I.; Brezo, F.; Ugarte-Pedrero, X.; Bringas, P.G. Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf. Sci. 2013, 231, 64–82. [Google Scholar] [CrossRef]
  • Islam, R.; Tian, R.; Batten, L.M.; Versteeg, S. Classification of malware based on integrated static and dynamic features. J. Netw. Comput. Appl. 2013, 36, 646–656. [Google Scholar] [CrossRef]
  • Yan, P.; Yan, Z. A survey on dynamic mobile malware detection. Softw. Qual. J. 2018, 26, 891–919. [Google Scholar] [CrossRef]
  • Saab, S.A.; Mitri, N.; Awad, M. Ham or spam? A comparative study for some content-based classification algorithms for email filtering. In Proceedings of the MELECON 2014-2014 17th IEEE Mediterranean Electrotechnical Conference, Beirut, Lebanon, 13–16 April 2014; pp. 339–343. [Google Scholar]
  • Zhang, Y.; Wang, S.; Phillips, P.; Ji, G. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl. -Based Syst. 2014, 64, 22–31. [Google Scholar] [CrossRef]
  • Sharma, S.; Arora, A. Adaptive approach for spam detection. Int. J. Comput. Sci. Issues 2013, 10, 23. [Google Scholar]
  • Alom, M.Z.; Bontupalli, V.; Taha, T.M. Intrusion detection using deep belief networks. In Proceedings of the 2015 National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA, 15–19 June 2015; pp. 339–344. [Google Scholar]
  • Tyagi, A. Content Based Spam Classification-A Deep Learning Approach; University of Calgary: Calgary, AB, Canada, 2016. [Google Scholar]
  • He, S.; Lee, G.M.; Han, S.; Whinston, A.B. How would information disclosure influence organizations’ outbound spam volume? Evidence from a field experiment. J. Cybersecur. 2016, 2, 99–118. [Google Scholar] [CrossRef]
  • Zhang, Y.; Li, P.; Wang, X.J.I.A. Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access 2019, 7, 31711–31722. [Google Scholar] [CrossRef]
  • Ye, Y.; Wang, D.; Li, T.; Ye, D.; Jiang, Q. An intelligent PE-malware detection system based on association mining. J. Comput. Virol. 2008, 4, 323–334. [Google Scholar] [CrossRef]
  • Alkaht, I.J.; Al-Khatib, B. Filtering SPAM Using Several Stages Neural Networks. Int. Rev. Comp. Softw. 2016, 11, 2. [Google Scholar] [CrossRef]
  • Yuan, Z.; Lu, Y.; Xue, Y. Droiddetector: Android malware characterization and detection using deep learning. Tsinghua Sci. Technol. 2016, 21, 114–123. [Google Scholar] [CrossRef]
  • Jo, S.; Sung, H.; Ahn, B. A comparative study on the performance of intrusion detection using decision tree and artificial neural network models. J. Korea Soc. Digit. Ind. Inf. Manag. 2015, 11, 33–45. [Google Scholar]
  • Ammar, A. A decision tree classifier for intrusion detection priority tagging. J. Comput. Commun. 2015, 3, 52. [Google Scholar] [CrossRef]
  • Li, Y.; Ma, R.; Jiao, R. A hybrid malicious code detection method based on deep learning. J. Secur. Appl. 2015, 9, 205–216. [Google Scholar] [CrossRef]
  • Rizk, Y.; Hajj, N.; Mitri, N.; Awad, M. Deep belief networks and cortical algorithms: A comparative study for supervised classification. Appl. Comput. Inform. 2019, 15, 81–93. [Google Scholar] [CrossRef]
  • Phan, T.D.; Zincir-Heywood, N. User identification via neural network based language models. Int. J. Netw. Manag. 2019, 29, e2049. [Google Scholar] [CrossRef]
  • Shrivas, A.K.; Dewangan, A.K. An ensemble model for classification of attacks with feature selection based on KDD99 and NSL-KDD data set. Int. J. Comput. Appl. 2014, 99, 8–13. [Google Scholar]
  • Shabtai, A.; Moskovitch, R.; Feher, C.; Dolev, S.; Elovici, Y.J.S.I. Detecting unknown malicious code by applying classification techniques on opcode patterns. Secur. Inform. 2012, 1, 1. [Google Scholar] [CrossRef]
  • Ahmad, I.; Abdullah, A.B.; Alghamdi, A.S. Artificial neural network approaches to intrusion detection: A review. In Proceedings of the 8th Wseas International Conference on Telecommunications and Informatics, Istanbul, Turkey, 30 May–1 June 2009. [Google Scholar]
  • Soranamageswari, M.; Meena, C. A novel approach towards image spam classification. Int. J. Comput. Theory Eng. 2011, 3, 84. [Google Scholar] [CrossRef]
  • Chen, Y.; Narayanan, A.; Pang, S.; Tao, B. Multiple sequence alignment and artificial neural networks for malicious software detection. In Proceedings of the 2012 8th International Conference on Natural Computation, Chongqing, China, 29–31 May 2012; pp. 261–265. [Google Scholar]
  • Arram, A.; Mousa, H.; Zainal, A. Spam detection using hybrid Artificial Neural Network and Genetic algorithm. In Proceedings of the 2013 13th International Conference on Intellient Systems Design and Applications, Salangor, Malaysia, 8–10 December 2013; pp. 336–340. [Google Scholar]
  • Qureshi, A.-U.-H.; Larijani, H.; Mtetwa, N.; Javed, A.; Ahmad, J.J.C. RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection. Computers 2019, 8, 59. [Google Scholar] [CrossRef]
  • Sheikhan, M.; Jadidi, Z.; Farrokhi, A. Intrusion detection using reduced-size RNN based on feature grouping. Neural Comput. Appl. 2012, 21, 1185–1190. [Google Scholar] [CrossRef]
  • Liangboonprakong, C.; Sornil, O. Classification of malware families based on n-grams sequential pattern features. In Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), Melbourne, Australia, 19–21 June 2013; pp. 777–782. [Google Scholar]
  • Hardy, W.; Chen, L.; Hou, S.; Ye, Y.; Li, X. DL4MD: A deep learning framework for intelligent malware detection. In Proceedings of the International Conference on Data Mining (DMIN), Las Vegas, NV, USA, 12–15 July 2010; p. 61. [Google Scholar]
  • Foqaha, M.A.M. Email spam classification using hybrid approach of RBF neural network and particle swarm optimization. Int. J. Netw. Secur. Appl. 2016, 8, 17–28. [Google Scholar]
  • Bassiouni, M.; Ali, M.; El-Dahshan, E.A. Ham and Spam E-Mails Classification Using Machine Learning Techniques. J. Appl. Secur. Res. 2018, 13, 315–331. [Google Scholar] [CrossRef]
  • Gao, Y.; Wu, H.; Song, B.; Jin, Y.; Luo, X.; Zeng, X.J.I.A. A Distributed Network Intrusion Detection System for Distributed Denial of Service Attacks in Vehicular Ad Hoc Network. IEEE Access 2019, 7, 154560–154571. [Google Scholar] [CrossRef]
  • Siddiqui, M.; Wang, M.C.; Lee, J. Detecting internet worms using data mining techniques. J. Syst. Cybern. Inform. 2009, 6, 48–53. [Google Scholar]
  • Rathi, M.; Pareek, V. Spam mail detection through data mining-A comparative performance analysis. Int. J. Mod. Educ. Comput. Sci. 2013, 5, 31. [Google Scholar] [CrossRef]
  • Zhou, Y.-Y.; Cheng, G. An Efficient Network Intrusion Detection System Based on Feature Selection and Ensemble Classifier. arXiv 2019, arXiv:1904.01352. [Google Scholar]
  • Xu, H.; Sun, W.; Javaid, A. Efficient spam detection across online social networks. In Proceedings of the 2016 IEEE International Conference on Big Data Analysis (ICBDA), Hangzhou, China, 12–14 March 2016; pp. 1–6. [Google Scholar]
  • Gupta, G.P.; Kulariya, M. A framework for fast and efficient cyber security network intrusion detection using apache spark. Procedia Comput. Sci. 2016, 93, 824–831. [Google Scholar] [CrossRef]
  • Vinayakumar, R.; Alazab, M.; Soman, K.; Poornachandran, P.; Al-Nemrat, A.; Venkatraman, S.J.I.A. Deep Learning Approach for Intelligent Intrusion Detection System. IEEE Access 2019, 7, 41525–41550. [Google Scholar] [CrossRef]
  • Prakash Chandra, P.U.K.; Lilhore, P.N.A. Network intrusion detection system based on modified Random forest classifiers for kdd cup-99 and nsl-kdd Dataset. Int. Res. J. Eng. Technol. 2017, 4, 786–791. [Google Scholar]
  • Vivek Nandan Tiwari, P.S.R. Enhanced Method for Intrusion Detection over KDD Cup 99 Dataset. Int. J. Curr. Trends Eng. Technol. 2016, 2, 218–224. [Google Scholar]
  • Galal, H.S.; Mahdy, Y.B.; Atiea, M.A. Behavior-based features model for malware detection. J. Comput. Virol. Hacking Tech. 2016, 12, 59–67. [Google Scholar] [CrossRef]
  • Mosli, R.; Li, R.; Yuan, B.; Pan, Y. A behavior-based approach for malware detection. In Proceedings of the IFIP International Conference on Digital Forensics, Orlando, FL, USA, 30 January–1 February 2017. [Google Scholar]
  • Lee, S.M.; Kim, D.S.; Kim, J.H.; Park, J.S. Spam detection using feature selection and parameters optimization. In Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems, Krakow, Poland, 15–18 February 2010; pp. 883–888. [Google Scholar]
  • Mccord, M.; Chuah, M. Spam detection on twitter using traditional classifiers. In Proceedings of the International Conference on Autonomic and Trusted Computing, Banff, AB, Canada, 2–4 September 2011; pp. 175–186. [Google Scholar]
  • Jiang, L.; Zhang, H.; Cai, Z. A novel Bayes model: Hidden naive Bayes. IEEE Trans. Knowl. Data Eng. 2008, 21, 1361–1371. [Google Scholar] [CrossRef]
  • Panda, M.; Patra, M.R. Network intrusion detection using naive bayes. Int. J. Comput. Sci. Netw. Secur. 2007, 7, 258–263. [Google Scholar]
  • Fan, C.-I.; Hsiao, H.-W.; Chou, C.-H.; Tseng, Y.-F. Malware detection systems based on API log data mining. In Proceedings of the 2015 IEEE 39th Annual Computer Software and Applications Conference, Taichung, Taiwan, 1–5 July 2015; pp. 255–260. [Google Scholar]

This entry is adapted from the peer-reviewed paper 10.3390/en13102509

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