A Literature Survey on the Importance of Intrusion Detection System for Wireless Networks
DOI:
https://doi.org/10.51983/ajcst-2018.7.3.1905Keywords:
Network Security, Cloud Computing, Sensor Networks, Ad Hoc Networks, Internet of ThingsAbstract
Network security has become more important to personal computer users, organizations, and the military. With the advent of the internet, security became a major concern and the history of security allows a better understanding of the emergence of security technology. The entire field of network security is vast and in an evolutionary stage. The range of study encompasses a brief history dating back to internet’s beginnings and the current development in network security. In order to understand the research being performed today, background knowledge of the importance of security, types of attacks in the networks. This paper elaborates theliterature study on network security in various domains in the year 2013 to 2018. Finally, it summarizes the research directions by literature survey.
References
R. M. Elbasiony et al., "A hybrid network intrusion detection framework based on random forests and weighted k-means," Ain Shams Engineering Journal, vol. 4, no. 4, pp. 753-762, 2013.
S. A. Joshi and V. S. Pimprale, "Network Intrusion Detection System (NIDS) based on data mining," International Journal of Engineering Science and Innovative Technology (IJESIT), vol. 2, no. 1, pp. 95-98, 2013.
S. Ganapathy et al., "Intelligent feature selection and classification techniques for intrusion detection in networks: a survey," EURASIP Journal on Wireless Communications and Networking, vol. 1, pp. 271, 2013.
P. Louvieris, N. Clewley, and X. Liu, "Effects-based feature identification for network intrusion detection," Neurocomputing, vol. 121, pp. 265-273, 2013.
J. Yu et al., "An in-depth analysis on traffic flooding attacks detection and system using data mining techniques," Journal of Systems Architecture, vol. 59, no. 10, pp. 1005-1012, 2013.
M. H. Bhuyan et al., "Detecting distributed denial of service attacks: methods, tools and future directions," The Computer Journal, vol. 57, no. 4, pp. 537-556, 2013.
I. Ahmad et al., "Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components," Neural computing and applications, vol. 24, no. 7-8, pp. 1671-1682, 2014.
W. Feng et al., "Mining network data for intrusion detection through combining SVMs with ant colony networks," Future Generation Computer Systems, vol. 37, pp. 127-140, 2014.
W. Li et al., "A new intrusion detection system based on KNN classification algorithm in wireless sensor network," Journal of Electrical and Computer Engineering, 2014.
F. Kuang et al., "A novel hybrid KPCA and SVM with GA model for intrusion detection," Applied Soft Computing, vol. 18, pp. 178-184, 2014.
R. Chitrakar and C. Huang, "Selection of candidate support vectors in incremental SVM for network intrusion detection," computers & security, vol. 45, pp. 231-241, 2014.
G. V. Nadiammai and M. Hemalatha, "Effective approach toward Intrusion Detection System using data mining techniques," Egyptian Informatics Journal, vol. 15, no. 1, pp. 37-50, 2014.
G. Kim et al., "A novel hybrid intrusion detection method integrating anomaly detection with misuse detection," Expert Systems with Applications, vol. 41, no. 4, pp. 1690-1700, 2014.
Shamshirband et al., "Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks," Engineering Applications of Artificial Intelligence, vol. 32, pp. 228-241, 2014.
E. Jeong and B. Lee, "An IP Traceback Protocol using a Compressed Hash Table, a Sinkhole router and data mining based on network forensics against network attacks," Future Generation Computer Systems, vol. 33, pp. 42-52, 2014.
S. Pan, T. Morris, and U. Adhikari, "Developing a hybrid intrusion detection system using data mining for power systems," IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 3104-3113, 2015.
M. A. Faisal et al., "Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study," IEEE Systems journal, vol. 9, no. 1, pp. 31-44, 2015.
S. Elhag et al., "On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems," Expert Systems with Applications, vol. 42, no. 1, pp. 193-202, 2015.
A. S. Eesa et al., "A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems," Expert Systems with Applications, vol. 42, no. 5, pp. 2670-2679, 2015.
K. M. Ali Alheeti et al., "An intrusion detection system against malicious attacks on the communication network of driverless cars," Consumer Communications and Networking Conference (CCNC), 2015 12th Annual IEEE. IEEE, 2015.
K. A. P. Costa et al., "A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks," Information Sciences, vol. 294, pp. 95-108, 2015.
O. Osanaiye et al., "Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing," EURASIP Journal on Wireless Communications and Networking, 2016, vol. 1, pp. 130.
K. Keerthi Vasan and B. Surendiran, "Dimensionality reduction using Principal Component Analysis for network intrusion detection," Perspectives in Science, vol. 8, pp. 510-512, 2016.
N. Keegan et al., "A survey of cloud-based network intrusion detection analysis," Human-centric Computing and Information Sciences, vol. 6, no. 1, pp. 19, 2016.
S. Ji et al., "A multi-level intrusion detection method for abnormal network behaviors," Journal of Network and Computer Applications, vol. 62, pp. 9-17, 2016.
S. M. Hosseini Bamakan et al., "An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization," Neurocomputing, vol. 199, pp. 90-102, 2016.
A. A. Aburomman and M. B. I. Reaz, "A novel SVM-kNN-PSO ensemble method for intrusion detection system," Applied Soft Computing, vol. 38, pp. 360-372, 2016.
A. Ashfaq et al., "Fuzziness based semi-supervised learning approach for intrusion detection system," Information Sciences, vol. 378, pp. 484-497, 2017.
J. Kevric et al., "An effective combining classifier approach using tree algorithms for network intrusion detection," Neural Computing and Applications, vol. 28, no. 1, pp. 1051-1058, 2017.
W. Laftah Al-Yaseen et al., "Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system," Expert Systems with Applications, vol. 67, pp. 296-303, 2017.
E. Hodo et al., "Shallow and deep networks intrusion detection system: A taxonomy and survey," arXivpreprintar, vol. XIV: 1701.02145, 2017.
I. S. Thaseen and C. A. Kumar, "Intrusion detection model using fusion of chi-square feature selection and multi class SVM," Journal of King Saud University-Computer and Information Sciences, vol. 29, no. 4, pp. 462-472, 2017.
M. R. G. Raman et al., "A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems," Neural Networks, vol. 92, pp. 89-97, 2017.
A. F. M. Agarap, "A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data," in Proceedings of the 2018 10th International Conference on Machine Learning and Computing, ACM, 2018.
S. A. R. Shah and B. Issac, "Performance comparison of intrusion detection systems and application of machine learning to Snort system," Future Generation Computer Systems, vol. 80, pp. 157-170, 2018.
W. Meng et al., "Enhancing Trust Management for Wireless Intrusion Detection via Traffic Sampling in the Era of Big Data," IeeeAccess, vol. 6, pp. 7234-7243, 2018.
K. Cabaj, M. Gregorczyk, and W. Mazurczyk, "Software-defined networking-based crypto ransomware detection using HTTP traffic characteristics," Computers & Electrical Engineering, vol. 66, pp. 353-368, 2018.
Y. Ye et al., "DeepAM: a heterogeneous deep learning framework for intelligent malware detection," Knowledge and Information Systems, vol. 54, no. 2, pp. 265-285, 2018.
S. K. Singh et al., "Joint-Transformation-Based Detection of False Data Injection Attacks in Smart Grid," IEEE Transactions on Industrial Informatics, vol. 14, no. 1, pp. 89-97, 2018.
L. Li et al., "Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO," Journal of Sensors, 2018.
K. Demertzis and L. Iliadis, "A hybrid network anomaly and intrusion detection approach based on evolving spiking neural network classification," in International Conference on e-Democracy, Springer, Cham, 2013.
I. Santos et al., "Opcode sequences as representation of executables for data-mining-based unknown malware detection," Information Sciences, vol. 231, pp. 64-82, 2013.
C. I. Pinzon et al., "idMAS-SQL: intrusion detection based on MAS to detect and block SQL injection through data mining," Information Sciences, vol. 231, pp. 15-31, 2013.
D. Zhao et al., "Botnet detection based on traffic behavior analysis and flow intervals," Computers & Security, vol. 39, pp. 2-16, 2013.
Y. Ding et al., "A fast malware detection algorithm based on objective-oriented association mining," computers & security, vol. 39, pp. 315-324, 2013.
N. Hubballi and V. Suryanarayanan, "False alarm minimization techniques in signature-based intrusion detection systems: A survey," Computer Communications, vol. 49, pp. 1-17, 2014.
G. Creech and J. Hu, "A semantic approach to host-based intrusion detection systems using contiguous and discontiguous system call patterns," IEEE Transactions on Computers, vol. 63, no. 4, pp. 807-819.
W. Wang et al., "Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks," Knowledge-Based Systems, vol. 70, pp. 103-117, 2014.
A. Alazab et al., "Using response action with intelligent intrusion detection and prevention system against web application malware," Information Management & Computer Security, vol. 22, no. 5, pp. 431-449, 2014.
I. Idris and A. Selamat, "Improved email spam detection model with negative selection algorithm and particle swarm optimization," Applied Soft Computing, vol. 22, pp. 11-27, 2014.
Y. Ki et al., "A novel approach to detect malware based on API call sequence analysis," International Journal of Distributed Sensor Networks, vol. 11, no. 6, pp. 659101, 2015.
B. Shah and B. H. Trivedi, "Improving performance of mobile agent based intrusion detection system," in Advanced Computing & Communication Technologies (ACCT), 2015 Fifth International Conference on IEEE, 2015.
M. Zhang et al., "An anomaly detection model based on one-class svm to detect network intrusions," in Mobile Ad-hoc and Sensor Networks (MSN), 2015 11th International Conference on, IEEE, 2015.
N. Khamphakdee et al., "Improving intrusion detection system based on snort rules for network probe attacks detection with association rules technique of data mining," Journal of ICT Research and Applications, vol. 8, no. 3, pp. 234-250, 2015.
S. Sangeetha et al., "Signature based semantic intrusion detection system on cloud," in Information Systems Design and Intelligent Applications. Springer, New Delhi, pp. 657-666, 2015.
Y. Fan et al., "Malicious sequential pattern mining for automatic malware detection," Expert Systems with Applications, vol. 52, pp. 16-25, 2016.
H. Galal et al., "Behavior-based features model for malware detection," Journal of Computer Virology and Hacking Techniques, vol. 12, no. 2, pp. 59-67, 2016.
A. Garg and P. Maheshwari, "Performance Analysis of Snort-based Intrusion Detection System," in Advanced Computing and Communication Systems (ICACCS), 3rd International Conference on IEEE, vol. 1, 2016.
K. Böhmer and S. Rinderle-Ma, "Automatic signature generation for anomaly detection in business process instance data," in Enterprise, Business-Process and Information Systems Modeling. Springer, Cham, pp. 196-211, 2016.
A. Saracino et al., "Madam: Effective and efficient behavior-based android malware detection and prevention," IEEE Transactions on Dependable and Secure Computing, 2016.
E. Viegas et al., "Towards an energy-efficient anomaly-based intrusion detection engine for embedded systems," IEEE Transactions on Computers, vol. 66, no. 1, pp. 163-177, 2017.
M. M. Baig et al., "A multiclass cascade of artificial neural network for network intrusion detection," Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2875-2883, 2017.
C. Feng et al., "Multi-level anomaly detection in industrial control systems via package signatures and lstm networks," in Dependable Systems and Networks (DSN), 2017 47th Annual IEEE/IFIP International Conference on IEEE, 2017.
W. Meng et al., "A bayesian inference-based detection mechanism to defend medical smartphone networks against insider attacks," Journal of Network and Computer Applications, vol. 78, pp. 162-169, 2017.
Z. Hu et al., "Anomaly detection system in secure cloud computing environment," International Journal of Computer Network and Information Security, vol. 9, no. 4, pp. 10, 2017.
E. K. Viegas et al., "Toward a reliable anomaly-based intrusion detection in real-world environments," Computer Networks, vol. 127, pp. 200-216, 2017.
S. Aljawarneh et al., "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model," Journal of Computational Science, vol. 25, pp. 152-160, 2018.
H. Hamamoto et al., "Network anomaly detection system using genetic algorithm and fuzzy logic," Expert Systems with Applications, vol. 92, pp. 390-402, 2018.
Y. Wang et al., "A fog-based privacy-preserving approach for distributed signature-based intrusion detection," Journal of Parallel and Distributed Computing, vol. 122, pp. 26-35, 2018.
Y. Cohen et al., "Detection of malicious webmail attachments based on propagation patterns," Knowledge-Based Systems, vol. 141, pp. 67-79, 2018.
M. Rezvani, "Assessment Methodology for Anomaly-Based Intrusion Detection in Cloud Computing," Journal of AI and Data Mining, vol. 6, no. 2, pp. 387-397, 2018.
S. M. Sohi et al., "Recurrent Neural Networks for Enhancement of Signature-based Network Intrusion Detection Systems," arXivpreprintarXiv: 1807.03212, 2018.
V. Hajisalem and S. Babaie, "A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection," Computer Networks, vol. 136, pp. 37-50, 2018.
N. Sainis et al., "Feature Classification and Outlier Detection to Increased Accuracy in Intrusion Detection System," International Journal of Applied Engineering Research, vol. 13, no. 10, pp. 7249-7255, 2018.
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