By: Syed Raiyan Ali – syedraiyanali@gmail.com, Department of computer science and Engineering( Data Science ), Student of computer science and Engineering( Data Science ), Madanapalle Institute Of Technology and Science, 517325, Angallu , Andhra Pradesh.
ABSTRACT
In today’s world networks are crucial in order to make sure that we have a safe environment over the internet which leads to cybersecurity being an important field of study. Networks require intruder detection systems (IDSs) for monitoring their software and hardware components but they encounter problems like improving accuracy of detection as well as lowering the number of false alarms besides discovering unidentified types of attacks. With machine learning (ML) it is possible to solve these problems, for example, by easily distinguishing between normal and abnormal data at high levels of accuracy and generalization. This paper discusses how ML has been incorporated in IDSs, various algorithms used and their effect on cybersecurity.
Keywords: Intrusion Detection Systems, Machine Learning, Cyber Security, Deep Learning, Anomaly Detection
INTRODUCTION
In the digital age, network security is crucial for its survival. Cyber-attacks are getting more advanced and traditional security measures seem to be unable to handle them. On the other hand, intrusion detection systems (IDSs) have been developed to monitor and detect possible security breaches but they still face notable challenges[1]. Thus, the inclusion of machine learning (ML) into IDSs provides a hopeful path to making them more effective. For instance, ML techniques allow IDSs to learn from data in order to identify patterns that signify a potential threat or an anomaly.
MACHINE LEARNING IN IDSs
The Role of Machine Learning
Many domains such as cybersecurity have been transformed by machine learning techniques. In the case of intrusion detection systems (IDSs), for example, ML algorithms can process large volumes of data so as to spot atypical patterns that might indicate an intrusion[2]. Different from conventional rule-driven systems, ML-based IDSs are quite flexible in learning from the history of similar occurrences and hence may be able to counter new variations of assaults. This feature is essential in sustaining solid safety amidst constantly changing network settings[3].
The below table shows the comparison of ML Algorithms in IDSs
Algorithm | Advantages | Disadvantages |
Decision Trees | Easy to interpret, fast | Prone to overfitting |
Support Vector Machines | Effective in high dimensions | Computationally intensive |
Neural Networks | Can handle complex patterns | Requires large datasets |
Deep Learning | High accuracy, handles big data | Long training times, requires huge resources |
Ensemble Methods | Improved accuracy, Robust | Complex implementation |
Table 1 Comparison of ML Algorithms in IDSs
TYPES OF MACHINE LEARNING ALGORITHMS USED
There are various types of ML algorithms that have been utilized in IDSs, each with its unique strengths and weaknesses[4]. Commonly used algorithms include:
- Decision Trees: Such algorithms are simple to interpret and operate on. They operate by breaking the data into smaller portions based upon the attributes’ ranges, resulting in a tree-like decision structure.
- Support Vector Machines (SVM): SVMs are quite effective in the high-dimensional areas and they mainly serve the purpose of classification. Their operation is based on discovering a hyperplane that acts as the optimal separator between classes of data.
- Neural Networks and Deep Learning: The handling of substantial datasets along with intricate designs is possible through algorithms including deep learning models. They are composed of dense levels of nerve cells capable of learning hierarchical representations pertaining to information.
- Ensemble Methods: To enhance precision and resilience in predictions, several models can be combined through methods such as Random Forests or Gradient Boosting.
PERFORMANCE METRICS AND BENCHMARK DATASETS
In fact, there are various measures for evaluating the performance of machine learning (ML) techniques that can be used in intrusion detection systems (IDSs), such as the precision-recall accuracy metrics, and F1 score. For training and testing an IDS, it is possible to use reference datasets like KDD Cup 99, NSL-KDD and UNSW-NB15. They contain labeled data about normal operations and different types of attacks on a network which provide means for comparing diverse ML techniques[5].
CHALLENGES AND FUTURE DIRECTIONS
In spite of the progress that has been made in the field of ML-based IDS, there are still many challenges that exist[6]. Essentially one of the major problems is the need for labeled data, which as it stands takes up a lot of time as well as cost. Moreover, ML models have to be constantly updated to embrace new versions of attacks. In terms of future research prospects, the preferred lines of investigation include developing unsupervised or semi-supervised learning methods so that they do not rely heavily on labeled data and working on improving the scalability and efficiency of real-time intrusion detection systems based on machine Learning.
CONCLUSION
The intrusion detection systems can be made more effective using machine learning, thereby improving their performance. By utilizing the power of machine learning (ML) algorithms in learning from data and identifying complicated patterns, intrusion detection systems (IDS) can detect both known and unknown threats with higher accuracy and adaptability. This is important because as cyber-attacks become increasingly sophisticated, it will be necessary to integrate advanced ML techniques into IDSs so as to ensure that networks remain secure at all times.
REFERENCES
- H. Liu and B. Lang, “Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey,” Appl. Sci., vol. 9, no. 20, Art. no. 20, Jan. 2019, doi: 10.3390/app9204396.
- L. Haripriya and M. A. Jabbar, “Role of Machine Learning in Intrusion Detection System: Review,” in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Mar. 2018, pp. 925–929. doi: 10.1109/ICECA.2018.8474576.
- M. Rahaman, C.-Y. Lin, P. Pappachan, B. B. Gupta, and C.-H. Hsu, “Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control,” Sensors, vol. 24, no. 13, Art. no. 13, Jan. 2024, doi: 10.3390/s24134157.
- I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput. Sci., vol. 2, no. 3, p. 160, Mar. 2021, doi: 10.1007/s42979-021-00592-x.
- T. Toto Haksoro, A. Aisjah, M. Rahaman, and T. R. Biyanto, “Enhancing Techno Economic Efficiency of FTC Distillation Using Cloud-Based Stochastic Algorithm,” Int. J. Cloud Appl. Comput., vol. 13, pp. 1–16, Jan. 2023, doi: 10.4018/IJCAC.332408.
- J. Asharf, N. Moustafa, H. Khurshid, E. Debie, W. Haider, and A. Wahab, “A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions,” Electronics, vol. 9, no. 7, Art. no. 7, Jul. 2020, doi: 10.3390/electronics9071177.
- Li, K. C., Gupta, B. B., & Agrawal, D. P. (Eds.). (2020). Recent advances in security, privacy, and trust for internet of things (IoT) and cyber-physical systems (CPS).
- Chaudhary, P., Gupta, B. B., Choi, C., & Chui, K. T. (2020). Xsspro: Xss attack detection proxy to defend social networking platforms. In Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9 (pp. 411-422). Springer International Publishing.
- Gupta, B. B., Gaurav, A., Arya, V., Alhalabi, W., Alsalman, D., & Vijayakumar, P. (2024). Enhancing user prompt confidentiality in Large Language Models through advanced differential encryption. Computers and Electrical Engineering, 116, 109215.
Cite As
Ali S.R. (2024) MACHINE LEARNING FOR INTRUSION DETECTION SYSTEMS, Insights2Techinfo, pp.1