Detectcy: ML-based Advanced Intrusion Detection System (IDS)
Keywords:
Anomaly detection, Cybersecurity, Decision tree, Dimensionality reduction, Feature selection, Intrusion detection system (IDS), Machine learning, Network security, Random forest, Real-time monitoring, Support vector machine (SVM), Threat detectionAbstract
Cybersecurity risks have gotten more sophisticated and frequent, with the rise of digital communication and interconnected systems. Conventional intrusion detection systems (IDS) rely heavily on signature-based methodologies, limiting their ability to detect new or previously unknown threats. To overcome this drawback, this study presents Detectcy, an advanced IDS (software) that incorporates machine learning techniques to enable intelligent, adaptive, and more effective threat detection. Detectcy uses supervised learning algorithms like random forest, support vector machine (SVM), and decision tree to analyze network data and classify it as normal or malicious. To achieve reliable detection, the system comprises critical operations such as data gathering, preprocessing, feature extraction, and model training. Detectcy enhances efficiency and performance by utilizing feature selection and dimensionality reduction approaches. The proposed system is capable of detecting multiple types of cyber-attacks, including denial of service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R) attacks. It also provides real-time monitoring and automated alert generation, enabling quick response to potential threats. Overall, Detectcy enhances detection accuracy, minimizes incorrect alerts while providing a flexible and scalable solution that can be effectively deployed in modern environments, including cloud-based platforms and IoT networks.
References
X. Li and Y. Zhou, “A quantum public-key cryptosystem with reusable keys using entangled states,” Applied Sciences, vol. 16, no. 7, p. 3335, Mar. 2026.
A. Alshammari, “A unified low-carbon cybersecurity framework integrating energy-efficient intrusion detection, lightweight cryptography, and carbon-aware scheduling for edge-cloud architectures,” Scientific Reports, vol. 16, no. 1, Mar. 2026.
A. Lunkeit and W. Zimmer, “Security engineering ist systems engineering,” in Security by Design. Berlin, Heidelberg: Springer Vieweg, 2021, ch. 3.
J. Thistlethwaite and D. Henstra, “Policy instruments to strengthen the cybersecurity of critical infrastructure,” Journal of Cyber Policy, pp. 1–21, 2026.
M. Farhan, H. Waheed ud Din, S. Ullah et al., “Network-based intrusion detection using a deep learning technique,” Scientific Reports, vol. 15, p. 25550, 2025.
K. Scarfone and P. Mell, Guide to Intrusion Detection and Prevention Systems (IDPS). Gaithersburg, MD, USA: National Institute of Standards and Technology (NIST), Feb. 2007.
E. Iasiello, “What is the role of cyber operations in information warfare?” Journal of Strategic Security, vol. 14, no. 4, pp. 72–86, 2021.
G. Bendiab, J. Rose, M. Swann, and S. Shiaeles, “Utilising honeypots and machine learning to mitigate unknown threats in IoT,” in Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation, 2022, ch. 6.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015.
M. M. Taye, “Understanding of machine learning with deep learning: Architectures, workflow, applications and future directions,” Computers, vol. 12, no. 5, Apr. 2023.
D. Zhao, Y. Gu, Z. Chen, Y. Liu, B. Chen, and J. Li, “Knowledge-guided interpretable machine learning framework for ladle furnace desulphurisation control,” Processes, vol. 14, no. 7, p. 1118, Mar. 2026.
C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
Ankita, “A review paper on data mining concepts and its techniques,” Journal of Advances and Scholarly Researches in Allied Education, vol. 14, no. 2, Jan. 2018.
A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd ed. Sebastopol, CA, USA: O’Reilly Media, 2019.
I. H. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN Computer Science, vol. 2, no. 3, pp. 1–21, Mar. 2021.
S. Badillo et al., “An introduction to machine learning,” Clinical Pharmacology & Therapeutics, vol. 107, no. 4, pp. 871–885, Mar. 2020.
Y. Li, D. Sun, and L. Zhang, “Unsupervised feature selection via nonnegative orthogonal constrained regularized minimization,” Journal of Machine Learning Research, vol. 27, no. 39, pp. 1–44, 2026.
W. Stallings, Network Security Essentials: Applications and Standards, 6th ed. Boston, MA, USA: Pearson, 2017.
A. Baihan, N. Kryvinska, M. Amoon et al., “Cloud-assisted blockchain-enabled split federated learning framework for security and privacy-preserving IoMT in Healthcare 5.0,” Scientific Reports, 2026.
