Wyszukiwarka
Abstrakt
Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-STM). Themodified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, espectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.
Cytowania
-
1
CrossRef
-
0
Web of Science
-
2
Scopus
Autorzy (6)
-
Siva Surya Narayana Chintapalli
-
Satya Prakash Singh
-
Jaroslav Frnda
-
Bidare Parameshachari Divakarachar
-
Vijaya Lakshmi Sarraju
Cytuj jako
Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.heliyon.2024.e29410
- Licencja
-
otwiera się w nowej karcie
Słowa kluczowe
Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
-
Heliyon
nr 10,
ISSN: 2405-8440 - Język:
- angielski
- Rok wydania:
- 2024
- Opis bibliograficzny:
- Narayana Chintapalli S. S., Prakash Singh S., Frnda J., Divakarachar B. P., Sarraju V. L., Falkowski-Gilski P.: OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems// Heliyon -Vol. 10,iss. 8 (2024), s.e29410-
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.heliyon.2024.e29410
- Źródła finansowania:
-
- Publikacja bezkosztowa
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 49 razy
Publikacje, które mogą cię zainteresować
An automated learning model for twitter sentiment analysis using Ranger AdaBelief optimizer based Bidirectional Long Short Term Memory
- S. Natarajan,
- S. Kurian,
- P. Bidare Divakarachari
- + 1 autorów
Cascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function
- S. N. Shivappriya,
- M. J. P. Priyadarsini,
- A. Stateczny
- + 2 autorów
Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm
- J. Sengupta,
- R. Alzbutas,
- P. Falkowski-Gilski
- + 1 autorów
Study of Multi-Class Classification Algorithms’ Performance on Highly Imbalanced Network Intrusion Datasets
- V. Bulavas,
- V. Marcinkevičius,
- J. Rumiński