An effective semi-supervised model for intrusion detection using feature selection based LapSVM

Zhang, Xiaofeng; Zhu, Peidong; Tian, Jing; Zhang, Jiexin

doi:10.1109/cits.2017.8035323

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…LoRa transmitter akan merubah data real menjadi data string untuk mengirimkan data string ke receiver. Dari data receiver akan mengirimkan ke Arduino 2 untuk memperoses data string yang akan dikirimkan ke Get web server melalui ESP 32 [12]. Dari data web server akan diproses ke aplikasi dan ke lcd untuk menapilkan nilai dari sensor suhu dan kelembapan tanah.…”

Section: Desain Perancangan Sistemunclassified

Perancangan Monitoring Teknologi Long Range (LoRa) Untuk Mendeteksi Kekeringan Tanah Berbasis IoT

Safii,

Seltika,

Vidy

et al. 2024

FIFO

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Suhu dan kelembaban tanah merupakan faktor penting dalam mempengaruhi kondisi pertumbuhan tanaman. Pada saat ini untuk memonitoring suhu dan kelembaban tanah harus memantau secara langsung dan masih menggunakan cara manual yaitu menggunakan moisture meter dan termometer tanah. Seiring perkembangan zaman, teknologi informasi dan komunikasi diterapkan pada pertanian seperti penggunaan sensor dan mikrokontroler. LoRa merupakan teknologi jaringan nirkabel yang dirancang khusus untuk komunikasi jarak jauh, sehingga dapat digunakan sebagai alat mengirim data monitoring dalam penggunaan sensor dan mikrokontroler, sistem ini berbasis Internet of Things (IoT), yang akan memudahkan pengguna memonitoring sistem melalui aplikasi android. Sensor suhu mampu mengakuisisi data dengan rata-rata error 3.1% sedangkan sensor kelembaban tanah mampu mengakusisi data dengan rata-rata error 7.3%. Dengan harapan alat monitoring suhu dan kelembaban tanah mampu membantu petani mengurangi berbagai dampak buruk kondisi pertumbuhan tanaman.

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Section: Desain Perancangan Sistemunclassified

Perancangan Monitoring Teknologi Long Range (LoRa) Untuk Mendeteksi Kekeringan Tanah Berbasis IoT

Safii,

Seltika,

Vidy

et al. 2024

FIFO

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“…It is well recognized that because there is no set pattern for monitoring, aberrant intrusions are exceedingly challenging to find. An occurrence is typically deemed abnormal if it occurs considerably more frequently or less frequently than a threshold [9]. Some AI methods employ tree-based algorithms like decision trees and random forests, which can build a structure for successfully detecting infiltration.…”

Section: Related Workmentioning

confidence: 99%

Proposed algorithm base optimization scheme for intrusion detection using feature selection

Laassar,

Hadi

2024

IJAAS

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<span>The number of devices linked to the internet is rapidly increasing as the internet has become ingrained in every aspect of modern life. However, certain issues are getting worse, and their resolutions are not well-defined. One of the main issues is convergence and speed for communication between different internet of things (IoT) devices and their security. For that purpose, in this paper, an improved artificial bee colony (ABC) algorithm with binary search equations along with neural networks is proposed, known as the artificial bee colony algorithm with binary search equations (BABCN) algorithm for intrusion detection in terms of convergence and speed for communication. The depth-first search framework and binary search equations on which the artificial bee colony algorithm with binary search equations algorithm is built improve the algorithm’s capacity for exploitation and speed up convergence. The initial weight and threshold value of the ABC neural networks are optimized using an algorithm to prevent them from entering a local optimum during the training procedure and accelerating training. The NSL-KDD dataset was used, and based on the results; the proposed algorithm improves classification and has high intrusion detection ability in the network. The proposed has undergone tests to be evaluated, and the results show that it performs better in detection accuracy, time, and false positive rate.</span>

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“…The authors [1] developed a system that used LapSVM to integrate labeled and unlabeled data to improve classification results. The research gap is that the approach requires more memory and processing speed.…”

Section: Related Workmentioning

confidence: 99%

“…1. Network Intrusion Detection System [NIDS] [1][2]: The NIDS structure or service must be deployed within the system architecture, and it can be launched in one or more locations. In the system It searches for and analyses incoming and outgoing traffic to and from aforementioned system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Network Intrusion Detection Framework on Sparse Deep Denoising Autoencoder for Dimensionality Reduction

et al. 2023

Preprint

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"Network security" is currently among the crucial areas of computer science. Due to the proliferation of IoT tools and "peer-to-peer" systems, the necessity to mitigate safety concerns is prominent. Intrusion detection in network systems is employed to analyse network packets for malicious activity. Different kinds of attacks, such as "Denial of Service", "Probe", "Remote-to-Local", and "User-to-Root" are instances of unexpected behaviour. Once attacks are spotted, customized alerts can be sent to the personnel responsible. In numerous application fields of information security, intrusion detection is a necessity in the modern era. In this study, we examined the usefulness of several autoencoder types in identifying network intrusions. In this research, a system for network intrusion detection based on Sparse Deep Denoising Autoencoder for dimension reduction was established. If the autoencoder is trained on regular network information, reconstruction error (the dissimilarity between the original and recreated data) is used to identify anomalies. Intrusion Detection System Development utilized standard datasets such as KDDCup99, NSL-KDD, UNSW-NB15, and NMITIDS. The effectiveness of four distinct autoencoders was analysed to identify network attacks. We were able to achieve over 96% accuracy with only reconstruction error by using a sparse deep denoising autoencoder. The primary research aim was to enhance the performance of the network by achieving high intrusion detection precision.

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An effective semi-supervised model for intrusion detection using feature selection based LapSVM

Cited by 9 publications

References 11 publications

Perancangan Monitoring Teknologi Long Range (LoRa) Untuk Mendeteksi Kekeringan Tanah Berbasis IoT

Perancangan Monitoring Teknologi Long Range (LoRa) Untuk Mendeteksi Kekeringan Tanah Berbasis IoT

Proposed algorithm base optimization scheme for intrusion detection using feature selection

A Network Intrusion Detection Framework on Sparse Deep Denoising Autoencoder for Dimensionality Reduction

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