A novel landmine detection system based on within and between subclasses dispersion information

Tbarki, Khaoula; Ksantini, Riadh; Said, Salma Ben; Lachiri, Zied

doi:10.1080/01431161.2021.1958389

Cited by 5 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SDOSVM is outperformed by the Deep SDOSVM for all subsets except for Subset-3, where both classifiers provide the same AUC result. This can be attributed to the usage of the K-means clustering algorithm, as in [41], by the SDOSVM to generate the subclasses based on Euclidean distance-based metrics, and these metrics are not adequate for the discovery of representative similarities between data points as they assume that the clusters are spherical, and therefore we cannot perform good clustering in spherical form. The DynAE clustering model used by the Deep SDOSVM is free from such an assumption on clusters, and therefore, generated better AUC results.…”

Section: E Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Intrusion Detection System for Internet of Healthcare Things Based on Deep Subclasses Dispersion Information

Fouda

Ksantini

Elmedany

2023

IEEE Internet Things J.

View full text Add to dashboard Cite

Despite the significant benefits that the Internet of Healthcare Things (IoHT) offered to the medical sector, there are concerns and risks regarding these systems which can delay their wide deployment as they handle sensitive, and often life-critical medical information. In addition to the IoHT concerns and risks, there are security constraints which include hardware, software, and network constraints that pose a security challenge to these systems. Therefore, security measures need to be deployed that can overcome the concerns, mitigate the risks, and meet the constraints of the IoHT. For these reasons, a subclasses intrusion detection system for the IoHT is proposed in this research work based on a novel variation of the standard One-class Support Vector Machine (OSVM), namely Deep Subclass Dispersion Oneclass Support Vector Machine (Deep SDOSVM), which considers subclasses in the target class, i.e. normal class, in order to minimize the data dispersion within and between subclasses, thereby improving the discriminative power and classification performance of the intrusion detection system. A deep clustering model is used for subclasses generation in the proposed Deep SDOSVM approach, namely the Dynamic Autoencoder Model (DynAE), to overcome the drawbacks of the classical clustering algorithms, and further enhance the classification performance of the intrusion detection system. The proposed deep clustering subclasses intrusion detection system was evaluated on the realworld TON IoT dataset, and compared to other state-of-theart one-class classifiers. Experimentation results showed that the proposed approach outperformed the other relevant one-class classifiers for network intrusion detection.

show abstract

Section: E Results and Discussionmentioning

confidence: 99%

“…The linear activation is used in the experimentation [40]. 6) SDOSVM: This classifier was built upon the OSVM as described in section III-C, and uses the K-means clustering algorithm to generate the subclasses as in [41].…”

Section: Classifiersmentioning

confidence: 99%