A Network-Based Framework for Mobile Threat Detection

Kumar, Sanjay; Viinikainen, Ari; Hämäläinen, Timo D.

doi:10.1109/icdis.2018.00044

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…In [9], the researchers proposed a ML classification model in creating the network-based IDS and is mainly for the threats induced in mobile devices network. As we know the threats in the mobile world increase rapidly, and the attackers steal sensitive information, exploiting the users by sending unwanted SMS.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…KL divergence changes close to similar data points focuses on joint probabilities. The expansion of this term to loss function and it benefits dimensionality decreases as shown in equation (9).…”

Section: Parameterized Rectified Linear Activation Functionmentioning

confidence: 99%

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

et al. 2023

Preprint

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“…Either signature (misuse detection) of well-known assaults or Behaviour-based techniques are utilized to detect these behaviours (anomaly detection). Behaviourbased systems identify deviations from the typical behaviour profile and can detect unknown attacks, whereas signature-based systems can only detect attacks for which they are trained to notify [26,27].…”

Section: Intrusion Detection System (Ids) and Machine Learningmentioning

confidence: 99%

Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System

2022

IJIES

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Unsupervised machine learning plays a critical role in improving the security level of applications and systems. The cyberattack floods the network with data streams to deny services or destroy the network infrastructure. In this paper, a new development strategy (dynamic evolving cauchy possibilistic clustering based on the self-similarity principle (DECS)) is proposed to optimize the data stream clustering model based on the self-similarity principles (inter-cluster and intra-cluster). It is based on computing the self-similarity principles for data between and within clusters. The proposed system mitigates the dependence on the centre of the cluster to produce clusters with highly correlated data and minimal errors within the same cluster. The DECS consists of two phases: In the first phase, the data are clustered based on density points to generate clusters, and in the second phase, the clusters are evolved according to the inter-and intra-distance points to/in the clusters. The proposed model is tested on well-known intrusion datasets such as UNSW-NB15, KDD99 and NSL-KDD. In the experiments, DECS efficiently clustered the data with fewer errors and an optimal number of clusters compared to other models, it has achieved minimum error, high silhouette coefficient and an optimal number of clusters. The implementation results show that the average error of DECS over eight datasets is (0.45) with an average silhouette coefficient (0.63). At the same time, the pure cauchy possibilistic clustering obtains on average error (0.69) and silhouette coefficient (0.44).

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“…Each application assigns a unique User ID and a set of permissions at the app installation time. Android developers must request permission to use these special features in a standard format that is parsed at installation time [2]. The permission model used in Android has many advantages and can be effective in preventing malware while also informing users what applications are capable of doing once installed [3].…”

Section: Introductionmentioning

confidence: 99%

Exploring permissions in android applications using ensemble-based extra tree feature selection

al-kaaf

Ali

Shamsuddin

et al. 2020

IJEECS

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<span>The fast development of mobile apps and its usage has led to increase the risk of exploiting user privacy. One method used in Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, that permissions could be exploited by attackers when granting certain combinations of permissions. So, the aim of this paper is to explore the pattern of malware apps based on analyzing permissions by proposing framework utilizing feature selection based on ensemble extra tree classifier method and machine learning classifier. The used dataset had 25458 samples (8643 malware apps & 16815 benign apps) with 173 features. Three dataset with 25458 samples and 5, 10 and 20 features respectively were generated after using the proposed feature selection method. All the dataset was fed to machine learning. Support Vector machine (SVM), K Neighbors Classifier, Decision Tree, Naïve bayes and Multilayer Perceptron (MLP) classifiers were used. The classifiers models were evaluated using true negative rate (TNR), false positive rate (FNR) and accuracy metrics. The experimental results obtained showed that Support Vector machine and KNeighbors Classifiers with 20 features achieved the highest accuracy with 94 % and TNR with rate of 89 % using KNeighbors Classifier. The FNR rate is dropped to 0.001 using 5 features with support vector machine (SVM) and Multilayer Perceptrons (MLP) classifiers. The result indicated that reducing permission features improved the performance of classification and reduced the computational overhead.</span>

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A Network-Based Framework for Mobile Threat Detection

Cited by 7 publications

References 18 publications

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

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

Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System

Exploring permissions in android applications using ensemble-based extra tree feature selection

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