An Optimized and Hybrid Framework for Image Processing Based Network Intrusion Detection System

Siddiqi, Murtaza Ahmed; Pak, Wooguil

doi:10.32604/cmc.2022.029541

Cited by 3 publications

(8 citation statements)

References 34 publications

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“…The proposed framework was able to achieve a precision of almost 98% on the dataset with only 72 features. In contrast to our earlier work [17], which achieved a slightly higher precision on the same dataset but with 79 features. While the other competitors were not able to achieve a precision higher than the suggested framework.…”

Section: Discussioncontrasting

confidence: 92%

“…[16] Converting the non-image dataset to a 2D-Gray scale image for a CNN-based NIDS [17] Transforming non-image dataset into images based on Deepnsight and Gabor filter for CNN-based NIDS [42] The DeepInsight methodology was used to turn non-image data into images for a CNN-based NIDS To provide comparable grounds for evaluation, the proposed framework and comparison methods are implemented with the same parameter settings. Such as the datasets after pre-processing, and the CNN model for classification.…”

Section: Referencementioning

confidence: 99%

“…Hence, a refined and scalable intrusion detection method is essential to counter the cybersecurity concern. With the advancements in Deep Learning (DL) and image processing, security experts are exploring the possibilities of using DL and image processing for NIDS [13][14][15][16][17]. DL is an improved form of the neural network (NN) as it overcomes three significant training phase issues of NN i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Such issues have opened doors for further exploration of methods that can improve the conversion of non-image data into images. In our earlier study [17], we implemented the image processingbased NIDS with CNN based classifier. In that study, we used all the features of the implemented datasets.…”

Section: Introductionmentioning

confidence: 99%

“…The inclusion of all features was based on the notion that higher pixel images are conducive to detecting anomalies [22]. In the prior study [17] the accuracy of anomaly detection was above 90% on all the datasets. This work is an enhancement to the earlier work by augmenting the proposed framework through feature selection.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Tier-Based Optimization for Synthesized Network Intrusion Detection System

Siddiqi

Pak

2022

IEEE Access

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The innovation and evolution of hacking methodologies have led to a sharp rise in cyber attacks, highlighting the need for enhanced network security approaches. Network intrusion detection systems based on machine learning are playing a significant role in the domain of network security. However, designing an optimal framework for a network intrusion detection system is an ongoing concern. In this study, an optimal framework for a network intrusion detection system based on image processing is proposed. The framework is a fusion of augmented feature selection flow with an image transformation and enhancement methodology. Initially, the proposed framework reduces the number of features to achieve overall efficiency. Later, the non-image data is transformed into images. The transformed images are then enhanced for achieving effective anomaly detection based on a deep-learning classifier. The proposed method is implemented on three diverse benchmark datasets of intrusion detection. To illustrate the efficiency of the proposed framework it is compared with some of the most recent publications on imageprocessing-based network intrusion detection systems.

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Section: Discussioncontrasting

confidence: 92%

Section: Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tier-Based Optimization for Synthesized Network Intrusion Detection System

Siddiqi

Pak

2022

IEEE Access

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Enhanced analysis of tabular data through Multi-representation DeepInsight

Sharma,

López,

Jia

et al. 2024

Sci Rep

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Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields.

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An effective attention and residual network for malware detection

Gu,

Xing,

Hou

2024

IET Communications

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Due to its open source and large user base, Android has emerged as the most popular operating system. Android's popularity and openness have made it a prime target for malicious attackers. Permissions have received great attention from researchers because of their effectiveness in restricting applications’ access to sensitive resources. However, existing malware detection methods based on permissions are easily bypassed by inter‐application resource access. To address these issues, we combine inter‐application resource access‐related intent features with permission features. Besides, we designed a customized convolutional neural network using two squeeze‐and‐excitation blocks to learn the inherent relationships between multi‐type features. The two basic SE blocks perform squeezing operations based on average pooling and max pooling, respectively, to compute channel‐wise attention from multiple perspectives. We designed a series of experiments based on real‐world samples to evaluate the efficacy of the proposed framework. Empirical results demonstrate that our framework outperforms state‐of‐the‐art methods, achieving an accuracy of 96.29%, precision of 97.52%, recall of 94.63%, F1‐score of 96.06% and MCC of 92.60%. These promising experimental results consistently demonstrate that AMERDroid is an effective approach for Android malware detection.

show abstract

An Optimized and Hybrid Framework for Image Processing Based Network Intrusion Detection System

Cited by 3 publications

References 34 publications

Tier-Based Optimization for Synthesized Network Intrusion Detection System

Tier-Based Optimization for Synthesized Network Intrusion Detection System

Enhanced analysis of tabular data through Multi-representation DeepInsight

An effective attention and residual network for malware detection

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