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Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.
Muhammad Khan. HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System. Processes 2021, 9, 834 .
AMA StyleMuhammad Khan. HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System. Processes. 2021; 9 (5):834.
Chicago/Turabian StyleMuhammad Khan. 2021. "HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System." Processes 9, no. 5: 834.
In the field of image processing, tangling noise and artefacts elimination of objects are two essential tasks. Tangling noise and lack of intensity in certain applications also occur at the same time. In this paper, a new variational model is proposed based on total variation and l0 the norm for simultaneously removing the tangling noise, estimating the location of missing pixels, and filling in them. To be specific, the total variation is used to regularize the estimated image and use the l0 norm to make the missing pixel to be sparse. Moreover, the data fidelity term is given by a new forward description about the degraded process and the gamma noise assumption. Finally, an algorithm based on the alternating direction multiplier method is exploited to solve the model. By conducting simulated and real experiments, the damaged images can be effectively restored by the proposed method. In qualitative and quantitative terms, this approach works better.
Muhammad Ashfaq Khan; Fayaz Ali Dharejo; Farah Deeba; Shahzad Ashraf; Juntae Kim; Hoon Kim. Toward developing tangling noise removal and blind inpainting mechanism based on total variation in image processing. Electronics Letters 2021, 57, 436 -438.
AMA StyleMuhammad Ashfaq Khan, Fayaz Ali Dharejo, Farah Deeba, Shahzad Ashraf, Juntae Kim, Hoon Kim. Toward developing tangling noise removal and blind inpainting mechanism based on total variation in image processing. Electronics Letters. 2021; 57 (11):436-438.
Chicago/Turabian StyleMuhammad Ashfaq Khan; Fayaz Ali Dharejo; Farah Deeba; Shahzad Ashraf; Juntae Kim; Hoon Kim. 2021. "Toward developing tangling noise removal and blind inpainting mechanism based on total variation in image processing." Electronics Letters 57, no. 11: 436-438.
Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super‐resolution image, etc. Due to the orbital location, revision time, quick view angle limitations, and weather impact, the satellite images are challenging to manage. There are many types of resolution, such as spatial, spectral, and temporal. Still, in our case, we concentrated on spatial image resolution to super resolve the images from low‐resolution images. For remote sensing image super‐resolution fast wavelet‐based super‐resolution (FWSR), we propose a novel, fast wavelet‐based plexus framework that performs super‐resolution convolutional neural network (SRCNN)‐like extraction of features based on three hidden layers. First, wavelet sub‐band images are combined into a pre‐defined full‐scale data training factor, including approximation and interchangeable stand‐alone units (frequency sub‐bands). Second, to speed up image recovery, mapping the sub‐band image of the wavelet is then measured using its approximate image. Third, the added sub‐pixel layer at the end of the network model is intended to reproduce image quality using a plexus framework. The approximation sub‐band images obtained after discrete wavelet transform wavelet decomposition are used as input rather than the original image because of their high‐frequency data and preserved characteristics. Five current super‐resolution neural network approaches are compared with the proposed technique and tested on three pubic satellite image datasets and two benchmark datasets. The experimental findings are well compared qualitatively and quantitatively.
Farah Deeba; Yuanchun Zhou; Fayaz Ali Dharejo; Muhammad Ashfaq Khan; Bhagwan Das; Xuezhi Wang; Yi Du. A plexus‐convolutional neural network framework for fast remote sensing image super‐resolution in wavelet domain. IET Image Processing 2021, 15, 1679 -1687.
AMA StyleFarah Deeba, Yuanchun Zhou, Fayaz Ali Dharejo, Muhammad Ashfaq Khan, Bhagwan Das, Xuezhi Wang, Yi Du. A plexus‐convolutional neural network framework for fast remote sensing image super‐resolution in wavelet domain. IET Image Processing. 2021; 15 (8):1679-1687.
Chicago/Turabian StyleFarah Deeba; Yuanchun Zhou; Fayaz Ali Dharejo; Muhammad Ashfaq Khan; Bhagwan Das; Xuezhi Wang; Yi Du. 2021. "A plexus‐convolutional neural network framework for fast remote sensing image super‐resolution in wavelet domain." IET Image Processing 15, no. 8: 1679-1687.
Image dehazing is a fast-growing research area in image processing and computer vision. Due to the extreme fog, haze, and air dispersion within an environment, the hazy image raises several challenges in retrieving original image information type. However, past techniques endure massive computation complexity and even the distortion of original images such as halos and over-saturation. In this research work, a new wavelet Hybrid (Local-Global Combined) Network is proposed for single image dehazing using a convolution neural network (CNN) in the wavelet domain (WH-Net). It is observed that low-level features such as edges are more important than high-level features such as texture. So we have used 2-DWT to decompose the single image model into the frequency subbands, which performs more quickly. It is demonstrated that the estimation of wavelet sub-bands reformulates the trainable end-to-end learning with a special architecture where DWT and IDWT are the feature extraction layers instead of Conv and Deconv, distinguishing it from classical CNN networks. The WHNet method is designed to achieve multi-level representations of hazy images to provide local and global information. The proposed network prominent features are designed with fewer convolution layers without decreasing performance relative to the commonly observed deeper learning models. Compared to several state-of-the-art algorithms, our proposed WHNet neural network surpasses in visual and quantitative performances on three public datasets.
Fayaz Ali Dharejo; Yuanchun Zhou; Farah Deeba; Munsif Ali Jatoi; Muhammad Ashfaq Khan; Ghulam Ali Mallah; Abdul Ghaffar; Muhammad Chhattal; Yi Du; Xuezhi Wang. A deep hybrid neural network for single image dehazing via wavelet transform. Optik 2021, 231, 166462 .
AMA StyleFayaz Ali Dharejo, Yuanchun Zhou, Farah Deeba, Munsif Ali Jatoi, Muhammad Ashfaq Khan, Ghulam Ali Mallah, Abdul Ghaffar, Muhammad Chhattal, Yi Du, Xuezhi Wang. A deep hybrid neural network for single image dehazing via wavelet transform. Optik. 2021; 231 ():166462.
Chicago/Turabian StyleFayaz Ali Dharejo; Yuanchun Zhou; Farah Deeba; Munsif Ali Jatoi; Muhammad Ashfaq Khan; Ghulam Ali Mallah; Abdul Ghaffar; Muhammad Chhattal; Yi Du; Xuezhi Wang. 2021. "A deep hybrid neural network for single image dehazing via wavelet transform." Optik 231, no. : 166462.
Recently, due to the rapid development and remarkable result of deep learning (DL) and machine learning (ML) approaches in various domains for several long-standing artificial intelligence (AI) tasks, there has an extreme interest in applying toward network security too. Nowadays, in the information communication technology (ICT) era, the intrusion detection (ID) system has the great potential to be the frontier of security against cyberattacks and plays a vital role in achieving network infrastructure and resources. Conventional ID systems are not strong enough to detect advanced malicious threats. Heterogeneity is one of the important features of big data. Thus, designing an efficient ID system using a heterogeneous dataset is a massive research problem. There are several ID datasets openly existing for more research by the cybersecurity researcher community. However, no existing research has shown a detailed performance evaluation of several ML methods on various publicly available ID datasets. Due to the dynamic nature of malicious attacks with continuously changing attack detection methods, ID datasets are available publicly and are updated systematically. In this research, spark MLlib (machine learning library)-based robust classical ML classifiers for anomaly detection and state of the art DL, such as the convolutional-auto encoder (Conv-AE) for misuse attack, is used to develop an efficient and intelligent ID system to detect and classify unpredictable malicious attacks. To measure the effectiveness of our proposed ID system, we have used several important performance metrics, such as FAR, DR, and accuracy, while experiments are conducted on the publicly existing dataset, specifically the contemporary heterogeneous CSE-CIC-IDS2018 dataset.
Muhammad Ashfaq Khan; Juntae Kim. Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset. Electronics 2020, 9, 1771 .
AMA StyleMuhammad Ashfaq Khan, Juntae Kim. Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset. Electronics. 2020; 9 (11):1771.
Chicago/Turabian StyleMuhammad Ashfaq Khan; Juntae Kim. 2020. "Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset." Electronics 9, no. 11: 1771.
With the rapid advancements of ubiquitous information and communication technologies, a large number of trustworthy online systems and services have been deployed. However, cybersecurity threats are still mounting. An intrusion detection (ID) system can play a significant role in detecting such security threats. Thus, developing an intelligent and accurate ID system is a non-trivial research problem. Existing ID systems that are typically used in traditional network intrusion detection system often fail and cannot detect many known and new security threats, largely because those approaches are based on classical machine learning methods that provide less focus on accurate feature selection and classification. Consequently, many known signatures from the attack traffic remain unidentifiable and become latent. Furthermore, since a massive network infrastructure can produce large-scale data, these approaches often fail to handle them flexibly, hence are not scalable. To address these issues and improve the accuracy and scalability, we propose a scalable and hybrid IDS, which is based on Spark ML and the convolutional-LSTM (Conv-LSTM) network. This IDS is a two-stage ID system: the first stage employs the anomaly detection module, which is based on Spark ML. The second stage acts as a misuse detection module, which is based on the Conv-LSTM network, such that both global and local latent threat signatures can be addressed. Evaluations of several baseline models in the ISCX-UNB dataset show that our hybrid IDS can identify network misuses accurately in 97.29% of cases and outperforms state-of-the-art approaches during 10-fold cross-validation tests.
Muhammad Khan; Rezaul Karim; Yangwoo Kim. A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network. Symmetry 2019, 11, 583 .
AMA StyleMuhammad Khan, Rezaul Karim, Yangwoo Kim. A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network. Symmetry. 2019; 11 (4):583.
Chicago/Turabian StyleMuhammad Khan; Rezaul Karim; Yangwoo Kim. 2019. "A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network." Symmetry 11, no. 4: 583.
Every day we experience unprecedented data growth from numerous sources, which contribute to big data in terms of volume, velocity, and variability. These datasets again impose great challenges to analytics framework and computational resources, making the overall analysis difficult for extracting meaningful information in a timely manner. Thus, to harness these kinds of challenges, developing an efficient big data analytics framework is an important research topic. Consequently, to address these challenges by exploiting non-linear relationships from very large and high-dimensional datasets, machine learning (ML) and deep learning (DL) algorithms are being used in analytics frameworks. Apache Spark has been in use as the fastest big data processing arsenal, which helps to solve iterative ML tasks, using distributed ML library called Spark MLlib. Considering real-world research problems, DL architectures such as Long Short-Term Memory (LSTM) is an effective approach to overcoming practical issues such as reduced accuracy, long-term sequence dependency, and vanishing and exploding gradient in conventional deep architectures. In this paper, we propose an efficient analytics framework, which is technically a progressive machine learning technique merged with Spark-based linear models, Multilayer Perceptron (MLP) and LSTM, using a two-stage cascade structure in order to enhance the predictive accuracy. Our proposed architecture enables us to organize big data analytics in a scalable and efficient way. To show the effectiveness of our framework, we applied the cascading structure to two different real-life datasets to solve a multiclass and a binary classification problem, respectively. Experimental results show that our analytical framework outperforms state-of-the-art approaches with a high-level of classification accuracy.
Muhammad Ashfaq Khan; Rezaul Karim; Yangwoo Kim. A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network. Symmetry 2018, 10, 485 .
AMA StyleMuhammad Ashfaq Khan, Rezaul Karim, Yangwoo Kim. A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network. Symmetry. 2018; 10 (10):485.
Chicago/Turabian StyleMuhammad Ashfaq Khan; Rezaul Karim; Yangwoo Kim. 2018. "A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network." Symmetry 10, no. 10: 485.
Kamran Siddique; Zahid Akhtar; Muhammad Ashfaq Khan; Yong-Hwan Jung; Yangwoo Kim. Developing an Intrusion Detection Framework for High-Speed Big Data Networks: A Comprehensive Approach. KSII Transactions on Internet and Information Systems 2018, 12, 4021 -4037.
AMA StyleKamran Siddique, Zahid Akhtar, Muhammad Ashfaq Khan, Yong-Hwan Jung, Yangwoo Kim. Developing an Intrusion Detection Framework for High-Speed Big Data Networks: A Comprehensive Approach. KSII Transactions on Internet and Information Systems. 2018; 12 (8):4021-4037.
Chicago/Turabian StyleKamran Siddique; Zahid Akhtar; Muhammad Ashfaq Khan; Yong-Hwan Jung; Yangwoo Kim. 2018. "Developing an Intrusion Detection Framework for High-Speed Big Data Networks: A Comprehensive Approach." KSII Transactions on Internet and Information Systems 12, no. 8: 4021-4037.