Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning

Asam, Muhammad; Hussain, Shaik Javeed; Mohatram, Mohammed; Khan, Saddam Hussain; Jamal, Tauseef; Zafar, Amad; Khan, Asifullah; Ali, Muhammad Umair; Zahoora, Umme

doi:10.3390/app112110464

Cited by 17 publications

(12 citation statements)

References 30 publications

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“…DL architectures have shown excellent performance in the medical and commercial fields [ 21 , 22 , 23 , 24 , 25 ]. Therefore, DL is primarily employed in the detection of COVID-19 infection and drug repurposing in diverse ways [ 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Khan¹,

Sohail²,

Khan

2022

Diagnostics

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COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The proposed STM-RENet is a block-based CNN that employs the idea of split–transform–merge in a new way. In this regard, we have proposed a new convolutional block STM that implements the region and edge-based operations separately, as well as jointly. The systematic use of region and edge implementations in combination with convolutional operations helps in exploring region homogeneity, intensity inhomogeneity, and boundary-defining features. The learning capacity of STM-RENet is further enhanced by developing a new CB-STM-RENet that exploits channel boosting and learns textural variations to effectively screen the X-ray images of COVID-19 infection. The idea of channel boosting is exploited by generating auxiliary channels from the two additional CNNs using Transfer Learning, which are then concatenated to the original channels of the proposed STM-RENet. A significant performance improvement is shown by the proposed CB-STM-RENet in comparison to the standard CNNs on three datasets, especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), accuracy (96.53%), and reasonable F-score (95%) of the proposed technique suggest that it can be adapted to detect COVID-19 infected patients.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Khan¹,

Sohail²,

Khan

2022

Diagnostics

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“…On the MalImg dataset, they were 97.78 percent accurate. Muhammad Asam et al [26] selected CNNs, ResNet-18 & DenseNet201 for the DFSMC framework. The hybrid learning capacity of these two customized CNNs has been utilized in the Deep Boosted Feature Space-Based Malware Classification framework (DBFS-MC).…”

Section: Literature Surveymentioning

confidence: 99%

“…This section presents a framework for transfer learning for multiclass classification of malware. The proposed work provides a hybrid deep neural network [26]. This section is divided into 2 subsections namely malware visualization and architecture of the model used for this proposed work.…”

Section: Proposed Workmentioning

confidence: 99%

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IVMCT: Image Visualization based Multiclass Malware Classification using Transfer Learning

Goyal¹

2022

MSEA

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Computer systems have made it possible to transfer human life from the real world to virtual reality. This process has been accelerated by the Covid-19 virus. Cybercriminals have also switched from a real-life to a virtual one. Online, committing a crime is far easier than in real life. Cybercriminals often use malicious software (malware), to launch cyber-attacks. Apart from this polymorphic and metamorphic malware are used that use obfuscation techniques to create new malware variants. To effectively battle new malware types, you'll need to employ creative approaches that depart from the conventional. Traditionally signature-based techniques are used with machine learning algorithms to detect malware that is unable to catch its variants. Deep learning (DL), which differs from typical machine learning methods, might be a potential approach to the challenge of identifying all varieties of malware. In the present study, an IVMCT framework is introduced which classifies malware using transfer learning. For this purpose, the MalImg dataset is used which is based on grayscale images converted from binaries of malware. The comparison of IVMCT is done with existing techniques which shows that our technique is better than existing techniques.

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“…Recently, new machine learning [7], [8] and deep learning techniques [9], [10]have been producing excellent results in various domains [11]- [16]. It gives significant results in cyber security [17]- [20] as well. Reinforcement learning (RL)-based techniques, in particular, suffer from a significant rise in sample complexity, making them unsuitable for usage by UAVs in safety-critical settings.…”

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confidence: 99%

Drone Navigation Using Region and Edge Exploitation-Based Deep CNN

et al. 2022

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Drones are unmanned aerial vehicles (UAV) utilized for a broad range of functions, including delivery, aerial surveillance, traffic monitoring, architecture monitoring, and even War-field. Drones confront significant obstacles while navigating independently in complex and highly dynamic environments. Moreover, the targeted objects within a dynamic environment have irregular morphology, occlusion, and minor contrast variation with the background. In this regard, a novel deep Convolutional Neural Network(CNN) based data-driven strategy is proposed for drone navigation in the complex and dynamic environment. The proposed Drone Split-Transform-and-Merge Region-and-Edge (Drone-STM-RENet) CNN is comprised of convolutional blocks where each block methodically implements region and edge operations to preserve a diverse set of targeted properties at multi-levels, especially in the congested environment. In each block, the systematic implementation of the average and max-pooling operations can deal with the region homogeneity and edge properties. Additionally, these convolutional blocks are merged at a multi-level to learn texture variation that efficiently discriminates the target from the background and helps obstacle avoidance. Finally, the Drone-STM-RENet generates steering angle and collision probability for each input image to control the drone moving while avoiding hindrances and allowing the UAV to spot risky situations and respond quickly, respectively. The proposed Drone-STM-RENet has been validated on two urban cars and bicycles datasets: udacity and collision-sequence, and achieved considerable performance in terms of explained variance (0.99), recall (95.47%), accuracy (96.26%), and F-score (91.95%). The promising performance of Drone-STM-RENet on urban road datasets suggests that the proposed model is generalizable and can be deployed for real-time autonomous drones navigation and real-world flights.

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Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning

Cited by 17 publications

References 30 publications

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

IVMCT: Image Visualization based Multiclass Malware Classification using Transfer Learning

Drone Navigation Using Region and Edge Exploitation-Based Deep CNN

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