Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

Thakur, Deepak; Singh, Jaiteg; Shabaz, Mohammad; Gera, Tanya

doi:10.1155/2021/4551067

Cited by 11 publications

(3 citation statements)

References 133 publications

(156 reference statements)

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“…Training and testing datasets are used to calculate the distance metric. To begin the algorithm, the first step is to determine the value of k and determine the distance between the instances using the k value [22].…”

Section: Methodsmentioning

confidence: 99%

Retracted: Development of intrusion detection system using machine learning for the analytics of Internet of Things enabled enterprises

Raghavendra

Dhabliya²,

Mondal³

et al. 2022

IET Communications

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The above article from IET Communications, published online on 28 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Methodsmentioning

confidence: 99%

Retracted: Development of intrusion detection system using machine learning for the analytics of Internet of Things enabled enterprises

Raghavendra

Dhabliya²,

Mondal³

et al. 2022

IET Communications

Self Cite

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“…In the previous section we have certainly discussed about enough background on android malware and the detection methods that were traditionally used to detect/evade attack attempts made on the android OS. In 2021, Deepak Thakur along with Jaiteg Singh et al proposed the SEAR framework [5], which was able to perform a quantitative investigation over enormous literature. In the same year 2021, [6] Jaiteg Singh along with Tanya Gera et al proposed a similar study.…”

Section: Literature Reviewmentioning

confidence: 99%

Retracted: Visualisation‐based binary classification of android malware using vgg16

et al. 2023

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Android has gained many patrons these past years due to its great UI as well as affordable price. This grabbed the unwanted attention of various cyber criminals (also known as malware authors), and they saw it as their lucrative target. These lingering malwares around the internet made it hard for the users to distinguish effectively between these malwares and the benign applications. To counteract this loathsome conduct, many approaches or methods were introduced. In this manuscript a visualization-based binary classification architecture for android malware with the help of VGG16 (VBBCAM using VGG16) was introduced. The DREBIN dataset as the dataset in this experiment was used. The architecture converted the application binaries in 15 distinct combinations to create 15 distinct RBG images and were used as separate models. These models were then compared in order to sort the best one out of all. After experimentation, the observations were recorded in a table, and the results were compared with respect to each image combination. An accuracy of 90.57% and an F1 score value of 86.74% (from AC + AM image combination) were achieved. Hence, a conclusion was reached that the AC + AM model was the best with a total execution time of 1498.77 s.

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“…The deployment of an actual example shows that an OS-ELM-based detection model can improve and enhance detection performance and efficiency in a changing environment. The online sequence extreme learning machine (OS-ELM) provides a specific capacity for learning, rapid training, and online learning [ 19 ]. However, the approaches outlined above do not take into account sensor datasets with latency correlations and need specialized empirical expertise.…”

Section: Related Workmentioning

confidence: 99%

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

Soni

Khan

Baseer

et al. 2022

Computational Intelligence and Neuroscience

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Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model’s online detection and update. The method’s efficacy is demonstrated by experiments based on real data from power plant sensors.

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Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

Cited by 11 publications

References 133 publications

Retracted: Development of intrusion detection system using machine learning for the analytics of Internet of Things enabled enterprises

Retracted: Development of intrusion detection system using machine learning for the analytics of Internet of Things enabled enterprises

Retracted: Visualisation‐based binary classification of android malware using vgg16

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

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