Natural Language Processing based Anomalous System Call Sequences Detection with Virtual Memory Introspection

Peddoju, Suresh K.; Upadhyay, Himanshu; Soni, Jayesh; Prabakar, Nagarajan

doi:10.14569/ijacsa.2020.0110559

Cited by 15 publications

(5 citation statements)

References 11 publications

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“…Another collection of features is produced by training a Variational AutoEncoder [17]. VAEs constitute a generative model in deep learning, and they attempt to define the internal structure of high-dimensional data with dimensionality reduction methods.…”

Section: Figure 2 One Class Classifier Feature Detectionmentioning

confidence: 99%

PhisNet: Deep learning-based Hybrid and Ensemble Multi-level Approach for the detection of phishing websites

Soni,

Prabakar,

Upadhyay

2024

Preprint

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Phishing is a cyber-attack that intends to trick individuals into providing an attacker with sensitive information, such as login authorizations or business information. One tactic that attackers use is creating fake websites that mimic legitimate ones to fool victims into entering their information. A learning-based model can be used to analyze patterns in website content, structure, behavior, email text, sender, and links. These models can help identify phishing attempts and protect individuals and organizations from falling victim to these scams. In this research, we study and experiment with the deep learning-based algorithm in classifying phishing webpages from legitimate webpages that can be generalized across multiple domains. We used the open-source benchmark dataset, "Phishing Dataset for Machine Learning," available on Kaggle. We propose a hybrid and a multi-level ensemble approach for phishing website detection. Several one-class classifiers are trained on the first level, and the Variational Autoencoder (VAE) is used to reduce the size of feature vectors. Each one class classifiers have its own strength and limitations, and thus an adaptive weightage approach is applied. At the second level, a multilayer neural network is trained for classification. Further, the training time is reduced due to feature reduction in the latent space. Our experimental results classify phishing websites from legitimate websites with 98% accuracy.

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Section: Figure 2 One Class Classifier Feature Detectionmentioning

confidence: 99%

PhisNet: Deep learning-based Hybrid and Ensemble Multi-level Approach for the detection of phishing websites

Soni,

Prabakar,

Upadhyay

2024

Preprint

Self Cite

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“…• Malware is the biggest threat to the safety and security of a computer device. Peddoju et al [197] implemented a method for tracing the system calls made by a device and detecting the presence of a malicious application using NLP based on Bags of System Calls (BoSC) and a cosine similarity (Co-Sim) detecting algorithm. • To detect phishing emails Alhogail et al [198] proposed a model using NLP and Graph Convolutional Network (GCN).…”

Section: Smart Securitymentioning

confidence: 99%

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Tyagi

Bhushan

2023

Wireless Pers Commun

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Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

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“…To extract patent data, a patent search using technology keywords was performed using the platform's patent database search engine. Effective data was extracted by filtering, as the raw data contains unnecessary noisy data in the analysis [105]. If the technology was not related to the railway main transformer, it could not be included in the study.…”

Section: Patent Data Collectmentioning

confidence: 99%

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

et al. 2022

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The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data.

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Natural Language Processing based Anomalous System Call Sequences Detection with Virtual Memory Introspection

Cited by 15 publications

References 11 publications

PhisNet: Deep learning-based Hybrid and Ensemble Multi-level Approach for the detection of phishing websites

PhisNet: Deep learning-based Hybrid and Ensemble Multi-level Approach for the detection of phishing websites

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

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