Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream

• URL: http://arxiv.org/abs/2004.00503v1
• Date: Tue, 31 Mar 2020 00:29:42 GMT
• Title: Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream
• Authors: Simran K, Prathiksha Balakrishna, Vinayakumar R, Soman KP
• Abstract summary: This work proposes a deep learning based approach for tweet data analysis. To convert the tweets into numerical representations, various text representations are employed. For comparative analysis, the classical text representation method with classical machine learning algorithm is employed.
• Score: 3.7354197654171797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent days, the amount of Cyber Security text data shared via social media resources mainly Twitter has increased. An accurate analysis of this data can help to develop cyber threat situational awareness framework for a cyber threat. This work proposes a deep learning based approach for tweet data analysis. To convert the tweets into numerical representations, various text representations are employed. These features are feed into deep learning architecture for optimal feature extraction as well as classification. Various hyperparameter tuning approaches are used for identifying optimal text representation method as well as optimal network parameters and network structures for deep learning models. For comparative analysis, the classical text representation method with classical machine learning algorithm is employed. From the detailed analysis of experiments, we found that the deep learning architecture with advanced text representation methods performed better than the classical text representation and classical machine learning algorithms. The primary reason for this is that the advanced text representation methods have the capability to learn sequential properties which exist among the textual data and deep learning architectures learns the optimal features along with decreasing the feature size.

Related papers

• Advanced Multimodal Deep Learning Architecture for Image-Text Matching [33.8315200009152]
  Image-text matching is a key multimodal task that aims to model the semantic association between images and text as a matching relationship. We introduce an advanced multimodal deep learning architecture, which combines the high-level abstract representation ability of deep neural networks for visual information with the advantages of natural language processing models for text semantic understanding. Experiments show that compared with existing image-text matching models, the optimized new model has significantly improved performance on a series of benchmark data sets.
  arXiv  Detail & Related papers  (2024-06-13T08:32:24Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)
• Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data [48.7576911714538]
  We discuss how these techniques can be used to detect political content across different platforms. We compare the performance of three groups of detection techniques relying on dictionaries, supervised machine learning, or neural networks. Our results show the limited impact of preprocessing on model performance, with the best results for less noisy data being achieved by neural network- and machine-learning-based models.
  arXiv  Detail & Related papers  (2022-07-01T15:23:23Z)
• Context-based Deep Learning Architecture with Optimal Integration Layer for Image Parsing [0.0]
  The proposed three-layer context-based deep architecture is capable of integrating context explicitly with visual information. The experimental outcomes when evaluated on benchmark datasets are promising.
  arXiv  Detail & Related papers  (2022-04-13T07:35:39Z)
• DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting [91.56988987393483]
  We present a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP. Specifically, we convert the original image-text matching problem in CLIP to a pixel-text matching problem and use the pixel-text score maps to guide the learning of dense prediction models. Our method is model-agnostic, which can be applied to arbitrary dense prediction systems and various pre-trained visual backbones.
  arXiv  Detail & Related papers  (2021-12-02T18:59:32Z)
• SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing [15.324528842034177]
  We propose a symbolic testing framework that helps to answer the question of what kinds of expert-derived features could be learned by a neural network. Inspired by this testing framework, we introduce an efficient architecture named SHORING. We argue that SHORING is capable of learning certain standard symbolic expressions which the standard multi-head self-attention network fails to learn.
  arXiv  Detail & Related papers  (2021-07-03T02:33:32Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language [148.0843278195794]
  We propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning. Our approach uses a dictionary learning-based method of learning relations between videos and their paired text descriptions.
  arXiv  Detail & Related papers  (2020-11-18T20:21:19Z)
• A Data-driven Neural Network Architecture for Sentiment Analysis [0.0]
  We present the creation steps of two big datasets of song emotions. We also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text datasets.
  arXiv  Detail & Related papers  (2020-06-30T10:08:36Z)
• From text saliency to linguistic objects: learning linguistic interpretable markers with a multi-channels convolutional architecture [2.064612766965483]
  We propose a novel approach to inspect the hidden layers of a fitted CNN in order to extract interpretable linguistic objects from texts exploiting classification process. We empirically demonstrate the efficiency of our approach on corpora from two different languages: English and French.
  arXiv  Detail & Related papers  (2020-04-07T10:46:58Z)
• Improved Code Summarization via a Graph Neural Network [96.03715569092523]
  In general, source code summarization techniques use the source code as input and outputs a natural language description. We present an approach that uses a graph-based neural architecture that better matches the default structure of the AST to generate these summaries.
  arXiv  Detail & Related papers  (2020-04-06T17:36:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.