A novel approach for APT attack detection based on combined deep learning model

2021 ◽  
Author(s):  
Cho Do Xuan ◽  
Mai Hoang Dao
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Attack Detection ◽  
Novel Approach ◽  
Deep Learning Model

scholarly journals A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

2018 ◽  
Vol 19 (9) ◽  
pp. 2817 ◽  
Author(s):  
Haixia Long ◽  
Bo Liao ◽  
Xingyu Xu ◽  
Jialiang Yang
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
New Drugs ◽  
Post Translational Modifications ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Memory Network ◽  
Scoring Matrix ◽  
Deep Learning Model

Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answered is: which residue can be hydroxylated, and which one cannot. The answer will not only help understand the mechanism of hydroxylation but can also benefit the development of new drugs. In this paper, we proposed a novel approach for predicting hydroxylation using a hybrid deep learning model integrating the convolutional neural network (CNN) and long short-term memory network (LSTM). We employed a pseudo amino acid composition (PseAAC) method to construct valid benchmark datasets based on a sliding window strategy and used the position-specific scoring matrix (PSSM) to represent samples as inputs to the deep learning model. In addition, we compared our method with popular predictors including CNN, iHyd-PseAAC, and iHyd-PseCp. The results for 5-fold cross-validations all demonstrated that our method significantly outperforms the other methods in prediction accuracy.

scholarly journals Which Attributes Matter the Most for Loan Origination? A Neural Attention Approach

2020 ◽  
Author(s):  
Antonios Alexos ◽  
Sotirios Chatzis
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Individual Case ◽  
Learning Model ◽  
Attention Mechanism ◽  
Novel Approach ◽  
Specific Individual ◽  
Real World Datasets ◽  
Deep Learning Model

In this paper we address the understanding of the problem, of why a deep learning model decides that an individual is eligible for a loan or not. Here we propose a novel approach for inferring, which attributes matter the most, for making a decision in each specific individual case. Specifically we leverage concepts from neural attention to devise a novel feature wise attention mechanism. As we show, using real world datasets, our approach offers unique insights into the importance of various features, by producing a decision explanation for each specific loan case. At the same time, we observe that our novel mechanism, generates decisions which are much closer to the decisions generated by human experts, compared to the existent competitors.

AOPL: Attention Enhanced Oversampling and Parallel Deep Learning Model for Attack Detection in Imbalanced Network Traffic

2021 ◽  
pp. 84-95
Author(s):  
Leiqi Wang ◽  
Weiqing Huang ◽  
Qiujian Lv ◽  
Yan Wang ◽  
HaiYan Chen
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Learning Model ◽  
Attack Detection ◽  
Deep Learning Model

Internet of Things attack detection using hybrid Deep Learning Model

2021 ◽  
Author(s):  
Amiya Kumar Sahu ◽  
Suraj Sharma ◽  
M. Tanveer ◽  
Rohit Raja
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Learning Model ◽  
Attack Detection ◽  
Deep Learning Model

scholarly journals Hybrid Deep-Learning Model to Detect Botnet Attacks over Internet of Things Environments

2021 ◽  
Author(s):  
Mohammed Y. Alzahrani ◽  
Alwi M Bamhdi
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Short Term Memory ◽  
Weighted Average ◽  
Learning Model ◽  
Attack Detection ◽  
Data Sets ◽  
Intelligence Gathering ◽  
Deep Learning Model

Abstract In recent years, the use of the internet of things (IoT) has increased dramatically, and cybersecurity concerns have grown in tandem. Cybersecurity has become a major challenge for institutions and companies of all sizes, with the spread of threats growing in number and developing at a rapid pace. Artificial intelligence (AI) in cybersecurity can to a large extent help face the challenge, since it provides a powerful framework and coordinates that allow organisations to stay one step ahead of sophisticated cyber threats. AI provides real-time feedback, helping rollover daily alerts to be investigated and analysed, effective decisions to be made and enabling quick responses. AI-based capabilities make attack detection, security and mitigation more accurate for intelligence gathering and analysis, and they enable proactive protective countermeasures to be taken to overwhelm attacks. In this study, we propose a robust system specifically to help detect botnet attacks of IoT devices. This was done by innovatively combining the model of a convolutional neural network with a long short-term memory algorithm mechanism to detect two common and serious IoT attacks (BASHLITE and Mirai) on four types of security camera. The data sets, which contained normal malicious network packets, were collected from real-time lab-connected camera devices in IoT environments. The results of the experiment showed that the proposed system achieved optimal performance, according to evaluation metrics. The proposed system gave the following weighted average results for detecting the botnet on the Provision PT-737E camera: camera precision: 88%, recall: 87% and F1 score: 83%. The results of system for classifying botnet attacks and normal packets on the Provision PT-838 camera were 89% for recall, 85% for F1 score and 94%, precision. The intelligent security system using the advanced deep learning model was successful for detecting botnet attacks that infected camera devices connected to IoT applications.

An Enhanced Deep Learning Model to Network Attack Detection, by using Parameter Tuning, Hidden Markov Model and Neural Network

2021 ◽  
Vol 15 (01) ◽  
pp. 35-41
Author(s):  
Choukri Djellali ◽  
Mehdi adda
Keyword(s):  
Deep Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Experimental Studies ◽  
Learning Model ◽  
Attack Detection ◽  
Success Factor ◽  
Network Attack ◽  
Deep Learning Model

In recent years, Deep Learning has become a critical success factor for Machine Learning. In the present study, we introduced a Deep Learning model to network attack detection, by using Hidden Markov Model and Artificial Neural Networks. We used a model aggregation technique to find a single consolidated Deep Learning model for better data fitting. The model selection technique is applied to optimize the bias-variance trade-off of the expected prediction. We demonstrate its ability to reduce the convergence, reach the optimal solution and obtain more cluttered decision boundaries. Experimental studies conducted on attack detection indicate that our proposed model outperformed existing Deep Learning models and gives an enhanced generalization.

DADEM

2021 ◽  
Vol 12 (1) ◽  
pp. 114-139
Author(s):  
Hassan I. Ahmed ◽  
Abdurrahman A. Nasr ◽  
Salah M. Abdel-Mageid ◽  
Heba K. Aslan
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Distributed Processing ◽  
Big Data Analytics ◽  
Learning Model ◽  
Attack Detection ◽  
Smart Devices ◽  
Detection Model ◽  
Deep Learning Model

Nowadays, Internet of Things (IoT) is considered as part our lives and it includes different aspects - from wearable devices to smart devices used in military applications. IoT connects a variety of devices and as such, the generated data is considered as ‘Big Data'. There has however been an increase in attacks in this era of IoT since IoT carries crucial information regarding banking, environmental, geographical, medical, and other aspects of the daily lives of humans. In this paper, a Distributed Attack Detection Model (DADEM) that combines two techniques - Deep Learning and Big Data analytics - is proposed. Sequential Deep Learning model is chosen as a classification engine for the distributed processing model after testing its classification accuracy against other classification algorithms like logistic regression, KNN, ID3 decision tree, CART, and SVM. Results showed that Sequential Deep Learning model outperforms the aforementioned ones. The classification accuracy of DADEM approaches 99.64% and 99.98% for the UNSW-NB15 and BoT-IoT datasets, respectively. Moreover, a plan is proposed for optimizing the proposed model to reduce the overhead of the overall system operation in a constrained environment like IoT.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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