Big data analysis and distributed deep learning for next-generation intrusion detection system optimization

Abstract With the growing use of information technology in all life domains, hacking has become more negatively effective than ever before. Also with developing technologies, attacks numbers are growing exponentially every few months and become more sophisticated so that traditional IDS becomes inefficient detecting them. This paper proposes a solution to detect not only new threats with higher detection rate and lower false positive than already used IDS, but also it could detect collective and contextual security attacks. We achieve those results by using Networking Chatbot, a deep recurrent neural network: Long Short Term Memory (LSTM) on top of Apache Spark Framework that has an input of flow traffic and traffic aggregation and the output is a language of two words, normal or abnormal. We propose merging the concepts of language processing, contextual analysis, distributed deep learning, big data, anomaly detection of flow analysis. We propose a model that describes the network abstract normal behavior from a sequence of millions of packets within their context and analyzes them in near real-time to detect point, collective and contextual anomalies. Experiments are done on MAWI dataset, and it shows better detection rate not only than signature IDS, but also better than traditional anomaly IDS. The experiment shows lower false positive, higher detection rate and better point anomalies detection. As for prove of contextual and collective anomalies detection, we discuss our claim and the reason behind our hypothesis. But the experiment is done on random small subsets of the dataset because of hardware limitations, so we share experiment and our future vision thoughts as we wish that full prove will be done in future by other interested researchers who have better hardware infrastructure than ours.

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Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network

Scientific Reports ◽

10.1038/s41598-020-80441-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rakesh David ◽

Rhys-Joshua D. Menezes ◽

Jan De Klerk ◽

Ian R. Castleden ◽

Cornelia M. Hooper ◽

...

Keyword(s):

Neural Network ◽

Language Processing ◽

Data Dissemination ◽

Short Term Memory ◽

Biological Data ◽

Experimental Methodology ◽

Subcellular Localisation ◽

Crop Species ◽

Deep Recurrent Neural Network ◽

Functional Features

AbstractThe increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic interaction between two or more biological entities in a published study. Here, we employed two deep neural network natural language processing (NLP) methods, namely: the continuous bag of words (CBOW), and the bi-directional long short-term memory (bi-LSTM). These methods were employed to predict relations between entities that describe protein subcellular localisation in plants. We applied our system to 1700 published Arabidopsis protein subcellular studies from the SUBA manually curated dataset. The system combines pre-processing of full-text articles in a machine-readable format with relevant sentence extraction for downstream NLP analysis. Using the SUBA corpus, the neural network classifier predicted interactions between protein name, subcellular localisation and experimental methodology with an average precision, recall rate, accuracy and F1 scores of 95.1%, 82.8%, 89.3% and 88.4% respectively (n = 30). Comparable scoring metrics were obtained using the CropPAL database as an independent testing dataset that stores protein subcellular localisation in crop species, demonstrating wide applicability of prediction model. We provide a framework for extracting protein functional features from unstructured text in the literature with high accuracy, improving data dissemination and unlocking the potential of big data text analytics for generating new hypotheses.

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Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation

Sensors ◽

10.3390/s21165446 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5446

Author(s):

Hyojung Ahn ◽

Inchoon Yeo

Keyword(s):

Deep Learning ◽

Anomaly Detection ◽

Short Term Memory ◽

Detection System ◽

Noisy Environments ◽

Acoustic Data ◽

Detection Techniques ◽

Machine Operation ◽

Detection Technology ◽

Environment Noise

As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.

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Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/4321131 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Kazi Nabiul Alam ◽

Md Shakib Khan ◽

Abdur Rab Dhruba ◽

Mohammad Monirujjaman Khan ◽

Jehad F. Al-Amri ◽

...

Keyword(s):

Deep Learning ◽

Language Processing ◽

Performance Metrics ◽

Short Term Memory ◽

Confusion Matrix ◽

Short Term ◽

Learning Techniques ◽

The World ◽

Long Short Term Memory ◽

Severe Anxiety

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

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FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS

10.21203/rs.3.rs-555961/v1 ◽

2021 ◽

Author(s):

Khloud Al Jallad

Keyword(s):

Big Data ◽

Deep Learning ◽

Language Processing ◽

Intrusion Detection Systems ◽

Dynamic Feature ◽

Statistical Features ◽

Text Data ◽

New Approach ◽

New Era ◽

Detection Systems

Abstract New Attacks are increasingly used by attackers every day but many of them are not detected by Intrusion Detection Systems as most IDS ignore raw packet information and only care about some basic statistical information extracted from PCAP files. Using networking programs to extract fixed statistical features from packets is good, but may not enough to detect nowadays challenges. We think that it is time to utilize big data and deep learning for automatic dynamic feature extraction from packets. It is time to get inspired by deep learning pre-trained models in computer vision and natural language processing, so security deep learning solutions will have its pre-trained models on big datasets to be used in future researches. In this paper, we proposed a new approach for embedding packets based on character-level embeddings, inspired by FastText success on text data. We called this approach FastPacket. Results are measured on subsets of CIC-IDS-2017 dataset, but we expect promising results on big data pre-trained models. We suggest building pre-trained FastPacket on MAWI big dataset and make it available to community, similar to FastText. To be able to outperform currently used NIDS, to start a new era of packet-level NIDS that can better detect complex attacks

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Chinese Text Classification Model Based on Deep Learning

Future Internet ◽

10.3390/fi10110113 ◽

2018 ◽

Vol 10 (11) ◽

pp. 113 ◽

Cited By ~ 17

Author(s):

Yue Li ◽

Xutao Wang ◽

Pengjian Xu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Language Processing ◽

Chinese Text ◽

Text Classification ◽

Short Term Memory ◽

Classification Model ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts.

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DoS and DDoS Attack Detection Using Deep Learning and IDS

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/4a/10 ◽

2020 ◽

Vol 17 (4A) ◽

pp. 655-661

Author(s):

Mohammad Shurman ◽

Rami Khrais ◽

Abdulrahman Yateem

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Detection System ◽

Denial Of Service ◽

Attack Detection ◽

Target System ◽

Online Systems ◽

Ddos Attack ◽

Long Short Term Memory ◽

Ddos Attack Detection

In the recent years, Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack has spread greatly and attackers make online systems unavailable to legitimate users by sending huge number of packets to the target system. In this paper, we proposed two methodologies to detect Distributed Reflection Denial of Service (DrDoS) attacks in IoT. The first methodology uses hybrid Intrusion Detection System (IDS) to detect IoT-DoS attack. The second methodology uses deep learning models, based on Long Short-Term Memory (LSTM) trained with latest dataset for such kinds of DrDoS. Our experimental results demonstrate that using the proposed methodologies can detect bad behaviour making the IoT network safe of Dos and DDoS attacks

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COVID-19 ChatBot

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38757 ◽

2021 ◽

Vol 9 (11) ◽

pp. 44-49

Author(s):

Satish Tirumalapudi

Keyword(s):

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

And Control ◽

Prediction Problems

Abstract: Chat bots are software applications that help users to communicate with the machine and get the required result, this is where Natural Language Processing (NLP) comes into the picture. Natural language processing is based on deep learning that enables computers to acquire meaning from inputs given by the users. Natural language processing techniques can make possible the use of natural language to express ideas, thus drastically increasing accessibility. NLP engines rely on the elements of intent, utterance, entity, context, and session. Here in this project, we will be using Deep learning techniques which will be trained on the dataset which contains categories, patterns, and responses. Long Short-Term Memory (LSTM) is a Recurrent Neural Network that is capable of learning order dependence in sequence prediction problems. One of the most popular RNN approaches is LSTM to identify and control a dynamic system. We use an RNN to classify the category user’s message belongs to and then will give a response from the list of responses. Keywords: NLP – Natural Language Processing, LSTM – Long Short Term Memory, RNN – Recurrent Neural Networks.

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Two-Phase Deep Learning-Based EDoS Detection System

Applied Sciences ◽

10.3390/app112110249 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10249

Author(s):

Chien-Nguyen Nhu ◽

Minho Park

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Network Traffic ◽

Input Data ◽

Short Term Memory ◽

Detection System ◽

Scale Up ◽

Sequence Length ◽

Two Phase ◽

Detection Scheme

Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack called an economic denial of sustainability (EDoS) attack exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, a few solutions have been proposed, including hard-threshold and machine learning-based solutions. Among them, long short-term memory (LSTM)-based solutions achieve much higher accuracy and false-alarm rates than hard-threshold and other machine learning-based solutions. However, LSTM requires a long sequence length of the input data, leading to a degraded performance owing to increases in the calculations, the detection time, and consuming a large number of computing resources of the defense system. We, therefore, propose a two-phase deep learning-based EDoS detection scheme that uses an LSTM model to detect each abnormal flow in network traffic; however, the LSTM model requires only a short sequence length of five of the input data. Thus, the proposed scheme can take advantage of the efficiency of the LSTM algorithm in detecting each abnormal flow in network traffic, while reducing the required sequence length of the input data. A comprehensive performance evaluation shows that our proposed scheme outperforms the existing solutions in terms of accuracy and resource consumption.

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A comparative analysis on question classification task based on deep learning approaches

PeerJ Computer Science ◽

10.7717/peerj-cs.570 ◽

2021 ◽

Vol 7 ◽

pp. e570

Author(s):

Muhammad Zulqarnain ◽

Ahmed Khalaf Zager Alsaedi ◽

Rozaida Ghazali ◽

Muhammad Ghulam Ghouse ◽

Wareesa Sharif ◽

...

Keyword(s):

Deep Learning ◽

Language Processing ◽

Web Mining ◽

Question Answering ◽

Short Term Memory ◽

Learning Approaches ◽

Question Classification ◽

Considerable Impact ◽

Turkish Language ◽

Learning Architectures

Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word embedding with various vector sizes on a large corpus composed of user questions. By comparing analysis, we conducted an experiment on deep learning architectures based on test and 10-cross fold validation accuracy. Experiment results were obtained to illustrate the effectiveness of various Word2vec techniques that have a considerable impact on the accuracy rate using different deep learning approaches. We attained an accuracy of 93.7% by using these techniques on the question dataset.

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Exploring the Impact of COVID-19 on Social Life by Deep Learning

Information ◽

10.3390/info12110459 ◽

2021 ◽

Vol 12 (11) ◽

pp. 459

Author(s):

Jose Antonio Jijon-Vorbeck ◽

Isabel Segura-Bedmar

Keyword(s):

Deep Learning ◽

Language Processing ◽

Social Life ◽

Data Science ◽

Short Term Memory ◽

Negative Impact ◽

Machine Learning Algorithms ◽

World Health ◽

Health Organization ◽

The Impact

Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currently fighting an “infodemic” in the same way as they were fighting the pandemic. An “infodemic” relates to the spread of information that is not controlled or filtered, and can have a negative impact on society. If not managed properly, an aggressive or negative tweet can be very harmful and misleading among its recipients. Therefore, authorities at WHO have called for action and asked the academic and scientific community to develop tools for managing the infodemic by the use of digital technologies and data science. The goal of this study is to develop and apply natural language processing models using deep learning to classify a collection of tweets that refer to the COVID-19 pandemic. Several simpler and widely used models are applied first and serve as a benchmark for deep learning methods, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the deep learning models outperform the traditional machine learning algorithms. The best approach is the BERT-based model.

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