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

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

scholarly journals Text Data Augmentation for Deep Learning

2021 ◽  
Author(s):  
Connor Shorten ◽  
Taghi M. Khoshgoftaar ◽  
Borko Furht
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Data Augmentation ◽  
Early Stage ◽  
Practical Implementation ◽  
Text Data ◽  
Training Strategy ◽  
Local Decision ◽  
Decision Boundaries

Abstract Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We highlight studies that cover how augmentations can construct test sets for generalization. NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight the key differences and promising ideas that have yet to be tested in NLP. For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. Finally, we discuss interesting topics around Data Augmentation in NLP such as task-specific augmentations, the use of prior knowledge in self-supervised learning versus Data Augmentation, intersections with transfer and multi-task learning, and ideas for AI-GAs (AI-Generating Algorithms). We hope this paper inspires further research interest in Text Data Augmentation.

scholarly journals CNN-Based Network Intrusion Detection against Denial-of-Service Attacks

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 916 ◽  
Author(s):  
Jiyeon Kim ◽  
Jiwon Kim ◽  
Hyunjung Kim ◽  
Minsun Shim ◽  
Eunjung Choi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
National Defense ◽  
Dos Attacks ◽  
Detection Systems ◽  
Network Intrusion

As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures and pretend to be normal users. Deep learning is an alternative to solving these issues. Deep Learning (DL)-based intrusion detection does not require a lot of attack signatures or the list of normal behaviors to generate detection rules. DL defines intrusion features by itself through training empirical data. We develop a DL-based intrusion model especially focusing on denial of service (DoS) attacks. For the intrusion dataset, we use KDD CUP 1999 dataset (KDD), the most widely used dataset for the evaluation of intrusion detection systems (IDS). KDD consists of four types of attack categories, such as DoS, user to root (U2R), remote to local (R2L), and probing. Numerous KDD studies have been employing machine learning and classifying the dataset into the four categories or into two categories such as attack and benign. Rather than focusing on the broad categories, we focus on various attacks belonging to same category. Unlike other categories of KDD, the DoS category has enough samples for training each attack. In addition to KDD, we use CSE-CIC-IDS2018 which is the most up-to-date IDS dataset. CSE-CIC-IDS2018 consists of more advanced DoS attacks than that of KDD. In this work, we focus on the DoS category of both datasets and develop a DL model for DoS detection. We develop our model based on a Convolutional Neural Network (CNN) and evaluate its performance through comparison with an Recurrent Neural Network (RNN). Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments.

scholarly journals Discovering Attackers Past Behavior to Generate Online Hyper-Alerts

2017 ◽  
Vol 10 (1) ◽  
pp. 122-147 ◽  
Author(s):  
Cláudio Toshio Kawakani ◽  
Sylvio Barbon ◽  
Rodrigo Sanches Miani ◽  
Michel Cukier ◽  
Bruno Bogaz Zarpelão
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Huge Amount ◽  
New Approach ◽  
University Of Maryland ◽  
Detection Systems ◽  
Past Behavior ◽  
The University ◽  
Security Organizations ◽  
Attack Strategy

To support information security, organizations deploy Intrusion Detection Systems (IDS) that monitor information systems and networks, generating alerts for every suspicious behavior. However, the huge amount of alerts that an IDS triggers and their low-level representation make the alerts analysis a challenging task. In this paper, we propose a new approach based on hierarchical clustering that supports intrusion alert analysis in two main steps. First, it correlates historical alerts to identify the most common strategies attackers have used. Then, it associates upcoming alerts in real time according to the strategies discovered in the first step. The experiments were performed using a real dataset from the University of Maryland. The results showed that the proposed approach could properly identify the attack strategy patterns from historical alerts, and organize the upcoming alerts into a smaller amount of meaningful hyper-alerts.

scholarly journals Deep Learning Based Truth Discovery Algorithm for Research the Genuineness of Given Text Corpus

2019 ◽  
Vol 8 (2S3) ◽  
pp. 605-611
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Multiple Sources ◽  
Text Data ◽  
Text Corpus ◽  
Automatic Feature Extraction ◽  
Deep Learning Algorithm ◽  
Truth Discovery ◽  
Improved Accuracy

Lot of research has gone into Natural language processing and the state of the art algorithms in deep learning that unambiguously helps in converting an English text into a data structure without loss of meaning. Also with the advent of neural networks for learning word representations as vectors has helped a lot in revolutionizing the automatic feature extraction from text data corpus. A combination of word embedding and the use of a deep learning algorithm like a convolution neural network helped in better accuracy for text classification. In this era of Internet of things and the voluminous amounts of data that is overwhelming the users determining the veracity of the data is a very challenging task. There are many truth discovery algorithms in literature that help in resolving the conflicts that arise due to multiple sources of data. These algorithms help in estimating the trustworthiness of the data and reliability of the sources. In this paper, a convolution based truth discovery with multitasking is proposed to estimate the genuineness of the data for a given text corpus. The proposed algorithm has been tested on analysing the genuineness of Quora questions dataset and experimental results showed an improved accuracy and speed over other existing approaches.

scholarly journals DEEP LEARNING: NEW APPROACH FOR DETECTING SCHOLAR EXAMS FRAUD

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 103-107
Author(s):  
S. El Kohli ◽  
Y. Jannaj ◽  
M. Maanan ◽  
H. Rhinane
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Experimental Studies ◽  
Validation Data ◽  
Data Set ◽  
New Approach ◽  
Detection Systems ◽  
Machine Learning Applications ◽  
Worldwide Phenomenon ◽  
3D Cnn

Abstract. Cheating in exams is a worldwide phenomenon that hinders efforts to assess the skills and growth of students. With scientific and technological progress, it has become possible to develop detection systems in particular a system to monitor the movements and gestures of the candidates during the exam. Individually or collectively. Deep learning (DL) concepts are widely used to investigate image processing and machine learning applications. Our system is based on the advances in artificial intelligence, particularly 3D Convolutional Neural Network (3D CNN), object detector methods, OpenCV and especially Google Tensor Flow, to provides a real-time optimized Computer Vision. The proposal approach, we provide a detection system able to predict fraud during exams. Using the 3D CNN to generate a model from 7,638 selected images and objects detector to identify prohibited things. These experimental studies provide a detection performance with 95% accuracy of correlation between the training and validation data set.

Case Studies in Amalgamation of Deep Learning and Big Data

2018 ◽  
pp. 159-174 ◽  
Author(s):  
Balajee Jeyakumar ◽  
M.A. Saleem Durai ◽  
Daphne Lopez
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Language Processing ◽  
Predictive Analytics ◽  
Vital Role ◽  
Absolute Amount ◽  
Water Resource System ◽  
The World ◽  
Research Domain ◽  
Resource System

Deep learning is now more popular research domain in machine learning and pattern recognition in the world. It is widely success in the far-reaching area of applications such as Speech recognition, Computer vision, Natural language processing and Reinforcement learning. With the absolute amount of data accessible nowadays, big data brings chances and transformative possible for several sectors, on the other hand, it also performs on the unpredicted defies to connecting data and information. The size of the data is getting larger, and deep learning is imminent to play a vital role in big data predictive analytics solutions. In this paper, we make available a brief outline of deep learning and focus recent research efforts and the challenges in the fields of science, medical and water resource system.

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