IMAGE CLASSIFIERS FOR NETWORK INTRUSIONS

Author :  David A. Noever

Affiliation :  PeopleTec, Inc., Huntsville, Alabama

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 05, April, 2021

Abstract :

This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important sourcedestination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle.

Keyword :  Neural Networks, Computer Vision, Image Classification, Intrusion Detection, MNIST Benchmark.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110504.pdf

Informative Multimodal Unsupervised Image-to-Image Translation

Author :  Tien Tai Doan

Affiliation :  University of Evry Val d’Essonne

Country :  France

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 05, April, 2021

Abstract :

We propose a new method of multimodal image translation, called InfoMUNIT, which is an extension of the state-of-the-art method MUNIT. Our method allows controlling the style of the generated images and improves their quality and diversity. It learns to maximize the mutual information between a subset of style code and the distribution of the output images. Experiments show that our model cannot only translate one image from the source domain to multiple images in the target domain but also explore and manipulate features of the outputs without annotation. Furthermore, it achieves a superior diversity and a competitive image quality to state-of-the-art methods in multiple image translation tasks.

Keyword :  Multimodal Image-to-Image Translation, Mutual Information, GANs, Manipulating Features, Disentangled Representation

For More Details :  https://aircconline.com/csit/papers/vol11/csit110503.pdf

FEDERATED IDENTITY MANAGEMENT (FIDM) SYSTEMS LIMITATION AND SOLUTIONS

Author :  Maha Aldosary

Affiliation :  Saud Islamic University

Country :  KSA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 05, April, 2021

Abstract :

Efficient identity management system has become one of the fundamental requirements for ensuring safe, secure, and transparent use of identifiable information and attributes. FIdM allows users to distribute their identity information across security domains which increase the portability of their digital identities. However, it also raises new architectural challenges and significant security and privacy issues that need to be mitigated. In this paper, we presented the limitations and risks in Federated Identity Management system and discuss the results and proposed solutions.

Keyword :  Federated Identity Management, Identity Management, Limitations, Identity Federation.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110502.pdf

Role-Based Embedded Domain-Specific Language for Collaborative Multi-Agent Systems through Blockchain Technology

Author :  Or¸cun Oru¸c

Affiliation :  Software Technology Group

Country :  Germany

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 05, April, 2021

Abstract :

Multi-agent systems have evolved with their complexities over the past few decades. To create multi-agent systems, developers should understand the design, analysis, and implementation together. Agent-oriented software engineering applies best practices through mainly software agents with abstraction levels in the multi-agent systems. However, abstraction levels take a considerable amount of time due to the design complexity and adversity of the analysis phase before implementing them. Moreover, trust and security of multi-agent systems have never been detailed in the design and analysis phase even though the implementation of trust and security on the tamper-proof data are necessary for developers. Nonetheless, object-oriented programming is the right way to do it, when implementing complex software agents, one of the major problems is that the object-oriented programming approach still has a complex process-interaction and a burden of event-goal combination to represent actions by multi-agents. Designated roles with their relationships, invariants, and constraints of roles can be constructed based on blockchain contracts between agents. Furthermore, in the case of new agents who participate in an agent network, decentralization and transparency are two key parameters, which agents can exchange trusted information and reach a consensus aspect of roles. This study will take the software agent development as a whole with analysis, design, and development with role-object pattern in terms of smart contract applications. In this paper, we aim to propose a role-based domain-specific language that enables smart contracts which can be used in agent-oriented frameworks. Furthermore, we would like to refer to methodology, results of the research, and case study to enlighten readers in a better way. Finally, we summarize findings and highlight the main research points by inferencing in the conclusion section.

Keyword :  Software agents, Domain-specific languages, Blockchain technology, Smart contracts, Role-based programming languages.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110501.pdf

ADGRAPH: ACCURATE, DISTRIBUTED TRAINING ON LARGE GRAPHS

Author :  Lizhi Zhang

Affiliation :  National University of Defence Technology

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 04, March, 2021

Abstract :

Graph neural networks (GNNs) have been emerging as powerful learning tools for recommendation systems, social networks and knowledge graphs. In these domains, the scale of graph data is immense, so that distributed graph learning is required for efficient GNNs training. Graph partition-based methods are widely adopted to scale the graph training. However, most of the previous works focus on scalability other than the accuracy and are not thoroughly evaluated on large-scale graphs. In this paper, we introduce ADGraph (accurate and distributed training on large graphs), exploring how to improve accuracy while keeping large-scale graph training scalability. Firstly, to maintain complete neighbourhood information of the training nodes after graph partitioning, we assign l-hop neighbours of the training nodes to the same partition. We also analyse the accuracy and runtime performance of graph training, with different l-hop settings. Secondly, multi-layer neighbourhood sampling is performed on each partition, so that the mini-batch generated can accurately train target nodes. We study the relationship between convergence accuracy and the sampled layers. We also find that partial neighbourhood sampling can achieve better performance than full neighbourhood sampling. Thirdly, to further overcome the generalization error caused by large-batch training, we choose to reduce batchsize after graph partitioned and apply the linear scaling rule in distributed optimization. We evaluate ADGraph using GraphSage and GAT models with ogbn-products and Reddit datasets on 32 GPUs. Experimental results show that ADGraph achieves better performance than the benchmark accuracy of GraphSage and GAT, while getting 24-29 times speedup on 32 GPUs.

Keyword :  Graph neural networks; Distributed training; Multi-GPU; Deep learning; Parameter Server.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110408.pdf

Towards Adversarial Genetic Text Generation

 Author :  Deniz Kavi

Affiliation :  The Koç School

Country :  Turkey

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 04, March, 2021

Abstract :

Text generation is the task of generating natural language, and producing outputs similar to or better than human texts. Due to deep learning’s recent success in the field of natural language processing, computer generated text has come closer to becoming indistinguishable to human writing. Genetic Algorithms have not been as popular in the field of text generation. We propose a genetic algorithm combined with text classification and clustering models which automatically grade the texts generated by the genetic algorithm. The genetic algorithm is given poorly generated texts from a Markov chain, these texts are then graded by a text classifier and a text clustering model. We then apply crossover to pairs of texts, with emphasis on those that received higher grades. The approach described in this paper was designed to be as modular as possible and as such, changes to the grading system and further improvements to the genetic algorithm are to be the focus of future research.

Keyword :  Genetic Text Generation, Network

For More Details :  https://aircconline.com/csit/papers/vol11/csit110407.pdf

A COLOR IMAGE BLIND DIGITAL WATERMARKING ALGORITHM BASED ON QR CODE

Author :  Xuecheng Gong

Affiliation :  Anhui Normal University

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 04, March, 2021

Abstract :

With the rapid development of network technology and multimedia, the current color image digital watermarking algorithm has the problems of small capacity and poor robustness. In order to improve the capacity and anti-attack ability of digital watermarking. A color image blind digital watermarking algorithm based on QR code is proposed. The algorithm combines Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). First, the color image was converted from RGB space to YCbCr space, and the Y component was extracted and the second-level discrete wavelet transform is performed; secondly, the LL2 subband was divided into blocks and carried out discrete cosine transform; finally, used the embedding method to embed the Arnold transform watermark information into the block. The experimental results show that the PSNR of the color image embedded with the QR code is 56.7159 without being attacked. After being attacked, its PSNR is more than 30dB and NC is more than 0.95. It is proved that the algorithm has good robustness and can achieve blind watermark extraction.

Keyword :  QR Code, Color Image, Arnold Transform, DWT

For More Details :  https://aircconline.com/csit/papers/vol11/csit110405.pdf

MANAGING THE COMPLEXITY OF CLIMATE CHANGE

Author :  Shann Turnbull

Affiliation :  Principal: International Institute for self-governance

Country :  Australia

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 04, March, 2021

Abstract :

This paper indicates how the knowledge of complex systems can be put into practice to counter climate change. A contribution of the paper is to show how individual behaviour, institutional analysis, political science and management can be grounded and integrated into the complexity of natural systems to introduce mutual sustainability. Bytes are used as the unit of analysis to explain how nature governs complexity on a more reliable and comprehensive basis than can be achieved by humans using markets and hierarchies. Tax incentives are described to increase revenues while encouraging organisations to adopt elements of ecological governance found in nature and in some social organisations identified by Ostrom and the author. Ecological corporations provide benefits for all stakeholders. This makes them a common good to promote global common goods like enriching democracy from the bottom up while countering: climate change, pollution, and inequalities in power, wealth and income.

Keyword :  Bytes, Climate Change, Common Good, Ecological Governance, Tensegrity

For More Details :  https://aircconline.com/csit/papers/vol11/csit110402.pdf

A DEEP LEARNING APPROACH TO NIGHTFIRE DETECTION BASED ON LOW-LIGHT SATELLITE

Author :  Yue Wang

Affiliation :  Harbin Institute of Technology

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 04, March, 2021

Abstract :

Wildfires are a serious disaster, which often cause severe damages to forests and plants. Without an early detection and suitable control action, a small wildfire could grow into a big and serious one. The problem is especially fatal at night, as firefighters in general miss the chance to detect the wildfires in the very first few hours. Low-light satellites, which take pictures at night, offer an opportunity to detect night fire timely. However, previous studies identify night fires based on threshold methods or conventional machine learning approaches, which are not robust and accurate enough. In this paper, we develop a new deep learning approach, which determines night fire locations by a pixel-level classification on low-light remote sensing image. Experimental results on VIIRS data demonstrate the superiority and effectiveness of the proposed method, which outperforms conventional threshold and machine learning approaches.

Keyword :  Night fire detection, pixel segmentation, low-light satellite image

For More Details :  https://aircconline.com/csit/papers/vol11/csit110401.pdf