scholarly journals FastText-Based Local Feature Visualization Algorithm for Merged Image-Based Malware Classification Framework for Cyber Security and Cyber Defense

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 460 ◽  
Author(s):  
Sejun Jang ◽  
Shuyu Li ◽  
Yunsick Sung
Keyword(s):  
Cyber Security ◽  
Local Features ◽  
Cyber Attacks ◽  
Local Feature ◽  
Easy Access ◽  
Malware Classification ◽  
Classification Framework ◽  
Starting Point ◽  
Global And Local ◽  
Feature Visualization

The importance of cybersecurity has recently been increasing. A malware coder writes malware into normal executable files. A computer is more likely to be infected by malware when users have easy access to various executables. Malware is considered as the starting point for cyber-attacks; thus, the timely detection, classification and blocking of malware are important. Malware visualization is a method for detecting or classifying malware. A global image is visualized through binaries extracted from malware. The overall structure and behavior of malware are considered when global images are utilized. However, the visualization of obfuscated malware is tough, owing to the difficulties encountered when extracting local features. This paper proposes a merged image-based malware classification framework that includes local feature visualization, global image-based local feature visualization, and global and local image merging methods. This study introduces a fastText-based local feature visualization method: First, local features such as opcodes and API function names are extracted from the malware; second, important local features in each malware family are selected via the term frequency inverse document frequency algorithm; third, the fastText model embeds the selected local features; finally, the embedded local features are visualized through a normalization process. Malware classification based on the proposed method using the Microsoft Malware Classification Challenge dataset was experimentally verified. The accuracy of the proposed method was approximately 99.65%, which is 2.18% higher than that of another contemporary global image-based approach.

scholarly journals Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

2021 ◽  
Vol 13 (22) ◽  
pp. 4518
Author(s):  
Xin Zhao ◽  
Jiayi Guo ◽  
Yueting Zhang ◽  
Yirong Wu
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Semantic Segmentation ◽  
Local Features ◽  
Local Feature ◽  
Global Information ◽  
Deep Convolutional Neural Networks ◽  
Global Representation ◽  
Local Feature Extraction ◽  
Global And Local

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods.

scholarly journals Commercial Maritime and Cyber Risk Management

2019 ◽  
Vol 5 (1) ◽  
pp. 46-48
Author(s):  
Akash RANA
Keyword(s):  
Risk Management ◽  
Best Practices ◽  
Cyber Security ◽  
Cyber Attacks ◽  
Signal Interference ◽  
Cyber Crime ◽  
British Government ◽  
Code Of Practice ◽  
Starting Point ◽  
Cyber Risk

The starting point of the paper is the recognition of the growing threat of cyber-attacks to commercial maritime. Constantly growing dependency on technology has obvious advantages, on the other hand, however, it makes commercial maritime vessels progressively more vulnerable to cyber-crime, including GPS signal interference, malware attacks or even gaining control over ships’ systems and networks. The main objective of the paper is to present and discuss the Guidelines on Cyber Security Onboard Ships developed by the International Maritime Organization, including best practices for implementation of cyber risk management. The article’s goal is to summarize the guidelines and to familiarize the reader with the reasons why and the methods how they should be implemented. The paper is concluded with an example how the Guidelines can be adopted by national authorities, i.e., a brief presentation of “Code of Practice: Cyber Security for Ships” – a document developed by the British government that transposes the IMO guidelines.

scholarly journals Vehicle Type Recognition Combining Global and Local Features via Two-Stage Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Sun ◽  
Xiaorui Zhang ◽  
Shunshun Shi ◽  
Jun He ◽  
Yan Jin
Keyword(s):  
Nearest Neighbor ◽  
Local Features ◽  
Detection Algorithm ◽  
Local Feature ◽  
Gabor Wavelet ◽  
Global Feature ◽  
Two Stage ◽  
Vehicle Type ◽  
Stage Classification ◽  
Global And Local

This study proposes a new vehicle type recognition method that combines global and local features via a two-stage classification. To extract the continuous and complete global feature, an improved Canny edge detection algorithm with smooth filtering and non-maxima suppression abilities is proposed. To extract the local feature from four partitioned key patches, a set of Gabor wavelet kernels with five scales and eight orientations is introduced. Different from the single-stage classification, where all features are incorporated into one classifier simultaneously, the proposed two-stage classification strategy leverages two types of features and classifiers. In the first stage, the preliminary recognition of large vehicle or small vehicle is conducted based on the global feature via a k-nearest neighbor probability classifier. Based on the preliminary result, the specific recognition of bus, truck, van, or sedan is achieved based on the local feature via a discriminative sparse representation based classifier. We experiment with the proposed method on the public and established datasets involving various challenging cases, such as partial occlusion, poor illumination, and scale variation. Experimental results show that the proposed method outperforms existing state-of-the-art methods.

scholarly journals Key Vulnerabilities of Industrial Automation and Control Systems and Actions to Prevent Cyber-Attacks

2016 ◽  
Vol 12 (1) ◽  
pp. 9 ◽  
Author(s):  
Isidro Calvo ◽  
Ismael Etxeberria-Agiriano ◽  
Miguel A Iñigo ◽  
Pablo González-Nalda
Keyword(s):  
Control Systems ◽  
Cyber Security ◽  
Communication Technologies ◽  
Cyber Attacks ◽  
Industrial Automation ◽  
Internet Protocols ◽  
The Public ◽  
Starting Point ◽  
Automation And Control ◽  
And Control

Until recently, Industrial Automation and Control Systems (IACS) were largely isolated from corporate systems by means of proprietary protocols, which facilitated their protection against cyber-attacks under the principle of security through obscurity. However, the widespread adoption of the new communication technologies, such as the Internet protocols and wireless communications has changed this scenario. During recent years there have been many evidences of cyber-attacks to IACS that exploit their vulnerabilities. Unfortunately, these attacks have increased significantly during the last five years, and we should be aware that only the tip of the iceberg comes to the public knowledge. The purpose of this article is twofold: (1) to raise awareness about the security vulnerabilities that most companies are facing at their IACS and (2) to set a starting point by proposing a roadmap that seeks to guide designers and programmers in the new and complex world of industrial cyber-security.

Face recognition based on fusion of global and local feature

2014 ◽  
Vol 27 (9) ◽  
pp. 817-822 ◽  
Author(s):  
Min Hu ◽  
Tianmei Cheng ◽  
Xiaohua Wang
Keyword(s):  
Face Recognition ◽  
Local Feature ◽  
Global And Local

scholarly journals A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring

2021 ◽  
Vol 11 (5) ◽  
pp. 2174
Author(s):  
Xiaoguang Li ◽  
Feifan Yang ◽  
Jianglu Huang ◽  
Li Zhuo
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Dynamic Scene ◽  
Feature Maps ◽  
Training Scheme ◽  
Real Scene ◽  
Global And Local

Images captured in a real scene usually suffer from complex non-uniform degradation, which includes both global and local blurs. It is difficult to handle the complex blur variances by a unified processing model. We propose a global-local blur disentangling network, which can effectively extract global and local blur features via two branches. A phased training scheme is designed to disentangle the global and local blur features, that is the branches are trained with task-specific datasets, respectively. A branch attention mechanism is introduced to dynamically fuse global and local features. Complex blurry images are used to train the attention module and the reconstruction module. The visualized feature maps of different branches indicated that our dual-branch network can decouple the global and local blur features efficiently. Experimental results show that the proposed dual-branch blur disentangling network can improve both the subjective and objective deblurring effects for real captured images.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Cybersecurity Teamwork: A Review of Current Practices and Suggested Improvements

2020 ◽  
Vol 64 (1) ◽  
pp. 451-455
Author(s):  
Richard J. Simonson ◽  
Joseph R. Keebler ◽  
Mathew Lessmiller ◽  
Tyson Richards ◽  
John C. Lee
Keyword(s):  
Cyber Security ◽  
Cyber Attacks ◽  
Team Science ◽  
The Past ◽  
Insight Into ◽  
Current Practices

As cyber-attacks and their subsequent responses have become more frequent and complex over the past decade, research into the performance and effectiveness of cybersecurity teams has gained an immense amount of traction. However, investigation of teamwork in this domain is lacking due to the exclusion of known team competencies and a lack of reliance on team science. This paper serves to provide insight into the benefit that can be gained from utilizing the extant teamwork literature to improve teams’ research and applications in the domain of cyber-security.

scholarly journals Security of Blockchain-Based Supply Chain Management Systems: Challenges and Opportunities

2021 ◽  
Vol 11 (12) ◽  
pp. 5585
Author(s):  
Sana Al-Farsi ◽  
Muhammad Mazhar Rathore ◽  
Spiros Bakiras
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Information Exchange ◽  
Cyber Attacks ◽  
Management Systems ◽  
Chain Management ◽  
Counter Measures ◽  
Starting Point ◽  
Challenges And Opportunities ◽  
Unique Approach

Blockchain is a revolutionary technology that is being used in many applications, including supply chain management. Although, the primary motive of using a blockchain for supply chain management is to reduce the overall production cost while providing the comprehensive security to the system. However, current blockchain-based supply-chain management (BC-SCM) systems still hold the possibility of cyber attacks. Therefore, the goal of this study is to investigate practical threats and vulnerabilities in the design of BC-SCM systems. As a starting point, we first establish key requirements for the reliability and security of supply chain management systems, i.e., transparency, privacy and traceability, and then discern a threat model that includes two distinctive but practical threats including computational (i.e., the ones that threaten the functionality of the application) and communication (i.e., the ones that threaten information exchange among interconnected services of the application). For investigation, we follow a unique approach based on the hypothesis that reliability is pre-requisite of security and identify the threats considering (i) design of smart contracts and associated supply chain management applications, (ii) underlying blockchain execution environment and (iii) trust between all interconnected supply management services. Moreover, we consider both academic and industry solutions to identify the threats. We identify several challenges that hinder to establish reliability and security of the BC-SCM systems. Importantly, we also highlight research gaps that can help to establish desired security of the BC-SCM. To the best of our knowledge, this paper is the first effort that identifies practical threats to blockchain-based supply chain management systems and provides their counter measures. Finally, this work establishes foundation for future investigation towards practical security of BC-SCM system.

Sign in / Sign up

Export Citation Format

Share Document