scholarly journals Poison in the Well: Securing the Shared Resources of Machine Learning

2021 ◽  
Author(s):  
Andrew Lohn ◽  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Open Source ◽  
The Internet ◽  
Shared Resources ◽  
Modern Machine ◽  
Cyber Infrastructure

Modern machine learning often relies on open-source datasets, pretrained models, and machine learning libraries from across the internet, but are those resources safe to use? Previously successful digital supply chain attacks against cyber infrastructure suggest the answer may be no. This report introduces policymakers to these emerging threats and provides recommendations for how to secure the machine learning supply chain.

Analysis of risk factors in financial supply chain based on machine learning and IoT technology

2020 ◽  
pp. 1-11
Author(s):  
Sun Hongjin
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Supply Chain ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
The Internet ◽  
Supply Chain Risk ◽  
Financing Model ◽  
Internet Of Things Technology ◽  
The Internet Of Things

The financial supply chain is affected by many factors, so an artificial intelligence model is needed to identify supply chain risk factors. This article combines the actual situation of the financial supply chain, improves the traditional machine learning algorithm, and takes the actual company as an example to build a corresponding risk factor recognition model. From the perspective of optimizing the supply chain financial model, this paper combines the functions of the Internet of Things technology and the characteristics of the supply chain financial inventory pledge financing model to design a new type of inventory pledge financing model. The new model makes up for the defects of the original model through the functions of intelligent identification, visual tracking and cloud computing big data processing of the Internet of Things technology. In addition, this study verifies the performance of the system, uses a large amount of data in Internet finance as an object, and obtains the corresponding results through mathematical statistical analysis. The research results show that the model proposed in this paper has a certain effect on the identification and analysis of financial supply chain risk factors.

scholarly journals Forensic Volatile Memory For Malware Detection Using Machine Learning Algorithm

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Fikri Bahtiar ◽  
Nur Widiyasono ◽  
Aldy Putra Aldya
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Malware Analysis ◽  
Cyber Crime ◽  
Volatile Memory ◽  
Ram Analysis

Forensics from volatile memory plays an important role in the investigation of cyber crime. The acquisition of RAM Memory or other terms of RAM dump can assist forensic investigators in retrieving much of the information related to crime. There are various tools available for RAM analysis including Volatility, which currently dominates open source forensic RAM tools. It has happened that many forensic investigators are thinking that they probably have malware in the RAM dump. And, if they do exist, they're still not very capable Malware Analysts, so it's hard for them to analyze the possibilities of malware in a RAM dump. The availability of tools such as Volatility allows forensic investigators to identify and link the various components to conclude whether the crime was committed using malware or not. However, the use of volatility requires knowledge of basic commands as well as static malware analysis. This work is done to assist forensic investigators in detecting and analyzing possible malware from dump RAM. This work is based on the volatility framework and the result is a Forensic tool for analyzing RAM dumps and detecting possible malware in it using machine learning algorithms in order to detect offline (not connected to the internet).

(World Wide) Work 2.0

2016 ◽  
Vol 11 (1) ◽  
pp. 91-103
Author(s):  
Carole Cusack ◽  
David Pecotic
Keyword(s):  
Open Source ◽  
World Wide ◽  
First Generation ◽  
Dominant Role ◽  
The Internet ◽  
Digital Ethnography ◽  
Beliefs And Practices ◽  
Web Presence ◽  
Theoretical Approaches ◽  
Online Engagement

The occult and the internet intersect in four ways: as a static medium for information; as a space where contested information or ideological conflict may occur; as a facilitator of communication; and as a medium for esoteric practice. The last type of activity is rare, but it is intriguing, in that technology can shape and inform beliefs and practices in unanticipated ways. Online engagement with the ‘Work’, the movement produced by the Greek Armenian spiritual teacher and esotericist G. I. Gurdjieff (c. 1866-1949) and his immediate followers, is an under-researched instance of online esoteric practice. This article addresses this scholarly desideratum, bringing the theoretical approaches of online religion and digital ethnography to bear on the Gurdjieff Internet Guide (GIG) website, founded by Reijo Oksanen (b. 1942) and later maintained by Kristina Turner, who created an accompanying Facebook page. The GIG manifests a shift away from the sectarian secrecy of the ‘Foundation’ groups, founded by Jeanne de Salzmann (1889-1990) after Gurdjieff’s death to formalise and protect the content of the Work, and the limited web presence that the Foundation permits. The GIG moves towards an ecumenical ‘open source’ approach to the dissemination of Gurdjieff’s teachings rooted in independent groups founded by other first generation followers of Gurdjieff who remained outside of the Foundation. It is argued that the deregulation of the religious and spiritual marketplace of the contemporary West, coupled with the dominant role played by the Internet in disseminating information, has radically transformed the Gurdjieff tradition, collapsing hierarchies and esoteric strategies, democratizing access for seekers, and creating new ritual and teaching modes.

Pengembangan Sistem VOIP Menggunakan Server Issabel Versi 4.0 dan Tunnel EOIP pada OMNI Hospital Alam Sutera

2018 ◽  
Vol 18 (1) ◽  
pp. 13-20
Author(s):  
Elly Mufida ◽  
David Wardana Agus Rahayu
Keyword(s):  
Operating System ◽  
Open Source ◽  
End Of Life ◽  
Communication System ◽  
Communication Protocol ◽  
The Internet ◽  
Migration Process ◽  
Web Browser ◽  
Backup Path ◽  
E Mail

The VoIP communication system at OMNI Hospital Alam Sutera uses the Elastix 2.5 server with the Centos 5.11 operating system. Elastix 2.5 by the developer has been declared End of Life. The server security system is a serious concern considering that VoIP servers can be accessed from the internet. Iptables and fail2ban applications are applications that are used to limit and counteract those who try to attack the VoIP server. One application that can be used as an open source VoIP server is the Issabel Application version 4.0. The migration process from Elastix 2.5 application to Issabel 4.0 by backing up all configurations in the Elastix 2.5 application through a web browser including the configuration of endpoints, fax, e-mail, asterisk. After the backup file is downloaded then upload the backup file to the Issabel 4.0 application then run the migration process. Adding a backup path as a failover connection is needed because the VoIP communication protocol between the OMNI Hospitals Group still uses one path so that when there is a problem in the connection path, the communication protocol will stop. The tunnel EoIP is a protocol used as a backup path between the OMNI Hospitals Group site.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

The Impact of E-Procurement Systems in the Biomedical Industry

2020 ◽  
Vol 27 (2) ◽  
pp. 66
Author(s):  
A. Seetharaman ◽  
Nitin Patwa ◽  
Simon Lai Koek Wai ◽  
Ahammed Shamir
Keyword(s):  
Supply Chain ◽  
Cost Savings ◽  
The Internet ◽  
Business Analytics ◽  
Process Automation ◽  
Short Term ◽  
Compliance Cost ◽  
Biomedical Industry ◽  
The Impact

The evolution of the Internet has revolutionised the sourcing and procurement processes in organisations in every industry. The focus of this paper is to analyse the perception of business users on the factors which impact the usage of eprocurement systems in the biomedical industry. There are four factors identified in this research: i.e. control and compliance, cost savings, process automation, and improvements and transparency. The benefit of achieving process automation is the first biggest factor, followed by the need for control and compliance, and transparency, being the second and third factors respectively. The fourth factor, cost savings, is ignored because the users perceived that cost savings will not be realised in the short term, and the returns from the investment could be a couple of years after the eprocurement system has been fully operational. The research also concludes that the ability to perform business analytics and to strengthen the supply chain are the most important factors in measuring the success in the adoption of e-procurement systems

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

2019 ◽  
Vol 12 (3) ◽  
pp. 171-179 ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Product Life Cycle ◽  
Consumer Preference ◽  
Bullwhip Effect ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

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