Analysis of Security Based Accessibility Control Models for Cloud Computing

Access control has become the most necessary requirement to limit unauthorized and privileged access to information systems in cloud computing. Access control models counter the additional security challenges like rules, domain names, job allocation, multi hosting and separation of tasks. This paper classifies the conventional and modern access control models which has been utilized to restrain these access flaws by employing a variety of practices and methodologies. It examine the frequent security threats to information confidentiality, integrity, data accessibility and their approach used for cloud solutions. This paper proposed a priority based task scheduling access control (PbTAC) model to secure and scheduled access of resources & services rendered to cloud user. PbTAC model will ensure the job allocation, tasks scheduling and security of information through its rule policies during transmission between parties. It also help in reducing system overhead by minimize the computation and less storage cost.

Machine Learning Approach using CNN for COVID-19 Pandemic Detection

Data science and machine learning are domain names in which data generation can assist with inside the fight towards the disease. Early caution systems which can are expecting how much a disease might effect society and permit the authorities to take suitable measures without disrupting the economy are extremely important. In the confrontation towards COVID-19 methods for forecasting the future cases primarily based totally on present data are extremely beneficial. The preceding are three strategies of machine learning which are discussed: Two for predicting the wide variety of positive cases in the coming ten days, and one for identifying COVID-19 infection via way of means of analyzing the patient's chest x-ray image. Various algorithms had been tested, and the only that produced the maximum accurate consequences become selected for use on this take a look at to forecast confirmed cases in India. Various government entities can leverage the findings to take corrective action. Now that methods for forecasting infectious disease are available, COVID-19 can be less complicated to combat.

Current state and role of domain names as an object of legal relations

The article is dedicated to the legal study of domain names. The authors of the article analyzed the scientific literature on the formation of the concept of "domain names". Theoretical and practical proposals have been formed to improve legislation in the field of the provision of domain names on the Internet information and communication network. General and special scientific methods were used. In addition, the subjects of the legal relationships under study were identified, analyzed exhaustively and, as a contribution to the research, a draft contract for the provision of paid Internet services was proposed, considering the details of the domain names and at the same time identifying the rights and obligations of the parties. In short, judicial practice materials relating to the attribution of domain names, the means of individualization and the Russian domain name market have been studied. Conclusions have been drawn on the need to improve Russian legislation in the field of paid provision of Internet services, namely the provision of domain name services, by amending and adding to existing regulatory legal acts.

MONOPOLI DI BIDANG TEKNOLOGI INFORMASI

Competition in the business world causes business actors to sometimes resort to various ways to conduct unfair business competition, resulting in losses for other business actors. In the virtual world (information technology for example) this action is often found. So it is necessary to study how the prohibition of monopolistic practices and unfair business competition against businesses in the field of information technology. This research is a normative legal research, the main data of which is obtained through library research. Based on the results of the research, it is known that the form of market monopoly and unfair business competition in information technology law can be in the form of vertical integration, discrimination of business actors, taking of domain names, and other actions that can harm business competitors. The Tying Arrangement in the Microsoft case is a form of unfair business competition, in which the giant software company Microsoft is accused of violating the antitrust law by taking Tying Arrangements by linking the windows product (the tying product) with the internet explorer browser product (the tied product). The Tying Arrangement was allegedly carried out in order to win a monopoly in the internet browser product market.

Detecting Web-Based Attacks with SHAP and Tree Ensemble Machine Learning Methods

Attacks using Uniform Resource Locators (URLs) and their JavaScript (JS) code content to perpetrate malicious activities on the Internet are rampant and continuously evolving. Methods such as blocklisting, client honeypots, domain reputation inspection, and heuristic and signature-based systems are used to detect these malicious activities. Recently, machine learning approaches have been proposed; however, challenges still exist. First, blocklist systems are easily evaded by new URLs and JS code content, obfuscation, fast-flux, cloaking, and URL shortening. Second, heuristic and signature-based systems do not generalize well to zero-day attacks. Third, the Domain Name System allows cybercriminals to easily migrate their malicious servers to hide their Internet protocol addresses behind domain names. Finally, crafting fully representative features is challenging, even for domain experts. This study proposes a feature selection and classification approach for malicious JS code content using Shapley additive explanations and tree ensemble methods. The JS code features are obtained from the Abstract Syntax Tree form of the JS code, sample JS attack codes, and association rule mining. The malicious and benign JS code datasets obtained from Hynek Petrak and the Majestic Million Service were used for performance evaluation. We compared the performance of the proposed method to those of other feature selection methods in the task of malicious JS code content detection. With a recall of 0.9989, our experimental results show that the proposed approach is a better prediction model.

Efficient Onboarding and Management of Members in Permissioned Blockchain Networks Utilizing TLS Certificates

Consortia blockchain networks face the issue of expanding their systems to new members. Onboarding processes are often cumbersome, as they require identifying the new participant, manually setting up rights, exchanging key material, and adding information about the new member to the consensus smart contract. Besides that, these processes are time-consuming and scale poorly. Identifying the members might be faulty as the pre-existing members might be deceived by malicious parties claiming to be someone else. This paper proposes a novel methodology to allow the onboarding of new parties without time-intensive off-chain processes. We establish identities of new consortia members by utilizing TLS certificates bound to publicly known domain names. With this identity scheme in place, the network operators can define rules such as only specific parties are allowed to join the network, e.g., only owners of *.edu domains. This methodology scales well, provides for extensive ruling and monitoring, and helps consortia blockchains to grow faster.

Identifying Influential SLD Authoritative Name Servers on the Internet

DNS plays an important role on the Internet. The addressing of most applications depends on the proper operation of DNS. The root servers and the top-level domain servers are relied upon by many domains on the Internet, and their security affects the whole Internet. As a result, more attention has been paid to the security of servers at these two levels. However, the security of second-level domains and their servers also needs to be brought to the forefront. This paper focuses on showing the complex resolving dependencies and identifying influential name servers for second-level domains. We start by detecting domain name resolution paths and building up a name dependency graph. Then we construct domain name resolution networks of different numbers and sizes, which are connected by a certain number of domain name resolution graphs. On this basis, the network is analyzed from the perspective of complex network analysis, and a multi-indicators node importance evaluation method based on partial order is proposed to identify the influential name servers of the network. Once these name servers are not properly configured and fail or are compromised by DDoS attacks, it will cause resolution failure for a wide range of domain names.

Edge-Based Detection and Classification of Malicious Contents in Tor Darknet Using Machine Learning

With the increase of data in the network, the load of servers and communication links becomes heavier and heavier. Edge computing can alleviate this problem. Due to a sea of malicious contents in Darknet, it is of high research value to combine edge computing with content detection and analysis. Therefore, this paper illustrates an intelligent classification system based on machine learning and Scrapy that can detect and judge fleetly categories of services with malicious contents. Because of the nondisclosure and short survival time of Tor Darknet domain names, obtaining uniform resource locators (URLs) and resources of the network is challenging. In this paper, we focus on a network based on the Onion Router (tor) anonymous communication system. We designed a crawler program to obtain the contents of the Tor network and label them into six classes. We also construct a dataset which contains URLs, categories, and keywords. Edge computing is used to judge the category of websites. The accuracy of the classifier based on a machine learning algorithm is as high as 89%. The classifier will be used in an operational system which can help researchers quickly obtain malicious contents and categorize hidden services.

"Off-Label" use of DNS

DNS is one of the most widely abused protocols that threat actors use to hide traffic. DNS is also actively used, or rather misused, by other service providers, vendors, etc., to provide enhanced services. An in-depth examination of DNS logs revealed several very interesting legitimate use cases of the DNS protocol, apart from the usual name resolution service function. We coined the term ?Off-label? use of DNS to represent those use cases. Legitimate here simply means using DNS for non-malicious purposes other than what it was traditionally designed for, providing domain name resolution; a dictionary service mapping domain names to corresponding IP addresses. One of the main reasons DNS is used, or possibly misused, for these off-label use cases is data transfer speed and reduced overhead. These use cases can often reveal important information about the clients and software they are running and can be leveraged by network security analysts to improve their defense of the network. This research will detail some of those legitimate off-label use cases and how analysts can use them to detect malware trends in the network and much more just by analyzing an enterprise?s DNS logs.

The Vulnerabilities of Trusted Notifier-Models in Russia: The Case of Netoscope

Current digital ecosystems are shaped by platformisation, algorithmic recommender systems, and news personalisation. These (algorithmic) infrastructures influence online news dissemination and therefore necessitate a reconceptualisation of how online media control is or may be exercised in states with restricted media freedom. Indeed, the degree of media plurality and journalistic independence becomes irrelevant when reporting is available but difficult to access; for example, if the websites of media outlets are not indexed or recommended by the search engines, news aggregators, or social media platforms that function as algorithmic gatekeepers. Research approaches to media control need to be broadened because authoritarian governments are increasingly adopting policies that govern the internet <em>through</em> its infrastructure; the power they leverage against private infrastructure owners yields more effective—and less easily perceptible—control over online content dissemination. Zooming in on the use of trusted notifier-models to counter online harms in Russia, we examine the Netoscope project (a database of Russian domain names suspected of malware, botnet, or phishing activities) in which federal censor Roskomnadzor cooperates with, e.g., Yandex (that downranks listed domains in search results), Kaspersky, and foreign partners. Based<strong> </strong>on publicly available reports, media coverage, and semi-structured interviews, the article analyses the degree of influence, control, and oversight of Netoscope’s participating partners over the database and its applications. We argue that, in the absence of effective legal safeguards and transparency requirements, the politicised nature of internet infrastructure makes the trusted notifier-model vulnerable to abuse in authoritarian states.