Survey of avifauna housed in the wild animal triage centers in the state of Bahia, period 2009 to 2019, emphasizing trafficking

ABSTRACT: Brazil is one of the primary suppliers of wildlife for trafficking, which constitutes a significant threat to biodiversity, which can cause the extinction of species, especially birds, the most important victims of trafficking. This study analyzed the wild avifauna collected in the Triage Centers of Wild Animals (CETAS) of Bahia, to inventory the most apprehended species that are threatened with extinction, and estimate the municipalities that function as suppliers and traders of wildlife. Data from 2009 to 2019 were made available by the Salvador and Vitoria da Conquista units, and that from 2010 to 2016 was made available by the, Porto Seguro unit. The survey was conducted through the entry records of birds, considering apprehension, spontaneous deliveries, rescues, and transfers between units involved in the study. Out of the total 80,948 birds analyzed, 65,315 (80.68%) were apprehended; 7,885 (9.74%) were voluntary deliveries; 6,196 (7.65%) were rescues, and 1,034 (1.28%) were transfers. There was no entry modality for 518 (0.64%) specimens. The most trafficked species were Sicalis flaveola (Saffron Finch), Sporophila nigricollis (Yellow-bellied Seedeater), Paroaria dominicana (Red-cowlead Cardinal), Cyanoloxia brissonii (Ultramarine Grosbeak), Sporophila caerulescens (Double-collared Seedeater), and Sporophila albogularis (White-throated Seedeater). Nine hundred twenty six specimens exhibited some threat. Analysis showed that the greatest flow of bird traffic occurs on BR 242 and BR 116. The municipalities that comprise the mesoregions Center-South, Center-North Baiano, and the Metropolitan Region of Salvador are those with greater concentrations of illegal activity. Irecê and Paulo Afonso constitute areas for the capture and commercialization of wild animals.

Bailey and Peak and Sorensen on Management and Planning

This chapter examines two approaches to the concept of management and the planning process: firstly the approach proposed by Bailey and Peak and secondly by Sorensen. Bailey and Peak present a review of all business functions to determine the competency of each function and then suggest a number of research and analysis techniques for reviewing the finances and the various external interactions with the organisation from competitors. Sorensen proposes using the resource-based view to analyse the competitive situation, followed by creating the business model and then developing the planning documents for business development. Sorensen asserts that ‘what the customer values' is a focal point of the approach to business development. The internal strength and weakness analysis investigates the value chain, processes, resources, and capabilities, and the external opportunities and threat analysis review customers, market, and competitors.

Vigorous IDS on Nefarious Operations and Threat Analysis Using Ensemble Machine Learning

The geometric increase in the usage of computer networking activities poses problems with the management of network normal operations. These issues had drawn the attention of network security researchers to introduce different kinds of intrusion detection systems (IDS) which monitor data flow in a network for unwanted and illicit operations. The violation of security policies with nefarious motive is what is known as intrusion. The IDS therefore examine traffic passing through networked systems checking for nefarious operations and threats, which then sends warnings if any of these malicious activities are detected. There are 2 types of detection of malicious activities, misuse detection, in this case the information about the passing network traffic is gathered, analyzed, which is then compared with the stored predefined signatures. The other type of detection is the Anomaly detection which is detecting all network activities that deviates from regular user operations. Several researchers have done various works on IDS in which they employed different machine learning (ML), evaluating their work on various datasets. In this paper, an efficient IDS is built using Ensemble machine learning algorithms which is evaluated on CIC-IDS2017, an updated dataset that contains most recent attacks. The results obtained show a great increase in the rate of detection, increase in accuracy as well as reduction in the false positive rates (FPR).

5G NETWORK THREAT ANALYSIS

This article is about analyzing threats in the network 5G.

Multi-Source Knowledge Reasoning for Data-Driven IoT Security

Nowadays, there are different kinds of public knowledge bases for cyber security vulnerability and threat intelligence which can be used for IoT security threat analysis. However, the heterogeneity of these knowledge bases and the complexity of the IoT environments make network security situation awareness and threat assessment difficult. In this paper, we integrate vulnerabilities, weaknesses, affected platforms, tactics, attack techniques, and attack patterns into a coherent set of links. In addition, we propose an IoT security ontology model, namely, the IoT Security Threat Ontology (IoTSTO), to describe the elements of IoT security threats and design inference rules for threat analysis. This IoTSTO expands the current knowledge domain of cyber security ontology modeling. In the IoTSTO model, the proposed multi-source knowledge reasoning method can perform the following tasks: assess the threats of the IoT environment, automatically infer mitigations, and separate IoT nodes that are subject to specific threats. The method above provides support to security managers in their deployment of security solutions. This paper completes the association of current public knowledge bases for IoT security and solves the semantic heterogeneity of multi-source knowledge. In this paper, we reveal the scope of public knowledge bases and their interrelationships through the multi-source knowledge reasoning method for IoT security. In conclusion, the paper provides a unified, extensible, and reusable method for IoT security analysis and decision making.

Hierarchical Multi-Blockchain System for Parallel Computation in Cryptocurrency Transfers and Smart Contracts

Most of the existing smart-contract-based cryptocurrencies, such as Ethereum, use an account-based ledger. However, while the account-based model is advantageous for the efficient use of smart contracts and the increased exchangeability of cryptocurrencies, it is not well-suited to the parallel execution of smart contracts. However, unspent transaction output (UTXO)-based cryptocurrencies such as Bitcoin are advantageous for parallel cryptocurrency transfers but not well-suited to smart contracts. In this paper, we propose a hierarchical multi-blockchain system that uses multiple pairs of sidechain and dual-sidechains extended by independent block mining in their blockchain networks and a mainchain to control the branching and connection process of sidechains and dual sidechains. In the proposed method, one pair of a sidechain and dual sidechain forms one shard. The proposed method uses multiple shards to execute cryptocurrency transfers and smart contracts in parallel. In addition, the proposed model uses an accoutchain to record the resulting state changes generated by smart contract executions in each shard and securely share them with all other nodes. The proposed method uses a modifiable blockchain structure for the accountchain to obtain the database to record the smart contract execution results in each shard in as small and secure a manner as possible to ensure that all nodes trust the recorded results without executing smart contracts themselves. To examine the validity of the proposed method, we conducted a threat analysis of the proposed method by examining possible attacks in various scenarios as a thought experiment. This threat analysis concludes that the proposed blockchain system can execute smart contracts in parallel while keeping the concurrency in resulting state changes secure.