A New Approach Based On Honeybee Guarding System To Improve Intrusion Detection System

Serangan yang semakin meningkat terhadap rangkaian dalam pelbagai cara yang canggih mendapat perhatian daripada pihak keselamatan rangkaian. Increasing of network attacks with sophisticated forms has made the network security concern a significant necessity for such a network

A Bio-Inspired Trust Framework in Wireless Ad Hoc Networks

Cyber attacks are amongst the most serious threats facing people and organizations. In the face of the increasing complexity and effectiveness of these attacks, creative approaches to defense are required. Groups of insects survive due to their use of collaborative approaches with the unique ability to detect anomalies using primarily local data and very limited computational resources (i.e., limited brain power). These attributes are even more crucial for wireless ad hoc networks where the number of nodes and connections between those nodes are ephemeral. We propose a trust framework inspired by the detection mechanisms exhibited by bee swarms in which a wireless node can only observe and leverage the actions of their neighbors rather than the global knowledge of the network to make a decision. This leveraging of local knowledge is an important aspect of trust in wireless networks in which global state information is difficult to encapsulate. We also utilize models from binary voting to present a straightforward mathematical model for our bee-inspired trust framework in wireless ad hoc networks

Machine Learning and Computer Vision Techniques in Bee Monitoring Applications

Machine learning and computer vision are dynamically growing fields, which have proven to be able to solve very complex tasks. They could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. In this paper, we present an overview of the state-of-the-art computer vision and machine learning applications used for bee monitoring. We also demonstrate the potential of those methods as an example of an automated bee counter algorithm. The paper is aimed at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce to them its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use the machine learning techniques for other applications in bee monitoring

An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval

Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem for nuisance network administrators in detecting attacks is false alerts. Regardless of the methods implemented by this system, eliminating false alerts is still a huge problem. To describe data traffic passing through the network, a database of the network security layer (NSL) knowledge discovery in database (KDD) dataset is used. The massive traffic of data sent over the network contains excessive and duplicated amounts of information. This causes the classifier to be biased, reduce classification accuracy, and increase false alert. To that end, we proposed a model that significantly improve the accuracy of the intrusion detection system by eliminating false alerts, whether they are false negative or false positive negative alerts. The results show that the proposed intelligent exoplanet atmospheric retrieval (INARA) algorithm has improved accuracy and is able to detect new attack types efficiently

Regulating services

No abstract available

PROJETO GENERATIVO E OTIMIZAÇÃO DE DESEMPENHO IEQ DE EDIFÍCIOS ESCOLARES COM BASE EM UM ALGORITMO PARAMÉTRICO

This research aims to examine the potential of generative and optimization algorithms in the early stage of a school building design in Tabriz to achieve better IEQ. It also investigates the compatibility of the evolutionary optimization tools combined with a parametric model in stimulating building comfort performance in achieving an optimized design. This process includes four steps: defining the parametric building model, defining its material and construction properties, stimulation of thermal and visual comfort and carbon dioxide concentration, optimization, and choosing the best result. The adaptive PMV model is used for thermal comfort, imageless daylight glare probability is used for visual comfort, and a CO2 concentration is used for IAQ assessment. It was found that the performance of the options introduced by the algorithm is more appropriate than the design prototype. However, the results show that the samples are acceptable in carbon dioxide concentration. What needs further investigation is thermal and visual comfort. Among the studied variables on IEQ performance, the WWR ratio of the southern wall had the most significant impact. Based on the optimization results, thermal comfort changed in the range of 10%, visual comfort in the range of 30%, and CO2 concentration in the range of 0.19%.Esta pesquisa tem como objetivo examinar os potenciais de algoritmos generativos e de otimização na fase inicial de um projeto de edifício escolar em Tabriz para obter um melhor IEQ. Também investiga a compatibilidade das ferramentas de otimização evolutiva combinadas com um modelo paramétrico para estimular o desempenho de conforto de construção na obtenção de um design otimizado. Este processo inclui quatro etapas: definição do modelo paramétrico de construção, definição de suas propriedades materiais e construtivas, estimulação do conforto térmico e visual e da concentração de dióxido de carbono, otimização e escolha do melhor resultado. O modelo adaptativo PMV é usado para conforto térmico, a probabilidade de ofuscamento da luz do dia sem imagens é usada para conforto visual, uma concentração de CO2 é usada para avaliação de IAQ. A investigação revelou que o desempenho das opções introduzidas pelo algoritmo é mais adequado do que o protótipo de projecto. No entanto, os resultados mostram que as amostras são aceitáveis na concentração de dióxido de carbono. É necessário mais investigação para conforto térmico e visual. Dentre as variáveis estudadas sobre o desempenho do IEQ, a relação WWR da parede sul teve o impacto mais significativo. Com base nos resultados da otimização, o conforto térmico mudou na faixa de 10%, o conforto visual na faixa de 30% e a concentração de CO2 na faixa de 0,19%

Flying Animal Inspired Behavior-Based Gap-Aiming Autonomous Flight with a Small Unmanned Rotorcraft in a Restricted Maneuverability Environment

This dissertation research shows a small unmanned rotorcraft system with onboard processing and a vision sensor can produce autonomous, collision-free flight in a restricted maneuverability environment with no a priori knowledge by using a gap-aiming behavior inspired by flying animals. Current approaches to autonomous flight with small unmanned aerial systems (SUAS) concentrate on detecting and explicitly avoiding obstacles. In contrast, biology indicates that birds, bats, and insects do the opposite; they react to open spaces, or gaps in the environment, with a gap_aiming behavior. Using flying animals as inspiration a behavior-based robotics approach is taken to implement and test their observed gap-aiming behavior in three dimensions. Because biological studies were unclear whether the flying animals were reacting to the largest gap perceived, the closest gap perceived, or all of the gaps three approaches for the perceptual schema were explored in simulation: detect_closest_gap, detect_largest_gap, and detect_all_gaps. The result of these simulations was used in a proof-of-concept implementation on a 3DRobotics Solo quadrotor platform in an environment designed to represent the navigational diffi- culties found inside a restricted maneuverability environment. The motor schema is implemented with an artificial potential field to produce the action of aiming to the center of the gap. Through two sets of field trials totaling fifteen flights conducted with a small unmanned quadrotor, the gap-aiming behavior observed in flying animals is shown to produce repeatable autonomous, collision-free flight in a restricted maneuverability environment. Additionally, using the distance from the starting location to perceived gaps, the horizontal and vertical distance traveled, and the distance from the center of the gap during traversal the implementation of the gap selection approach performs as intended, the three-dimensional movement produced by the motor schema and the accuracy of the motor schema are shown, respectively. This gap-aiming behavior provides the robotics community with the first known implementation of autonomous, collision-free flight on a small unmanned quadrotor without explicit obstacle detection and avoidance as seen with current implementations. Additionally, the testing environment described by quantitative metrics provides a benchmark for autonomous SUAS flight testing in confined environments. Finally, the success of the autonomous collision-free flight implementation on a small unmanned rotorcraft and field tested in a restricted maneuverability environment could have important societal impact in both the public and private sectors

Intraspecific variation in invertebrate cognition: a review

A well-established field of research in vertebrates focuses on the variability of cognitive abilities within species. From mammals to fish, numerous studies have revealed remarkable differences in the cognitive phenotype among individuals, particularly in terms of sex or personality. However, many aspects of the mechanisms, genetics, and selective pressures that underlie individual cognitive variation remain unclear. Surprisingly, intraspecific variability in cognition has received much less attention in invertebrates, despite the increasing evidence of remarkable cognitive abilities in this group and the insights that could be gained from examining simultaneously two distinct taxa, namely vertebrates and invertebrates. In this review, we provide evidence that certain invertebrate species exhibit all the key features of cognitive variation observed in vertebrates, including differences related to sex and personality. In many cases, invertebrate studies have provided insights into the genetic basis, evolvability and response to selection of cognitive variability. Moreover, we highlight evidence for caste differences in eusocial insects, which are linked to task specialisation within the colony. This makes insect eusociality a valuable system for understanding how selection influences cognitive variation. We propose that cognitive variation in invertebrates may be more widespread than currently thought, and that selection may operate in a similar manner on two distantly related cognitive systems (vertebrates and invertebrates). Finally, we suggest that invertebrates hold the potential to serve both as alternative and complementary models to vertebrates, contributing to a deeper understanding of cognitive evolution

Bio-inspired network security for 5G-enabled IoT applications

Every IPv6-enabled device connected and communicating over the Internet forms the Internet of things (IoT) that is prevalent in society and is used in daily life. This IoT platform will quickly grow to be populated with billions or more objects by making every electrical appliance, car, and even items of furniture smart and connected. The 5th generation (5G) and beyond networks will further boost these IoT systems. The massive utilization of these systems over gigabits per second generates numerous issues. Owing to the huge complexity in large-scale deployment of IoT, data privacy and security are the most prominent challenges, especially for critical applications such as Industry 4.0, e-healthcare, and military. Threat agents persistently strive to find new vulnerabilities and exploit them. Therefore, including promising security measures to support the running systems, not to harm or collapse them, is essential. Nature-inspired algorithms have the capability to provide autonomous and sustainable defense and healing mechanisms. This paper first surveys the 5G network layer security for IoT applications and lists the network layer security vulnerabilities and requirements in wireless sensor networks, IoT, and 5G-enabled IoT. Second, a detailed literature review is conducted with the current network layer security methods and the bio-inspired techniques for IoT applications exchanging data packets over 5G. Finally, the bio-inspired algorithms are analyzed in the context of providing a secure network layer for IoT applications connected over 5G and beyond networks

Intrusion tolerant routing with data consensus in wireless sensor networks

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaWireless sensor networks (WSNs) are rapidly emerging and growing as an important new area in computing and wireless networking research. Applications of WSNs are numerous, growing, and ranging from small-scale indoor deployment scenarios in homes and buildings to large scale outdoor deployment settings in natural, industrial, military and embedded environments. In a WSN, the sensor nodes collect data to monitor physical conditions or to measure and pre-process physical phenomena, and forward that data to special computing nodes called Syncnodes or Base Stations (BSs). These nodes are eventually interconnected, as gateways, to other processing systems running applications. In large-scale settings, WSNs operate with a large number of sensors – from hundreds to thousands of sensor nodes – organised as ad-hoc multi-hop or mesh networks, working without human supervision. Sensor nodes are very limited in computation, storage, communication and energy resources. These limitations impose particular challenges in designing large scale reliable and secure WSN services and applications. However, as sensors are very limited in their resources they tend to be very cheap. Resilient solutions based on a large number of nodes with replicated capabilities, are possible approaches to address dependability concerns, namely reliability and security requirements and fault or intrusion tolerant network services. This thesis proposes, implements and tests an intrusion tolerant routing service for large-scale dependable WSNs. The service is based on a tree-structured multi-path routing algorithm, establishing multi-hop and multiple disjoint routes between sensors and a group of BSs. The BS nodes work as an overlay, processing intrusion tolerant data consensus over the routed data. In the proposed solution the multiple routes are discovered, selected and established by a self-organisation process. The solution allows the WSN nodes to collect and route data through multiple disjoint routes to the different BSs, with a preventive intrusion tolerance approach, while handling possible Byzantine attacks and failures in sensors and BS with a pro-active recovery strategy supported by intrusion and fault tolerant data-consensus algorithms, performed by the group of Base Stations