This paper introduces a novel technique of computational art with mandala-an iconic heritage of Indian folk art. Its novelty lies in several fundamental steps. The first one is fixing the asymmetries and the imperfect...
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Fog and Edge Computing is still a relatively new field, being less than a decade old. As it is under rapid development, it is of utmost importance that the tools and software used for this new field is better applied ...
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Traffic classification is an automated technique that divides computer network traffic into several categories depending on different factors like protocol or port number. In a complicated context, traffic categorizat...
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ISBN:
(纸本)9798350336009
Traffic classification is an automated technique that divides computer network traffic into several categories depending on different factors like protocol or port number. In a complicated context, traffic categorization is an important tool for network and system security. A monitoring system called intrusion detection looks for abnormal activity and sends out notifications. In order to safeguard a system from network-based attacks, Network Intrusion Detection Systems (NIDS) play a crucial role in monitoring and analyzing network traffic. Active and passive intrusion detection systems (IDS), network intrusion detection systems (NIDS), host intrusion detection systems (HIDS), knowledge-based (signature-based) IDS, and behaviorbased (anomaly-based) IDS are some of the numerous types of intrusion detection systems (IDS). Passive IDS is just designed to monitor and analyze network traffic behaviour and notify an operator of potential vulnerabilities and attacks, whereas Active IDS is also known as Intrusion Detection and Prevention System. A network's malicious traffic is identified using a network-based intrusion detection system (NIDS). A host-based IDS monitors system activity and seeks for indications of abnormal behaviour. For networks with unidentified traffic, the intrusion detection system designed using flow and payload statistical characteristics and clustering approach needs additional clusters. The present intrusion detection system however is affected by false alarm rate, poor detection rate, imbalanced datasets and response time which lead to misclassification of intrusions in various scenarios. Hence, there is a requirement for developing an automated intrusion detection system that works well in different scenarios. The proposed system uses supervised and unsupervised intrusion detection and classification methods to increase the classification accuracy. To categorize the intrusions, dimensionality reduction strategies are used in conjunction with the c
Despite popular belief, agricultural research today is more based on hard evidence;exact;precise, and rigorous than ever before. Almost every industry has been disrupted by the spread of IoT-based technologies, includ...
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Currently, there exist effective strategies for providing practical recommendations crucial in many industries, this encompasses a range of online platforms, such as electronic commerce, social networking sites, and v...
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Cognitive states are crucial for understanding the complex functioning of the human brain, which determine our perceptions and interactions with the surrounding environment. These states include different mental proce...
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Named Entity Recognition (NER) is a commonly followed standard approach in natural language processing for recognizing category of the textual term such as noun, pronoun or any other pre-defined class. Consequently, t...
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Exchange of data in networks necessitates provision of security and *** networks compromised by intruders are those where the exchange of data is at high *** main objective of this paper is to present a solution for s...
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Exchange of data in networks necessitates provision of security and *** networks compromised by intruders are those where the exchange of data is at high *** main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed *** activities are monitored and detected by the Intrusion Detection System(IDS)that operates with nodes connected to a distributed *** IDS operates in two phases,where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest,Convolutional neural network,and XGBoost along with genetic algorithm to improve the performance of *** novel attacks detected in this phase are converted into signatures and exchanged further through the network using the blockchain framework in the second *** phase uses the cryptosystem as part of the blockchain to store data and secure it at a higher *** blockchain is implemented using the Hyperledger Fabric v1.0 and v2.0,to create a prototype for secure signature *** exchanges signatures in a much more secured manner using the blockchain architecture when implemented with version 2.0 of Hyperl-edger *** performance of the proposed blockchain system is evaluated on UNSW NB15 *** performance has been evaluated in terms of execution time,average latency,throughput and transaction processing *** evidence of the proposed IDS system demonstrates improved performance with accuracy,detection rate and false alarm rate(FAR)as key parameters *** and detection rate increase by 2%and 3%respectively whereas FAR reduces by 1.7%.
To meet the growing computational needs of machine learning, cloud services have provided a cost-effective alternative to fulfil the memory intensive task requirements. Cloud providers offer many benefits including mo...
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This work presents an innovative system/app designed to assist individuals with visual or hearing impairments in communicating with the outside world using American Sign Language (ASL). By leveraging machine learning ...
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