Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network

Demertzis, Konstantinos; Iliadis, Lazaros; Bougoudis, Ilias

doi:10.1007/s00521-019-04363-x

Cited by 47 publications

(21 citation statements)

References 28 publications

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“…Flow data is usually large in size, difficult to be processed in real-time, and when processed, it is either destroyed or archived, and is very difficult to be recovered again, because the system's memory is typically very small. The analysis, monitoring, and categorization of the Internet network traffic [2] is one of the most important tasks and characterised as a specialised solution and a valuable tool that can be used not only to effectively deal with the design, management, and monitoring of the critical infrastructure of the system, but also for the monitoring of attacks and the study of cybercrime [3].…”

Section: Introductionmentioning

confidence: 99%

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

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Section: Introductionmentioning

confidence: 99%

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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“…In the FWCN, the competition is used in every training process of the activation function to find the transformation of normal code to a malicious file. In the implementation, three nets, including Maxnet, Mexican hat, and Hamming net, are exploited to handle the malicious activity against the normal file and malicious file with the fixed weight to find malware present in the unknown file [13][14][15][16]. One of the competition-based networks with fixed weight is Maxnet.…”

Section: Delineation Of Unsupervised Fixed Weight Competitive Net Learning Used To Analysis the Unknown File Instruction Setsmentioning

confidence: 99%

Prevention of Runtime Malware Injection Attack in Cloud Using Unsupervised Learning

Prabhavathy¹,

Umamaheswari²

2022

Intelligent Automation &Amp; Soft Computing

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Cloud computing utilizes various Internet-based technologies to enhance the Internet user experience. Cloud systems are on the rise, as this technology has completely revolutionized the digital industry. Currently, many users rely on cloud-based solutions to acquire business information and knowledge. As a result, cloud computing services such as SaaS and PaaS store a warehouse of sensitive and valuable information, which has turned the cloud systems into the obvious target for many malware creators and hackers. These malicious attackers attempt to gain illegal access to a myriad of valuable information such as user personal information, password, credit/debit card numbers, etc., from systems as the unsecured e-learning ones. As an important part of cloud services, security is needed to protect business customers and users from unauthorized threats. This paper aims to identify malware that attacks cloud-based software solutions using an unsupervised learning model with fixed-weight Hamming and Mexiannet. Different types of attack methodologies and various ways of malicious instructions targeting unknown files in cloud services are investigated. The result and analysis in this study provide an evolution of the unsupervised learning detection algorithm with an accuracy of 94.05%.

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“…As it happens with the biological neurons, when the dynamics of their cell membrane reaches a particular value, which is called action potential, then the neuron triggers and produces a signal that travels to other neurons, which, in turn, increase or decrease the dynamics of their cell membrane according to this particular signal. The SNNs use peak sequences as mechanisms of internal information presentation, in contrast to the usual continuous variables, while at the same time having equal, if not better, performance in computational cost to the traditional NNs [ 79 , 80 , 81 ].…”

Section: Architecturesmentioning

confidence: 99%

A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and Activations Functions

Demertzis

Papadopoulos

Iliadis

et al. 2022

Sensors

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In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective, and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of neural networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization was realized along with the study of special issues such as optical activation functions and cost functions. The main contribution of this research work is that it is the first time in the literature that the most well-known architectures, training methods, optimization and activations functions of the nanophotonic networks are presented in a single paper. This study also includes an extensive detailed meta-review analysis of the advantages and disadvantages of nanophotonic networks.

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Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network

Cited by 47 publications

References 28 publications

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Prevention of Runtime Malware Injection Attack in Cloud Using Unsupervised Learning

A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and Activations Functions

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