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Darknet traffic big-data analysis and network management to real-time automating the malicious intent detection process by a weight agnostic neural networks framework

arXiv

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arXiv 2021年

作者： Demertzis, Konstantinos Tsiknas, Konstantinos Takezis, Dimitrios Skianis, Charalabos Iliadis, Lazaros Laboratory of Complex Systems Department of Physics Faculty of Sciences International Hellenic University Kavala Campus St. Loukas65404 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Vas. Sofias 12 Xanthi67100 Greece Hellenic National Defence General Staff Stratopedo Papagou Mesogeion 227-231 Athens15561 Greece University of Aegean Karlovassi Samos83200 Greece School of Engineering Department of Civil Engineering Faculty of Mathematics Programming and General courses Democritus University of Thrace Kimmeria Xanthi Greece

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 Weight Agnostic Neural Networks (WANNs) methodology, we propose an automated searching neural net architectures strategy that can perform various tasks such as identify 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. Copyright © 2021, The Authors. All rights reserved.

关键词： Network management

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A flexible framework for web-based virtual reality presentation of cultural heritage

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AIP Conference Proceedings 2021年第1期2333卷

作者： Dimo Chotrov Angel Bachvarov 1Technical University Sofia Faculty of Computer Systems and Technologies Department Programming and Computer Technologies Bulgaria 2Technical University Sofia Faculty of Mechanical Engineering Department Automation of Discrete Production Engineering Bulgaria

The Virtual Reality (VR) experiences are great for an engaging presentation creating wide public awareness of cultural heritage, especially if the experience is built on a web-based technology. One of the still posing challenges is that once an application has been developed, for example for a virtual museum or a virtual tour, it usually remains fixed to an embedded in the application model. Adding new functionalities or interaction paradigms needs additional development and new deployment. In this paper, we propose a framework, which achieves the decoupling of an experience from the used models and interaction paradigms through scene and input templates and configuration files. This allows an easy way for generation of a variety of experiences even by people with no or limited programming skills.

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Automatic Detection of Smishing Attacks by Machine Learning Methods

Automatic Detection of Smishing Attacks by Machine Learning ...

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International Informatics and Software engineering Conference (UBMYK)

作者： Caner Balim Efnan Sora Gunal Dept. of Computer Programming Afyon Kocatepe University Afyonkarahisar Turkiye Dept. of Computer Engineering Eskisehir Osmangazi University Eskisehir Turkiye

ISBN: (纸本)9781728139937

Phishing is a type of cyber-attack aiming to steal people's confidential information using a bait. Smishing is a form of phishing that is carried out via SMS messages sent to mobile phones. Attackers intend to steal the private information of their victims through the content they send in their SMS messages. Therefore, the detection of smishing messages has a vital role in information security. In this work, a machine learning-based model is proposed to detect smishing messages. Experiments conducted on a relatively large dataset, which contains legitimate and smishing messages, have shown that the proposed model offers a promising detection performance.

关键词： Phishing Smishing Text mining Machine learning Information security computer information security phishing text mining SMS message Confidential Information automatic detection Artificial Intelligence Machine Learning Messages

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A botnet detection approach based on the clonal selection algorithm 9

A botnet detection approach based on the clonal selection al...

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9th IEEE International Conference on Dependable Systems, Services and Technologies, DESSERT 2018

作者： Lysenko, Sergii Bobrovnikova, Kira Savenko, Oleg Department of Computer Engineering and System Programming Khmelnitsky National University Instytutska 11 Khmelnitsky Ukraine

ISBN: (纸本)9781538659038

The paper presents a new technique for the botnets' detection in the corporate area networks. It is based on the usage of the algorithms of the artificial immune systems. Proposed approach is able to distinguish benign network traffic from malicious one using the clonal selection algorithm taking into account the features of the botnet's presence in the network. An approach present the main improvements of the BotGRABBER system. It is able to detect the IRC, HTTP, DNS and P2P botnets. © 2018 IEEE.

关键词： Botnet

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DDoS botnet detection technique based on the use of the semi-supervised fuzzy C-means clustering 14

DDoS botnet detection technique based on the use of the semi...

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14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, ICTERI 2018

作者： Lysenko, Sergii Savenko, Oleg Bobrovnikova, Kira Department of Computer Engineering System Programming Khmelnitsky National University Instytutska 11 Khmelnitsky Ukraine

A new technique for the DDoS botnet detection based on the botnets network features analysis is proposed. It uses the semi-supervised fuzzy c-means clustering. The proposed approach includes the learning and the detection stages. Analysis is based on the extracted from the network traffic features that may indicate the presence of the DDoS botnets' presence in the network. Experimental results demonstrated that the proposed technique ensures the DDoS botnet detection at the rate at about 95%. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Botnet

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Cyber-Typhon: An Online Multi-task Anomaly Detection Framework 15th

Cyber-Typhon: An Online Multi-task Anomaly Detection Framewo...

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15th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Demertzis, Konstantinos Iliadis, Lazaros Kikiras, Panayiotis Tziritas, Nikos School of Engineering Department of Civil Engineering Faculty of Mathematics Programming and General Courses Democritus University of Thrace Kimmeria Xanthi Greece School of Science Department of Computer Science University of Thessaly Lamia Greece Research Center for Cloud Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783030198220

According to the Greek mythology, Typhon was a gigantic monster with one hundred dragon heads, bigger than all mountains. His open hands were extending from East to West, his head could reach the sky and flames were coming out of his mouth. His body below the waste consisted of curled snakes. This research effort introduces the "Cyber-Typhon" (CYTY) an Online Multi-Task Anomaly Detection Framework. It aims to fully upgrade old passive infrastructure through an intelligent mechanism, using advanced Computational Intelligence (COIN) algorithms. More specifically, it proposes an intelligent Multi-Task Learning framework, which combines On-Line Sequential Extreme Learning Machines (OS-ELM) and Restricted Boltzmann Machines (RBMs) in order to control data flows. The final target of this model is the intelligent classification of Critical Infrastructures’ network flow, resulting in Anomaly Detection due to Advanced Persistent Threat (APT) attacks. © 2019, IFIP International Federation for Information Processing.

关键词： Anomaly detection

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Using the Model of a Functional Rationalizer of Consumer Behavior in Recommendation Systems for Managing the Transport Activity of the Urban Population 23

Using the Model of a Functional Rationalizer of Consumer Beh...

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23rd International Conference on Soft Computing and Measurements, SCM 2020

作者： Seliverstov, Yaroslav A. Seliverstov, Svytoslav A. Podoprigora, Nikolay V. Starichenkov, Alexey L. Naryshkin, Roman S. Solomenko Institute of Transport Problems Russian Academy of Sciences Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University Laboratory of Intelligent Transport Systems St. Petersburg Russia Saint Petersburg State University of Architecture and Civil Engineering Department of Ground Transportation Technological Machines St. Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Ship Control Systems St. Petersburg Russia Saint Petersburg State University Department of Programming Technology St. Petersburg Russia

ISBN: (纸本)9781728196923

An overview of existing urban software mobile applications of the transport and economic direction is given. A model of a functional rationalizer of consumer behavior is being built. The software model of the functional rationalizer of consumer behavior (SM FRCB) was developed for the first time. PMFRCB allows the user to expand the number of his preferred needs (occupations, activities) during the daily cycle through intelligent recommendations. Also, SM FRCB allows you to optimize needs (activities, activities) at the cost and rate of their use. The SM FRCB is made in Python_3 using the libraries matplotlib, collections, numpy, pyplot, mplot3d. The analysis of consumer behavior before and after using the functional rationalizer model is carried out. The graphical results of a structural analysis of the costs of old and new consumer behavior, analysis of consumer behavior within the boundaries of the theory of possibilities and the model of transport behavior of a resident before and after applying the functional rationalizer model are presented. The prospects of further improving the SM FRCB are considered. © 2020 IEEE.

关键词： Smart city

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A Model of Deep Neural Network for Iris Classification With Different Activation Functions

A Model of Deep Neural Network for Iris Classification With ...

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2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018

作者： Eldem, Ayşe Eldem, Hüseyin Üstün, Deniz Department of Computer Engineering Karamanoǧlu Mehmetbey University Karaman Turkey Department of Technology and Computer Programming Karamanoǧlu Mehmetbey University Karaman Turkey

ISBN: (纸本)9781538668788

In recent years, deep neural network (DNN) has been frequently used for classification. In this study, iris flowers having 3 different types are classified by using DNN which are utilized the width and length of petal and sepal features as input. Some experiments are made for the iris dataset by using different activation functions and different epoch numbers. Then, the activation function which gives the best result is determined. The classification success of the developed model is achieved 96% for the iris dataset. © 2018 IEEE.

关键词： Deep neural networks

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Monitoring System for Prediction and Detection of Epilepsy Seizure

Monitoring System for Prediction and Detection of Epilepsy S...

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International Conference on Advances in Computational Tools for engineering Applications, ACTEA

作者： Nashaat el Halabi Roy Abi Zeid Daou Roger Achkar Ali Hayek Josef Börcsök Department of Computer and Communications Engineering American University of Science and Technology Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon Department of Computer Architecture and System Programming University of Kassel Germany

Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.

关键词： Electroencephalography Epilepsy Feature extraction Tools Monitoring Artificial neural networks Support vector machines

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Piquasso: A Photonic Quantum computer Simulation Software Platform

arXiv

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arXiv 2024年

作者： Kolarovszki, Zoltán Rybotycki, Tomasz Rakyta, Péter Kaposi, Ágoston Poór, Boldizsár Jóczik, Szabolcs Nagy, Dániel T.R. Varga, Henrik El-Safty, Kareem H. Morse, Gregory Oszmaniec, Michal Kozsik, Tamás Zimborás, Zoltán Quantum Computing and Quantum Information Research Group HUN-REN Wigner Research Centre for Physics Konkoly-Thege Miklós út 29-33 BudapestH-1525 Hungary Department of Programming Languages and Compilers Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Center for Theoretical Physics Polish Academy of Sciences Al. Lotników 32/46 Warszawa02-668 Poland Department of Physics of Complex Systems Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Quantinuum 17 Beaumont Street OxfordOX1 2NA United Kingdom Robert Bosch Kft. Gyömrői út 104. BudapestH-1103 Hungary Department of Computer Engineering Technical University of Munich Arcisstraße 21 München80333 Germany Algorithmiq Ltd. Kanavakatu 3C Helsinki00160 Finland

We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performance C++ backends, Piquasso provides state-of-the-art performance in the simulation of photonic quantum computers. The Piquasso framework is supported by an intuitive web-based graphical user interface where the users can design quantum circuits, run computations, and visualize the results. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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