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Malware Squid: A Novel IoT Malware Traffic Analysis Framework Using Convolutional Neural Network and Binary Visualisation [chapter]

Robert Shire, Stavros Shiaeles, Keltoum Bendiab, Bogdan Ghita, Nicholas Kolokotronis
2019 Lecture Notes in Computer Science  
In this paper, we propose a new method to detect and diagnose variations in overall Facebook user psychology through Open Source Intelligence (OSINT) and machine learning techniques.  ...  We are aggregating the spectrum of user sentiments and views by using N-Games charts, which exhibit noticeable variations over time, validated through long term collection.  ...  In this paper, we present a novel IoT malware traffic analysis method that addresses this issue by using a TensorFlow convolutional neural network paired with a binary visualization technique.  ... 
doi:10.1007/978-3-030-30859-9_6 fatcat:rbxwau3acrenfofvhh53srauqy

Machine Learning Techniques for Malware Detection

Harsha A K, Thyagaraja Murthy A
2021 International Journal of Scientific Research in Science Engineering and Technology  
Because of the growing use of encryption and other evasion measures, traditional content-based network traffic categorization is becoming more challenging.  ...  In this paper, we provide a malware classification technique that uses packet information and machine learning algorithms to detect malware.  ...  [10] described a method for detecting user activities by analysing an Android device's encrypted network.  ... 
doi:10.32628/ijsrset21858 fatcat:6hlog6gjm5fnbger75duywlpjm

Malware threat analysis techniques and approaches for IoT applications: a review

Chimeleze Collins Uchenna, Norziana Jamil, Roslan Ismail, Lam Kwok Yan, Mohamad Afendee Mohamed
2021 Bulletin of Electrical Engineering and Informatics  
However, with the sophistication of technology has resulted in IoT applications facing with malware threat.  ...  In this paper, we studied extensively the adoption of static, dynamic and hybrid malware analyses in proffering solution to the security problems plaguing different IoT applications.  ...  [47] proposed a framework for malware detection that can endure encrypted HTTPS and non-encrypted HTTP traffic in home networks, bring-your-owndevice (BYOD) enterprise networks, and 3G/4G mobile networks  ... 
doi:10.11591/eei.v10i3.2423 fatcat:tmkgezmv5ngcblgcxr6bmbqd3q

Ransomware Protection Tool based on Recurrent Neural Network (RNN)

Nandhini S
2020 International Journal for Research in Applied Science and Engineering Technology  
According to Bitdefender "Ransomware is a form of malicious software (or malware) that, once it's taken over your computer, threatens you with harm, usually by denying you access to your data.  ...  We extant specific recurrent neural networks for catching resident happening designs in ransomware orders using the idea of attention mechanisms.  ...  Mustafa Kaiili [4] proposes a system analysis in which the network traffic of Locky ransomware to extract a number of informative network features.  ... 
doi:10.22214/ijraset.2020.5325 fatcat:qbesh23tqvhg7lz2ya3fjtxwm4

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

Mohammad Alauthaman, Nauman Aslam, Li Zhang, Rafe Alasem, M. A. Hossain
2016 Neural computing & applications (Print)  
The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets.  ...  This work presents a method of P2P Bot detection based on an adaptive multilayer feedforward neural network in cooperation with decision trees.  ...  With this characteristic, our detection approach will not be affected by traffic encryption.  ... 
doi:10.1007/s00521-016-2564-5 pmid:29769759 pmcid:PMC5940715 fatcat:copi67qk6ne55jswz3yhdgliii

Machine Learning for Traffic Analysis: A Review

Nour Alqudah, Qussai Yaseen
2020 Procedia Computer Science  
Increased network traffic and the development of artificial intelligence require new ways to detect intrusions, analyze malware behavior, and categorize Internet traffic and other security aspects.  ...  Increased network traffic and the development of artificial intelligence require new ways to detect intrusions, analyze malware behavior, and categorize Internet traffic and other security aspects.  ...  Intrusion detection analysis (ID) search for attacks by monitoring the activities of network malware.  ... 
doi:10.1016/j.procs.2020.03.111 fatcat:oc7zr2jj6zfldkoa5hyvsv6tlm

Encrypted and Covert DNS Queries for Botnets: Challenges and Countermeasures

Constantinos Patsakis, Fran Casino, Vasilios Katos
2019 Computers & security  
Current state of the art and practice considers that the DNS queries performed by a compromised device are transparent to the network administrator and therefore can be monitored, analysed, and blocked  ...  In this work, we showcase that the latter is a strong assumption as malware could efficiently hide its DNS queries using covert and/or encrypted channels bypassing the detection mechanisms.  ...  Responsibility for the information and views expressed therein lies entirely with the authors.  ... 
doi:10.1016/j.cose.2019.101614 fatcat:e4dxvp2hbrclla4fxtdvhx5yni

Predicting the Dynamic Behaviour of Malware using RNN

2020 International Journal of Engineering and Advanced Technology  
Malware analysis can be classified as static and dynamic analysis. Static analysis involves the inspection of the malicious code by observing the features such as file signatures, strings etc.  ...  Recurrent Neural Networks are capable of predicting whether an executable is malicious and have the ability to capture time-series data.  ...  The zero-day malware cannot be detected this way if it does not share any code with any malware previously detected.  ... 
doi:10.35940/ijeat.c6291.029320 fatcat:n3bithgfvbfwzhm4dplioddcre

The rise of machine learning for detection and classification of malware: Research developments, trends and challenges

Daniel Gibert, Carles Mateu, Jordi Planes
2020 Journal of Network and Computer Applications  
Current state-of-the-art research focus on the development and application of machine learning techniques for malware detection due to its ability to keep pace with malware evolution.  ...  The survey helps researchers to have an understanding of the malware detection field and of the new developments and directions of research explored by the scientific community to tackle the problem.  ...  Acknowledgements This research has been partially funded by the Spanish MICINN Projects TIN2015-71799-C2-2-P, ENE2015-64117-C5-1-R, and is supported by the University of Lleida.  ... 
doi:10.1016/j.jnca.2019.102526 fatcat:3bf6afjqpnb53eoeghfxjeaus4

A Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques

Damien Warren Fernando, Nikos Komninos, Thomas Chen
2020 IoT  
Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them.  ...  These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution.  ...  Deep Learning Detection Studies Deep Neural Networks In this study, the authors use a network-based approach and use a combination of network monitoring with a deep neural network to detect ransomware  ... 
doi:10.3390/iot1020030 fatcat:3kx6wf352zdvtdmsxcumqwsuqq

Technical Analysis of Thanos Ransomware

Ikuromor Ogriki, Christopher Beck, Vahid Heydari
2022 International Conference on Cyber Warfare and Security (ICIW)  
It was founded in 2020 and is building up to be the leading malware used by low-to-medium-level attackers.  ...  Therefore, new malware can detect antivirus and intrusion detection systems and evade them or manifest in ways to make themselves undetectable.  ...  ), neural networks (backpropagation, radial basis functions), and Random Forest.  ... 
doi:10.34190/iccws.17.1.62 fatcat:pdxbgustgvglfojk7462hk2hrq

The Next Generation Cognitive Security Operations Center: Adaptive Analytic Lambda Architecture for Efficient Defense against Adversarial Attacks

Konstantinos Demertzis, Nikos Tziritas, Panayiotis Kikiras, Salvador Llopis Sanchez, Lazaros Iliadis
2019 Big Data and Cognitive Computing  
Specifically, it uses an Extreme Learning Machine neural network with Gaussian Radial Basis Function kernel (ELM/GRBFk) for the batch data analysis and a Self-Adjusting Memory k-Nearest Neighbors classifier  ...  The SOC staff works closely with incident response teams, security analysts, network engineers and organization managers using sophisticated data processing technologies such as security analytics, threat  ...  Acknowledgments: Nikos Tziritas's work was partly supported by the PIFI International Scholarship, Y75601. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/bdcc3010006 fatcat:qskf3u5xkfephh5tcis3ibo35i

Intrusion Detection and Prevention in Cloud, Fog, and Internet of Things

Xuyun Zhang, Yuan Yuan, Zhili Zhou, Shancang Li, Lianyong Qi, Deepak Puthal
2019 Security and Communication Networks  
With a wide spectrum, the detection and prevention systems vary from antivirus software to hierarchical systems monitoring the traffic of an entire backbone networks.  ...  Intrusion detection and prevention systems that monitor the devices, networks, and systems for malicious activities and policy violations are one of the key countermeasures against cybersecurity attacks  ...  analyse online traffic, respectively.  ... 
doi:10.1155/2019/4529757 fatcat:ehi3or2qtbdtzluyvgr5libxpe

A Review of Computer Vision Methods in Network Security [article]

Jiawei Zhao, Rahat Masood, Suranga Seneviratne
2020 arXiv   pre-print
In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection.  ...  On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks.  ...  Similar to phishing and malware detection, neural networkbased methods in anomaly detection also generates images from network traffic and then fed into a deep neural network.  ... 
arXiv:2005.03318v1 fatcat:pcng7535obec3l6fejkllbi3ii

Detecting Malicious DNS Queries Over Encrypted Tunnels Using Statistical Analysis and Bi-Directional Recurrent Neural Networks

Mohammad Al-Fawa'reh, Zain Ashi, Mousa Tayseer Jafar
2021 Karbala International Journal of Modern Science  
They obtained 25 types of malwares intrusions by monitoring network traffic, preserving represented by 9342 greyscale images for their dataset.  ...  These protocols neural networks - such as Convolutional Neural have made monitoring the network traffic more com- Network (CNN) and Recurrent Neural Network (RNN)  ... 
doi:10.33640/2405-609x.3155 fatcat:shd27ytlvndirjdkrrykgjejou
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