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State-of-the-Art Violence Detection Techniques: A review

Milon Biswas, Afjal Hossain Jibon, Mim Kabir, Khandokar Mohima, Rahman Sinthy, Md. Shamsul Islam, Monowara Siddique
2022 Asian Journal of Research in Computer Science  
These categories are based on the classification techniques used. These categories are: traditional violence detection using machine learning, Support Vector Machine (SVM) & Deep Learning.  ...  Moreover, dataset & video features that help in the recognition process are also discussed.  ...  The violent flow descriptors for each video are applied to different machine learning techniques to make a final decision about violent events. Different machine learning techniques are used here.  ... 
doi:10.9734/ajrcos/2022/v13i130305 fatcat:us54wnmflrho3nnuijn4ofzmme

Human Actions and Hand Gesture Recognition with Deep Learning

2019 International Journal of Engineering and Advanced Technology  
Hand gesture recognition is also discussed along with human activities using deep learning.  ...  We can also intimate the near ones about the status of the people. Also, it is a low-cost method and has high accuracy.  ...  While leveraging the capacity of deep learning algorithms, transfer learning is applied on full data from several users.  ... 
doi:10.35940/ijeat.b2815.129219 fatcat:h4el4sy3bzfhfenzv2x3fne7ny

DeepEye: A Surveillance System Using Deep Learning for Intruder Detection in SmartHome Remote App

2020 International Journal of Advanced Trends in Computer Science and Engineering  
The motivation of this project is to mitigates the weaknesses of existing solutions by incorporates learning-based face recognition techniques to enables real-time intruder detection on a cost-friendly  ...  The trained face recognition model provides a method for users to train a face recognition model on Raspberry Pi and ultimately, the ability to discriminate identity in real-time.  ...  Then, the video feeds will be analyzed with a deep neural network that is being hosted on GCP. Users will be notified immediately with a remote app, if suspicious events are detected.  ... 
doi:10.30534/ijatcse/2020/63952020 fatcat:vcqpoxplkng33psf3pid7csyrm

Distribution and Uncertainty in Complex Event Recognition [chapter]

Alexander Artikis, Matthias Weidlich
2015 Lecture Notes in Computer Science  
In particular, we focus on the questions of (1) how to distribute event recognition and (2) how to deal with the inherent uncertainty observed in many event recognition scenarios.  ...  In this paper, we reflect on some of these application areas to outline open research problems in event recognition.  ...  machine learning in event recognition.  ... 
doi:10.1007/978-3-319-21542-6_5 fatcat:uk7ysbbk4jc3lgucakobhbzh2q

Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward [article]

Ram Shankar Siva Kumar, Andrew Wicker, Matt Swann
2017 arXiv   pre-print
Operationalizing machine learning based security detections is extremely challenging, especially in a continuously evolving cloud environment.  ...  insights on how we have addressed them.  ...  ACKNOWLEDGMENTS We would like to thank Bryan Smith, Eugene Bobukh, Asghar Dehghani, Anisha Mazumder, Haijun Zhai, and Bin Xu for their valuable comments and members of Identity Driven Machine Learning  ... 
arXiv:1709.07095v1 fatcat:pit6mskwnncfjhaoqf5nmswna4

Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics [article]

Nadia Pocher, Mirko Zichichi, Fabio Merizzi, Muhammad Zohaib Shafiq, Stefano Ferretti
2022 arXiv   pre-print
In particular, we offer insights into the application to the Internet of Money (IoM) of machine learning, network and transaction graph analysis.  ...  Namely, we analyzed a real-world dataset of Bitcoin transactions represented as a directed graph network through various machine learning techniques.  ...  ., 2021) leverages random walks on a cryptocurrency graph to characterize distances to previous suspicious activity.  ... 
arXiv:2206.04803v1 fatcat:35ik44ftpzde5bmv5pmq6yo37e

A Machine Learning Approach for RDP-based Lateral Movement Detection

Tim Bai, Haibo Bian, Abbas Abou Daya, Mohammad A. Salahuddin, Noura Limam, Raouf Boutaba
2019 2019 IEEE 44th Conference on Local Computer Networks (LCN)  
In this thesis, we propose to detect evidence of LM using an anomaly-based approach that leverages Windows RDP event logs.  ...  We explore different feature sets extracted from these logs and evaluate various supervised and unsupervised machine learning (ML) techniques for classifying RDP sessions with high precision and recall  ...  I am highly grateful to him for giving me the opportunity to pursue research and work on a variety of topics. It was an honor for me to be his student.  ... 
doi:10.1109/lcn44214.2019.8990853 dblp:conf/lcn/BaiBDSLB19 fatcat:7mz5jp4p7rao7nc3vpl65zgq6e

Event processing under uncertainty

Alexander Artikis, Opher Etzion, Zohar Feldman, Fabiana Fournier
2012 Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems - DEBS '12  
In this tutorial we classify the different types of uncertainty found in event processing applications and discuss the implications on event representation and reasoning.  ...  Big data is recognized as one of the three technology trends at the leading edge a CEO cannot afford to overlook in 2012.  ...  Acknowledgments We have benefited from discussions with and the work of Anastasios Skarlatidis and Jason Filippou on AI-based event processing systems.  ... 
doi:10.1145/2335484.2335488 dblp:conf/debs/ArtikisEFF12 fatcat:6gnsppw4qnh77lfegd5ei4k5lm

Severity level assessment from semantically fused video content analysis for physical threat detection in ground segments of space systems

Antzoulatos Gerasimos, Orfanidis Georgios, Giannakeris Panagiotis, Tzanetis Giorgos, Kampilis-Stathopoulos Grigorios, Kopalidis Nikolaos, Gialampoukidis Ilias, Vrochidis Stefanos, Kompatsiaris Ioannis
2021 Zenodo  
Advanced machine learning techniques provide analysis of events and useful information, which are fused semantically and estimate the severity level of the potential attack, serving the needs for real-time  ...  In this landscape, the aim of this work focuses on proposing a framework that enables the identification of potential human-made threats, created by the usage of natural means and captured by heterogeneous  ...  Machine learning advances are the core aspect of our approach as innovative deep-learning methodologies analyse multimedia content from videos, aiming to detect malicious objects and suspicious activities  ... 
doi:10.5281/zenodo.5567019 fatcat:fkvq3pgk3ndchgyftjrt3incui

Characterizing Network Intrusion Prevention System

Deris Stiawan, Abdul Hanan Abdullah, Mohd. Yazid Idris
2011 International Journal of Computer Applications  
On contrary, IPS can be used to alarm for attacks within a network and provide for acting on attack preventive with Firewall and IDS function mechanism.  ...  An IPS can be defined as an in-line product that focuses on identifying and blocking malicious network activity in real time [4] .  ...  On the contrary, FP and FN are the events that undermine the detection performance when unknown or suspicious is not identify.  ... 
doi:10.5120/1811-2439 fatcat:pirx3fj76faibm3anbzmhdkx6e

A Deep Belief Network Based Machine Learning System for Risky Host Detection [article]

Wangyan Feng, Shuning Wu, Xiaodan Li, Kevin Kunkle
2017 arXiv   pre-print
While most previous research focused on network intrusion detection, we focus on risk detection and propose an intelligent Deep Belief Network machine learning system.  ...  Results on real enterprise data indicate that the deep belief network machine learning system performs better than other algorithms for our problem and is six times more effective than current rule-based  ...  We also thank our colleague, Ningwei Liu, for his help on data engineering and model refinement.  ... 
arXiv:1801.00025v1 fatcat:46ymwqijs5aldanfx6wjxqool4

The Safety Transformation in the Future Internet Domain [chapter]

Roberto Gimenez, Diego Fuentes, Emilio Martin, Diego Gimenez, Judith Pertejo, Sofia Tsekeridou, Roberto Gavazzi, Mario Carabaño, Sofia Virgos
2012 Lecture Notes in Computer Science  
Other methods achieve event recognition by relying on both low-level motion detection and tracking, and high level recognition of predefined (threat) scenarios corresponding to specific behaviors.  ...  Safety applications will leverage the IoT platforms described previously; in particular the capillary network hot spots will be very important points for installation on the territory of safety oriented  ... 
doi:10.1007/978-3-642-30241-1_17 fatcat:qsg7ebknyzcuxdlbn4u2tsw4dm

A Low-Cost Attack against the hCaptcha System [article]

Md Imran Hossen, Xiali Hei
2021 arXiv   pre-print
CAPTCHAs are a defense mechanism to prevent malicious bot programs from abusing websites on the Internet. hCaptcha is a relatively new but emerging image CAPTCHA service.  ...  We evaluate our system against 270 hCaptcha challenges from live websites and demonstrate that it can solve them with 95.93% accuracy while taking only 18.76 seconds on average to crack a challenge.  ...  Interesting, data from all of these categories are already available on the OpenImages [25] dataset, a publicly accessible dataset for training machine learning models on various image recognition tasks  ... 
arXiv:2104.04683v1 fatcat:5sdd4vlmjnhuhj6yudl6uenfny

Methodology for the Automated Metadata-Based Classification of Incriminating Digital Forensic Artefacts

Xiaoyu Du, Mark Scanlon
2019 Proceedings of the 14th International Conference on Availability, Reliability and Security - ARES '19  
A supervised machine learning approach is employed, which leverages the recorded results of previously processed cases.  ...  Usually, most of the file artefacts on seized devices are not pertinent to the investigation.  ...  This research presents an approach that enables automatic determination of suspicious file artefacts by training machine learning models.  ... 
doi:10.1145/3339252.3340517 dblp:conf/IEEEares/DuS19 fatcat:dqmvorgilrhcrhgcitpsvfkiee

Artificial Immune Ecosystems: the role of expert-based learning in artificial cognition

Pierre Parrend, Fabio Guigou, Julio Navarro, Aline Deruyver, Pierre Collet
2018 Journal of Robotics, Networking and Artificial Life (JRNAL)  
Artificial immune ecosystems support a comprehensive model for anomaly detection and characterization, but their cognitive capacity are limited by the state of the art in machine learning and the rapid  ...  The rapid evolution of IT ecosystems significantly challenges the security models our infrastructures rely on.  ...  Based on a list of suspicious traces, the expert thus identifies the actual threat and confirms that a given event sequence builds a single attack.  ... 
doi:10.2991/jrnal.2018.4.4.10 fatcat:zi2bohitknhm3fp62vdskwqqae
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