tLab: A System Enabling Malware Clustering Based on Suspicious Activity Trees

2017 ◽  
pp. 195-210 ◽  
Author(s):  
Anton Kopeikin ◽  
Arnur Tokhtabayev ◽  
Nurlan Tashatov ◽  
Dina Satybaldina
Keyword(s):  
Suspicious Activity

scholarly journals Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Tanzila Saba ◽  
Amjad Rehman ◽  
Rabia Latif ◽  
Suliman Mohamed Fati ◽  
Mudassar Raza ◽  
...  
Keyword(s):  
Activity Recognition ◽  
System Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Suspicious Activity

Suspicious activity detection using deep learning in secure assisted living IoT environments

2020 ◽  
Author(s):  
G. Vallathan ◽  
A. John ◽  
Chandrasegar Thirumalai ◽  
SenthilKumar Mohan ◽  
Gautam Srivastava ◽  
...  
Keyword(s):  
Deep Learning ◽  
Assisted Living ◽  
Activity Detection ◽  
Suspicious Activity

Video representation and suspicious event detection using semantic technologies

Semantic Web ◽  
2020 ◽  
pp. 1-25
Author(s):  
Ashish Singh Patel ◽  
Giovanni Merlino ◽  
Dario Bruneo ◽  
Antonio Puliafito ◽  
O.P. Vyas ◽  
...  
Keyword(s):  
Event Detection ◽  
Video Data ◽  
Machine Learning Techniques ◽  
Novel Approach ◽  
Learning Techniques ◽  
Video Scene ◽  
Scene Representation ◽  
Level Information ◽  
Complex Events ◽  
Suspicious Activity

Storage and analysis of video surveillance data is a significant challenge, requiring video interpretation and event detection in the relevant context. To perform this task, the low-level features including shape, texture, and color information are extracted and represented in symbolic forms. In this work, a methodology is proposed, which extracts the salient features and properties using machine learning techniques and represent this information as Linked Data using a domain ontology that is explicitly tailored for detection of certain activities. An ontology is also developed to include concepts and properties which may be applicable in the domain of surveillance and its applications. The proposed approach is validated with actual implementation and is thus evaluated by recognizing suspicious activity in an open parking space. The suspicious activity detection is formalized through inference rules and SPARQL queries. Eventually, Semantic Web Technology has proven to be a remarkable toolchain to interpret videos, thus opening novel possibilities for video scene representation, and detection of complex events, without any human involvement. The proposed novel approach can thus have representation of frame-level information of a video in structured representation and perform event detection while reducing storage and enhancing semantically-aided retrieval of video data.

scholarly journals A First Line of Defence? Vigilant surveillance, participatory policing and the reporting of ‘suspicious’ activity

2017 ◽  
Vol 15 (1) ◽  
pp. 94-107 ◽  
Author(s):  
Sebastian Larsson
Keyword(s):  
Everyday Life ◽  
Political Campaigns ◽  
The State ◽  
First Line ◽  
Current Role ◽  
Security Practices ◽  
Terrorist Acts ◽  
Suspicious Activity ◽  
As If

What is at stake when citizens are encouraged to deploy vigilant surveillance and report what they consider to be unusual and “suspicious” activity? This article explores the current role of vigilance in contemporary Western security practices aimed at battling terrorist acts and major crime. It does so by critically analysing official constructions of suspiciousness, the responsibilisation process of participatory policing, and the assignments of prejudiced amateur detectives. It concludes, firstly, that the agency offered by political campaigns such as “If You See Something, Say Something” is highly illusive since the act of reporting simply demarcates where participation ends, and where fear and paranoia are turned into legitimate intelligence, enabling the state to exercise authoritative action and preemptive violence. Secondly, these kinds of vigilance initiatives also nurture a normalisation of suspicion towards strangers since the encouragements to be aware of anything-and-anyone deemed “out of the ordinary”, as well as the tools for reporting such suspicions, increasingly creep into the mundane realms of everyday life.

Anti-money laundering and moral intensity in suspicious activity reporting

2018 ◽  
Vol 21 (4) ◽  
pp. 520-533
Author(s):  
Brett Coombs-Goodfellow ◽  
Mark Eshwar Lokanan
Keyword(s):  
Decision Making ◽  
Money Laundering ◽  
Design Methodology ◽  
Moral Intensity ◽  
Content Type ◽  
Compliance Officers ◽  
Moral Intent ◽  
Practical Implications ◽  
Activity Report ◽  
Suspicious Activity

PurposeThis paper aims to examine the influence Jones’ Moral Intensity Model (1991) has on the decision-making process of anti-money laundering (AML) compliance officers charged with reporting suspicious money laundering transactions in Jersey.Design/methodology/approachTen interviews were conducted to elicit participants’ views on the six dimensions of moral intensity and their influence on the compliance officers’ decision to submit a suspicious activity report (SAR) of potential money laundering.FindingsThe findings indicate that the officers’ moral intensity to submit a SAR seems to be heavily influenced by issue-specific contextual factors. Contexts (legal and legislative mandates) seem to have more of an effect on the moral intent and actions of the officers rather than directly affecting the decision to submit a report of a suspicious money laundering transaction.Research limitations/implicationsThe paper lays the groundwork for further work in this area and calls on researchers to develop instruments that can enhance the measurements of the dimensions of moral intensity.Practical implicationsThe setting (AML in the financial sector) is both timely and extremely interesting to keep studying, particularly in Jersey because of its dubious sensitive particularities.Originality/valueThe study is the first to examine Jersey AML sector through the lens of moral intensity. In this sense, the paper poses interesting questions, namely, to explore the dynamic complexities experienced by compliance officers in Jersey to detect and report suspicious money laundering activities and the decision-making criteria of actually submitting a SAR.

scholarly journals Arcabouço de Vigilância Inteligente para Operações de Compras Eletrônicas no Contexto de Smart Cities

2019 ◽  
Author(s):  
Suzani Dos Santos ◽  
Robson De Melo ◽  
Nivaldi Calonego Junior
Keyword(s):  
Computer Networks ◽  
Smart Cities ◽  
Computer Systems ◽  
User Profile ◽  
Surveillance Systems ◽  
Intelligent Surveillance ◽  
Vulnerability Reduction ◽  
Surveillance Model ◽  
Smart Surveillance ◽  
Suspicious Activity

Intelligent surveillance systems aim to identify suspicious activity beyond the observation of people and environments, as computer networks and computer systems remain vulnerable to malicious actions. The intelligent surveillance framework (Arcabouço de Vigilância Inteligente - AVI) uses the cognitive construction of a user profile and the recognition of devices for electronic purchases, aiming to ensure the authenticity of transactions for computer systems in networked interconnected. The AVI assessment shows that it is effective with respect to user authenticity and that the smart surveillance model in purchasing operations matches the expectation of vulnerability reduction.

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