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Topic Review
Deception Technology
Deception technology is a category of cyber security defense. Deception technology products can detect, analyze, and defend against zero-day and advanced attacks, often in real time. They are automated, accurate, and provide insight into malicious activity within internal networks which may be unseen by other types of cyber defense. Deception technology enables a more proactive security posture by seeking to deceive the attackers, detect them and then defeat them, allowing the enterprise to return to normal operations. Existing defense-in-depth cyber technologies have struggled against the increasing wave of sophisticated and persistent human attackers. These technologies seek primarily to defend a perimeter, but both firewalls and endpoint security cannot defend a perimeter with 100% certainty. Cyber-attackers can penetrate these networks and move unimpeded for months, stealing data and intellectual property. Heuristics may find an attacker within the network, but often generate so many alerts that critical alerts are missed. Since 2014, attacks have accelerated and there is evidence that cyber-attackers are penetrating traditional defenses at a rapidly increasing rate. Deception technology considers the human attacker's point of view and method for exploiting and navigating networks to identify and exfiltrate data. It integrates with existing technologies to provide new visibility into the internal networks, share high probability alerts and threat intelligence with the existing infrastructure.
  • 493
  • 10 Nov 2022
Topic Review
Breast Density and Pre-Trained Convolutional Neural Network
Breast density describes the amount of fibrous and glandular tissue in a breast compared with the amount of fatty tissue. The breast density is assigned to one of four classes in the mammogram report based on the ACR BI-RADS standard. Convolutional Neural Network (CNN) are a type of artificial neural network usually used for classification and computer vision tasks. Therefore, CNNs are considered efficient tools for medical imaging classification.
  • 492
  • 21 Jun 2022
Topic Review
Shibboleth (Shibboleth Consortium)
Shibboleth is a single sign-on log-in system for computer networks and the Internet. It allows people to sign in using just one identity to various systems run by federations of different organizations or institutions. The federations are often universities or public service organizations. The Shibboleth Internet2 middleware initiative created an architecture and open-source implementation for identity management and federated identity-based authentication and authorization (or access control) infrastructure based on Security Assertion Markup Language (SAML). Federated identity allows the sharing of information about users from one security domain to the other organizations in a federation. This allows for cross-domain single sign-on and removes the need for content providers to maintain user names and passwords. Identity providers (IdPs) supply user information, while service providers (SPs) consume this information and give access to secure content.
  • 492
  • 20 Oct 2022
Topic Review
Neuron (Software)
Neuron is a simulation environment for modeling individual and networks of neurons. It was primarily developed by Michael Hines, John W. Moore, and Ted Carnevale at Yale and Duke. Neuron models individual neurons via the use of sections that are automatically subdivided into individual compartments, instead of requiring the user to manually create compartments. The primary scripting language is hoc but a Python interface is also available. Programs can be written interactively in a shell, or loaded from a file. Neuron supports parallelization via the MPI protocol. Neuron is capable of handling diffusion-reaction models, and integrating diffusion functions into models of synapses and cellular networks. Parallelization is possible via internal multithreaded routines, for use on multi-core computers. The properties of the membrane channels of the neuron are simulated using compiled mechanisms written using the NMODL language or by compiled routines operating on internal data structures that are set up with Channel Builder. Along with the analogous software platform GENESIS, Neuron is the basis for instruction in computational neuroscience in many courses and laboratories around the world.
  • 492
  • 11 Nov 2022
Topic Review
ID2SBVR: Semantics of Business Vocabulary and Rules
Semantics of Business Vocabulary and Rules (SBVR) is a standard that is applied in describing business knowledge in the form of controlled natural language. Business process designers develop SBVR from formal documents and later translate it into business process models. In many immature companies, these documents are often unavailable and could hinder resource efficiency efforts. ID2SBVR mines fact type candidates using word patterns or extracting triplets (actor, action, and object) from sentences.
  • 492
  • 18 Nov 2022
Topic Review
VRPN
VRPN (Virtual-Reality Peripheral Network) is a device-independent, network-based interface for accessing virtual reality peripherals in VR applications. It was originally designed and implemented by Russell M. Taylor II at the Department of Computer Science of the University of North Carolina at Chapel Hill. VRPN was maintained and supported by Sensics while it was business. It is currently maintained by ReliaSolve and developed in collaboration with a productive community of contributors. It is described more fully at vrpn.org and in VRPN-VRST. The purpose of VRPN is to provide a unified interface to input devices, like motion trackers or joystick controllers. It also provides the following: The VRPN system consists of programming interfaces for both the client application and the hardware drivers and a server application that communicates with the hardware devices. The client interfaces are written in C++ but have been wrapped in C#, Python and Java. A typical application of VRPN is to encode and send 6DoF motion capture data through the network in real time.
  • 492
  • 28 Nov 2022
Topic Review
SIGKDD
SIGKDD is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining. It became an official ACM SIG in 1998.
  • 491
  • 18 Nov 2022
Topic Review
Smart Chatbot for User Authentication
Despite being the most widely used authentication mechanism, password-based authentication is not very secure, being easily guessed or brute-forced. To address this, many systems which especially value security adopt Multi-Factor Authentication (MFA), in which multiple different authentication mechanisms are used concurrently. JitHDA (Just-in-time human dynamics based authentication engine) is a new authentication mechanism which can add another option to MFA capabilities. JitHDA observes human behaviour and human dynamics to gather up to date information on the user from which authentication questions can be dynamically generated. 
  • 491
  • 23 Dec 2022
Topic Review
Data Integrity Tracking and Verification System
Data integrity is a prerequisite for ensuring data availability of IoT data and has received extensive attention in the field of IoT big data security. Stream computing systems are widely used in the field of IoT for real-time data acquisition and computing. The real-time, volatility, suddenness, and disorder of stream data make data integrity verification difficult. The data integrity tracking and verification system is constructed based on a data integrity verification algorithm scheme of the stream computing system (S-DIV) to  track and analyze the message data stream in real time. By verifying the data integrity of message during the whole life cycle, the problem of data corruption or data loss can be found in time, and error alarm and message recovery can be actively implemented.
  • 491
  • 13 Feb 2023
Topic Review
Federated Learning Algorithms for IoT
Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL make it a suitable choice for IoT networks due to its intrinsic distributed infrastructure. However, FL presents a few unique challenges; the most noteworthy is training over largely heterogeneous data samples on IoT devices. The heterogeneity of devices and models in the complex IoT networks greatly influences the FL training process and makes traditional FL unsuitable to be directly deployed, while many recent research works claim to mitigate the negative impact of heterogeneity in FL networks, unfortunately, the effectiveness of these proposed solutions has never been studied and quantified.
  • 490
  • 27 May 2022
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