Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Abstract

Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unable to produce results due to their slowness and inefficiency. The methods for accurately predicting crime before it happens "automated machine" to help police officers ease the burden on personnel while also assisting in preventing offense. To achieve and explain the results of a few cases in which such approaches were applied, we advise combining machine learning (ML) with computer vision (CV) strategies. This study's objective is to present dark web crime statistics and a forecasting model for generating alerts of illegal operations like drug supply, people smuggling, terrorist staffing and radicalization, and deceitful activities that are connected to gangs or organizations showing online presence using ML and CV to help law enforcement organizations identify, and accumulate proactive tactics for solving crimes.

Design and implementation of an alcohol detection driver system

“…The MQ03 sensor, NodeMCU, and Arduino implementation entail wiring up the hardware parts, creating code to read the sensor's output, calibrating the sensor, and testing the implementation to make sure everything is operating as it should. The development of low cost alcohol detection systems for uses like breathalyzer tests or alcohol ignition interlocks may benefit from this kind of implementation [23], [24]. This system's technology automatically recognizes drunk driving and traffic incidents.…”