Paper Title: COLLABORATIVE FILTERING OF MALICIOUS INFORMATION FROM THE MULTIMEDIA DATA USING DEEP BELIEF NEURAL NETWORK

Author's name: Ms Gomathy M , Dr.A.Vidhya

Every aspect of daily life has been impacted by the Internet's debut, which allowed for global virtual collaborations. Owing to the internet's wide distribution, the exponential increase of mobile data, and the widespread use of online forums, online crime has become more common. Online users, including but not limited to service providers, face a serious threat from unwanted information since it harms their reputations and services. Consequently, creating an intelligent model to filter unsolicited information is required. Unwanted information is categorized and filtered using contemporary machine learning algorithms. While processing all the text, image, and video files, some standard procedures are insecurely created, making it difficult to detect malware in the files. Due to the exponential growth in the creation of new ransomware, malware identification has become a challenging research topic. Businesses and commoners find it difficult to protect themselves against malware in the digital environment, which emphasizes the importance of developing efficient spyware protection techniques. Considering the above context, the objective of this systematic review is two-fold, (1) To examine and comprehend the numerous problems caused by malicious programs in virtual platforms and (2) to assess and suggest a method using Deep Neural Network for classifying a sample as benign or malicious with high accuracy and minimal overhead.

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