Visible to the public A Semantic Machine Learning Approach for Cyber Security Monitoring

TitleA Semantic Machine Learning Approach for Cyber Security Monitoring
Publication TypeConference Paper
Year of Publication2019
AuthorsGoyal, Y., Sharma, A.
Conference Name2019 3rd International Conference on Computing Methodologies and Communication (ICCMC)
Date Publishedmar
Keywordsbusiness processes, Computer crime, Cyber Attacks, cyber security, cyber- attacks, cyber-attacks, cyber-threats, Data models, destiny attacks, deterministic styles, digital global community, information safety measure, learning (artificial intelligence), machine learning, Monitoring, network monitoring, pubcrawl, resilience, Resiliency, Safety, Scalability, security of data, semantic machine, software technologies, Stochastic Computing Security, stochastic styles, Training
AbstractSecurity refers to precautions designed to shield the availability and integrity of information exchanged among the digital global community. Information safety measure typically protects the virtual facts from unauthorized sources to get a right of entry to, disclosure, manipulation, alteration or destruction on both hardware and software technologies. According to an evaluation through experts operating in the place of information safety, some of the new cyber-attacks are keep on emerging in all the business processes. As a stop result of the analyses done, it's been determined that although the level of risk is not excessive in maximum of the attacks, it's far a severe risk for important data and the severity of those attacks is prolonged. Prior safety structures has been established to monitor various cyber-threats, predominantly using a gadget processed data or alerts for showing each deterministic and stochastic styles. The principal finding for deterministic patterns in cyber- attacks is that they're neither unbiased nor random over the years. Consequently, the quantity of assaults in the past helps to monitor the range of destiny attacks. The deterministic styles can often be leveraged to generate moderately correct monitoring.
Citation Keygoyal_semantic_2019