The Future of Cybersecurity | Trends and Predictions of Cybersecurity in 2021

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[Music] future of cyber security with this four evolving technologies more than ever we conduct more of our personal lives and business activities online making cyber security a key issue of our time understanding what cyber securitys future is will teach you how to make the best use of your resources and remain secure not only today but tomorrow as well the future of cyber security is difficult to foresee as the market is continuously changing in response to cyber criminals shifting activities and the new attacks they are creating for instance between 2019 and 2020 the number of global ransomware attacks increased by almost 25 causing cybersecurity developers and businesses to build new applications to combat the phenomenon despite the challenges there is a promise to reduce human dependence and to strengthen the capacity for cyber security myriad of evolving cognitive technologies can help us improve cyber security and navigate the increasingly malicious and disruptive landscape of cyber threats 1. artificial intelligence artificial intelligence has come to life in many sectors as a technology over the past few years today it is possible to use ai and machine learning algorithms to automate jobs crunch data and make decisions much faster than a person ever could nonetheless potentially new technologies like ai pose cyber security threats as future vulnerabilities are poorly understood at release time this means that ai systems are sure to become a major target for hackers with more organizations relying on machine learning for mission critical operations in addition potential tools and staff for cyber security will be forced to build techniques to detect and combat ai corruption attacks however ai changes the world of cyber security by offering hackers a new way to hit target organizations cyber security developers will use ai to address vulnerabilities themselves detect security issues before they can be exploited and repel cyber attacks once they have started 2. machine learning effective cyber security technology cant be deployed today without relying heavily on machine learning at the same time deploying machine learning effectively is impossible without a comprehensive rich and complete approach to the underlying data cyber security systems can identify patterns with machine learning and learn from them to help prevent repeated attacks and respond to the changing behavior it can help cyber security teams be more proactive in prevention of threats and the real-time response to active attacks this will reduce the amount of time spent on repetitive activities and allow companies to use their resources more strategically in short machine learning will make cyber security easier more proactive less expensive and much more efficient but only if the underlying data supporting machine learning offers the full image of the world can it do such things 3. adaptive networks adaptive networks are automated and programmable networks that can configure track manage and adapt to changing needs these networks are based on three basic layers programmable infrastructure analytics and intelligence and control and automation applications the programmable layer of infrastructure serves as a sensor and generates a real-time data on network efficiency and vulnerabilities enabling agencies to fix them proactively and assign resources accordingly while the analytics layer brings insight to the network it applies machine learning to analyze data based on performance and to predict network issues and threats more accurately the end layer is monitoring and automation applications to simplify network management and service delivery through multi-vendor multi-domain hybrid networks it leverages software-defined network architectures and multi-domain service orchestration such layers combine to create a more flexible scalable and stable network an adaptive network helps agencies meet rising bandwidth stresses as well as demands for modernization and security delivering high performance connectivity and faster services to constituents 4 super computing locating a needle in a haystack can be like identifying cyber security threats from raw internet data for example the amount of internet traffic data generated in a 48-hour span is too large for one or even 100 laptops for human analysts to process into something digestible for this purpose analysts rely on sampling to check for possible threats choosing small pieces of data to investigate in depth trying to detect unusual behavior although this form of sampling can work for certain tasks such as identifying common ip addresses the identification of subtler threatening patterns is inadequate supercomputing is promising in cyber security mit lincoln labs fellow jeremy kepner states that detecting cyber threats can be greatly improved by providing a detailed model of regular background network traffic and that researchers should equate the internet traffic data they are examining with these models in order to bring anomalous activity to the surface more readily at a conference sponsored by darpa supercomputer science humans this type of capability was shown to be exposed to bugs which the computers were able to detect and quickly repair the threats human factor fallibility has become a weakness in cyber security it will probably get more as we become more immersed in digital interconnectivity i.e remote work on the internet of things smart cities associated with the realities of a larger cyber attack surface smart cyber security plays a promising and an important role in identifying filtering neutralizing and remedying cyber threats through harnessing the emerging market technologies such as artificial intelligence machine learning automated and adaptive networks and super computing companies would be able to address potential challenges more readily like and subscribe if you like the [Music] information you

FUTUREYAN: The Future of Cybersecurity | Trends and Predictions of Cybersecurity in 2021 - Cybersecurity