Machine Learning is a sub-set of Artificial Intelligence, which is the idea of teaching technological machines to think and perform like humans or better. Artificial Intelligence (AI) is the algorithm design for technology to automatically and independently learn the data and information on the machine, and complete the required tasks (Marr 2016). AI is designed and created to initially perform tasks that outdo human performance (Future of Life 2016) for the purpose of changing the way humans interact with the world and technology.

The use of Artificial Intelligence and Machine Learning in Cybersecurity has assisted with the protection of networks, computers and programs from attacks and damage of confidential data. Machine Learning has the ability to recover and protect cybersecurity issues by detecting and blocking attacks with no human intervention. Every day we are faced with cyber threats and attacks whether that is through:

  • Connecting to an unsecure network
  • Opening an attachment
  • Clicking on the wrong web address, etc.

We now know Machine Learning is a fragment of Artificial Intelligence as it is the learning experience of recognising patterns and analysing data that has been taught over time to eventually operate human like and better (Lahiri 2018). “Artificial intelligience (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks” (SAS 2018a). Artificial Intelligence is now improving with Machine learning algorithms being able to automatically build itself to perform better than it once did, by using methods of neural networks, statistics and deep learning to extract data and perform adequately (SAS 2018a).

Machine Learning has a lot of history behind it as they have been evolving over many centuries, from ultimately being physical objects to now immaterials.

  1. Over the many decades of progressing Machine Learning, in 1950, Alan Turning invented the Turning Test. This was designed to determine if the computer had actual intelligence by testing humans and misleading them to think the computer is also a human with a like intelligence. For a human to be fooled by this test meant the computer considered to have Artificial Intelligence embedded into its system as it is able to mimic human responses (Sheth 2017).

 

  1. Next was in 1952, where Arthur Samuel “wrote the first computer learning program” (Murr 2016). The learning program was a game of checkers, where a human would verse the IBM computer. Over time, the more the computer played, the more advanced it became with studying the moves that made up the winning strategies (Murr 2016).

 

  1. In 1957, The Perception was created by Frank Rosenblatt. The Perception is a type of neural network that works a like the human brain by connecting together neurons in a network to make simple decisions by all coming together to then solve complex problems (Sheth 2017).

 

  1. A pattern recognition algorithm called The Nearest Neighbour was then written in 1967 to allow computers to use basic pattern recognition to perform small tasks that would usually be completed by a human (Sheth 2017).

 

  1. The Stanford Cart is a remote controlled robot that can move and     navigate obstacles on its own by a written computer program, which was invented by a group of students (Sheth 2017).

    (Sheth 2017)

  2. Explanation Based Learning is a program that was introduced in 1981. It was designed to analyse training examples and data, and disregard any irrelevant information “to form a general rule to follow” (Sheth 2017).

 

  1. In the 1990’s, researches began applying Machine Learning to applications and software’s to improve technology and the way they designed and wrote them, to be able to continue learning itself with no human interaction (Sheth 2017).

 

  1. Now in the 2000’s, Adaptive Programming is now existing where Machine Learning are. All these programs now have the ability to identify data and patterns, learn from their experiences and improve themselves with no human intervention (Sheth 2017).

 

Machine Learning has improved rapidly since the beginning of the invention of The Turning Test in 1950. Now as adaptive programming is present, it involves Machine Learning, such as the software program Palo Alto Networks created called the Magnifier Behaviorial Analytics. Palo Alto introduced Magnifier to improve network behaviour and threat detection by using supervised and unsupervised Machine Learning (Oltsik 2018).

Supervised Machine Learning refers to the learning algorithm of being taught using examples, “such as an input where the desired output is known” (SAS 2018b). Unstructured Machine Learning is the learning algorithm of going “against data that has no historical labels” (SAS 2018b). Both learning algorithms recognised patterns to make decisions and then complete the task alone, but in different ways and forms.

Machine Learning originates from science and technology research. The purpose was for researchers to attempt to improve computer’s self-understanding of data and to process it to gain quick insight and complete tasks with no human intervention (Sheth 2017). Machine Learning is to ultimately create Artificial Intelligence in the world and improve Computer Science. Machine Learning will eventually grow and improve more and more, particularly for Cybersecurity. Machine Learning in cyber security is designed to “detect, analyse, and defend against advanced attacks by proactively detecting and tricking attackers”. There is no human interaction involved as it builds intelligence itself with experiences of all types of attacks it is faced with (Lahiri 2018). Machine Learning is able to help improve all fields of all industries.

Machine Learning is a statistical model; it has advanced computers and the way IT now operates. It is the ability of a computer to self-learn and adapt through experiences (Lahiri 2018). It is the idea of building blocks, to build over time with its understandings. Machine Learning relies on well written algorithms to be able to advance its intelligence alone. For Machine Learning to sufficiently work, it has to be able to have “data preparation capabilities, algorithms – basic and advance, automation and iterative processes, scalability and ensemble modelling” (SAS 2018b).

Many companies are now starting to recognise the real value of Machine Learning technology (SAS 2018b) and how it can improve their performances. Such as Palo Alto Networks, a cybersecurity organisation, have recently designed the Magnifier Behavioral Analytics software. The software is intended for cybersecurity solutions by having the performance capability to self-operate to analyse rich networks, endpoints and cloud data all with machine learning to identify certain “targeted attacks, malicious insiders and malware” (Palo Alto Networks 2018a). The software is created to better computer protection from unauthorised access of confidential information for malicious use. Many companies can incorporate Machine Learning into their daily activities to improve their performances and effectiveness, which can create less stress and energy into completing tasks. Many companies can also incorporate this particular security solution to help protect themselves and their consumers from attacks, which can lead them away from liquidation.