Role of AI, Machine and Deep learning
in the future of information security

Mikin Patel

Harrisburg University of Science and

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Role of AI, Machine and Deep learning
in the future of information security


From North to South, East to West, Europe
to North America emerging technologies like machine learning, artificial
intelligence and deep learning are disruptive innovations that have changed the
landscape on how governments, businesses and individuals implement information
security measures in the era of ‘Big Data’. To start with Machine learning and
Artificial intelligence are used interchangeably today in big data but are
applied differently. It has made it possible for the gathering of big data and
expansion of Internet of Things (IoT), consequently new Artificial Intelligence
application and service to grow. The new Artificial intelligence application is
now used in healthcare, transportation, service robots; entertainment and
education just to mention a few. According to Deng (2014), argues that Artificial intelligence
entails a broader concept that machine learning which focuses on the use of
computers in mimicking cognitive functions of human beings. Machines are
usually programmed to carry out tasks based on programmed algorithms in an
‘intelligent ‘way, which now constitutes Artificial intelligence. But Machine
learning is now a subset of Artificial intelligence which deals with the
ability of machines to receive a certain set of data and learn for themselves
and changing algorithms which involve information processing. Thus, training of
computers to act and think like human beings entail the use of neural networks
which are series of algorithms which are modeled like the human brain. On the
other hand, deep learning is a subset f machine which entails the deep neural
networks where the data in the information system is exposed to millions of
data points during transmission. The current paper, therefore, examines how
artificial intelligence, machine learning and deep learning play a leading role
in the future of information security.



of AI, Machine learning and deep learning in the future of information security

First, Artificial intelligence which has
been defined effectively as the simulation of intelligence processes by
computers employs the elements of deep and machine learning in the world of
information and communication technology. In this case, the former usually
comes into play when Artificial Intelligence gives the inanimate system some ability
to automatically learn and improve the experience with human-like traits while
machine learning gives computers some kind of ability to learn without being
programmed. In the recent past, companies like Ube have deployed machine
learning in their operation to determine arrival times for riders and estimate
meal delivery times on the UberEATS and lastly use it to compute some proper
optimal pickup locations(Jin, Gubbi,
Marusic & Palaniswami,2014).In addition, Google search engine use
deep learning in voice and image recognition algorithms and while Amazon uses
the same in determining on what customers love to watch or buy next, that is
testing consumer tests and preferences. Some of the latest statistics indicate
that machine learning has grown threefold since 2015 to companies like Gmail,
Google, Netflix and Amazon which deployed machine learning in the day to day
operations thus becoming critical components of online retail, fraud detection
and recommendation systems just to mention a few (Witten, Frank & Hall, 2011).  Besides, some of the machine learning
algorithms are categorized as, reinforcement supervised or unsupervised
learning. First, the supervised learning which requires a kind labeled training
datasets and sometimes not recommended for cyber security. Besides,
reinforcement learning is when algorithm interacts with a dynamic environment
which provides some kind of feedback in terms of rewards and punishments like
self-driving cars which are usually rewarded to say on roads. Also, unsupervised
learning does not require training data and in most cases, it is the best bet
for detecting suspicious activities like detecting attacks which have never
seen before on the information systems anywhere in the world

According to Deng (2014) posits a very interesting opinion on the
issue cyber security and how it has broken down completely. In the day to day
operation, big companies suffer attacks and many of these attacks are
successful therefore affecting information system. Since cyber security is zero
tolerance field by which if there is one successful attack on information
system infrastructure is a clear indication of the failure of the whole
security system and thus if there is way machine learning can aid in addressing
this weakness in the cyber securer then there is need to implement it once and
for all.

Big data capabilities

In the last two decades, major business
and companies have adopted machine learning capabilities meaning that day by
day machine learning is no longer optional in the cyber security
infrastructure. Thus, the Internet of Things (IoT)   will then challenge existing cyber security
and in future will increase the amount of data produced online and distributed.
As a result, this will change the landscape of weaknesses’ which can be
exploited by hackers and this can be handled by machine learning. It provides a
24/7 monitoring security and carries larger data loads than can human beings
deal with but requires human interventions. Since it is not a plug and plays
technology, requires human tuning t aid in filtering real attacks from what it
appears to be suspicious activity. Although machine has added a new layer in
the area of information systems, it cannot replace human interventions but
merely ha aided in adding a multi-layered defense (Witten, Frank & Hall, 2011). On the other
hand, machine learning algorithms and generated interest in areas of neural
network and this the current growth in Artificial intelligence and machine
learning are tied to development in the three main areas

First, data availability where over 5
billion are online and it is estimated that over 2 billion are connected to
device or sensors which generate large amounts of data and when combined with
decreasing costs of data storage can readily be available for use. Thus,
machine learning can be used in training data for learning new algorithms and
developing new rules to perform some of the complex tasks(Jin, Gubbi, Marusic &

In addition, the computing power with
powerful computers and the ability to connect to remote processing power though
intent has now enabled machine learning techniques with large amounts f data

Lastly, the algorithmic innovation in new
machine learning methods especially in neural networks’ also called deep
learning has led to new services like research and investment in other parts of
the field.

Meanwhile, deep learning the new area t
watch for future as it a bit different from machine learning since it solely
based algorithms which mimic how brain neurons behave. It tries to achieve
unsupervised learning as in the case of machine learning, that is tries to be
able to recognize known and unknown attacks types. Also, it is important to
note that it may be incomplete, make complex data better or messy which is the
kind of data we usually see in the real world. Take an example in the
manufacturing where it has been dubbed as Industry 4.0 is getting smarter now
that managers have fully implemented Artificial intelligence, machine learning
and internet of things .Consequently, all data collected from partners, market
factory floors, and customers is analyzed and predicated thus allowing them to
change the way production is done, shipping and packing in order to improve
efficiency and accuracy.

In conclusion, as Artificial Intelligence
,deep and machine learning continues o evolve and mature over time ,it is
important for organization to start implementing it  in order to enable organization to embrace
working smarter, better and faster. To achieve this, businesses need
information security strategy policy that is formal, with a codified method of
making prediction about the future. Businesses therefore are required o make
assumptions about the future by first reexamining existing processes and make
it easier to see the type of future in the current strategy which they are
striving to head towards. Thus, this requires left brained which is analytical
and right brained, creative, talent and culture when deployment these
technologies (Najafabad et al 2015).Lastly,
value creation processes from Artificial intelligence are no different, but
begins with genres new ideas and ends with proper execution strategy.


















Deng, L.
(2014). Deep Learning: Methods and Applications. Foundations and Trends® in Signal
Processing, 7(3-4), 197-387.

Jin, J.,
Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An
Information Framework for Creating a Smart City Through Internet of
Things. IEEE
Internet of Things Journal, 1(2), 112-121.

Najafabadi, M. M.,
Villanustre, F., Khoshgoftaar, T. M., Seliya, N.,
Wald, R., & Muharemagic, E. (2015). Deep learning applications
and challenges in big data analytics. Journal of Big Data, 2(1).

Witten, I. H.,
Frank, E., & Hall, M. A. (2011). Embedded Machine
Learning. Data
Mining: Practical Machine Learning Tools and Techniques,
531-538. doi:10.1016/b978-0-12-374856-0.00015-8



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