How Machine Learning Can Defend The Enterprise From Cyber Attacks?

Businesses today operate based on a rigid, rule-based approach to tackle complex processes. Most firms have a good grasp of their direct and indirect competitors. These can be influenced by various factors such as social, economic, and technical changes which carry with them the potential to disrupt the functioning of business in ways unimaginable.


As we know, businesses invest heavily in technology and intelligence projects to enhance the quality of their day-to-day operations and to aid in decision-making. In spite of being equipped with such information, it is possible for firms to be affected by financial, legal, and reputational factors. The term that can be used to describe these factors is a security breach or a cyber attack.


So, what is a cyber attack?



Also known as a Computer Network Attack (CNA), it refers to the deliberate exploitation of computer code, computer systems, data, and technology-dependent enterprises. It uses malicious code which leads to disruptive consequences and cyber crimes such as stolen hardware, breach of access, password sniffing, system infiltration, exploitation of public and private web browsers, and unauthorized access.


In order to protect themselves from falling prey to cyber attacks, companies today use technologies such as Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI). Of these, this article will throw light on Machine Learning.


Machine Learning is one that builds algorithms which can receive input data and make use of statistical analysis to predict an output. It requires searching for data and adjusting program actions accordingly. It is used for detecting security threats in networks, spam filtering, predictive maintenance, fraud detection, and for creating news feeds.


With the types of cyber attacks on companies constantly evolving, Machine Learning is a necessity for businesses as it plays the major roles of:


  • Processing large data sets to train predictive models and identify relationships.
  • Updating and keeping pace with changing data.
  • Processing and analyzing numerous malicious samples.
  • Analyzing previous cyber attacks and developing the required defense responses.
  • Aiding businesses for better threat analysis.
  • Enabling computers to learn and adopt new behaviors and features.
  • Finding, interpreting, and analyzing relationships and data trends.
  • Rapidly determining if a file is benign or malicious and instantly performing the necessary actions for blocking it.
  • Automating repetitive tasks thereby enabling organizations to save time, effort, and money.
  • Increasing the accuracy and precision of automated processes.
  • Helping in pattern recognition, predictive analysis, and unstructured data processing.


Apart from protection against cyber attacks, businesses today use Machine Learning for solving data-rich business problems, enhancing scalability, and improving business operations. With the increasing amount of data used in companies, Machine Learning is the ideal solution as it is highly adaptive and keeps learning new data continuously. Companies can protect themselves from headline-grabbing data leaks by implementing Machine Learning.