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AI in Cybersecurity: A Safe Haven Amid the World of Chaos

With an unprecedented development in artificial intelligence (AI) and machine learning (ML), threats in cybersecurity are on the rise.

Owing to the recent digital transformation caused due to the COVID-19, it has become critical to pay heed to cybersecurity concerns and develop ways to improve.

Cybersecurity may become futile before the eyes of creative hackers and saboteurs. This is where AI and ML need to play their role. Considered one of the latest trends in AI this 2021, cybersecurity remains a top concern for most organizations.

The evolution of cyber-systems

From profiles of Barack Obama to Elon Musk and Bill Gates, the attacker managed to hack nearly 130 accounts on Twitter in July 2020. These attackers bypassed account security by gaining access to Twitter’s internal administration tools.

This was not the very first-time such attacks took place. There have been attacks on social networks such as Facebook too.

Due to multiple cyber-attacks taking place over the years, people have become more than aware of securing their data online.

Cyber-attack isn’t new, it has been here for a long time now. Although they were considered as basic attacks, the recent ones are now considered as the advanced cyber threats the world is experiencing. These attackers have started targeting multiple networks and devices. The reason why cybersecurity evolved ensuring agility to be the major part of the protection strategy.

Let us shed more light on the subject.

  • Security Vs. Privacy 

Keeping hidden personal information from the observed data while gathering insights to make accurate decisions may not be a great task to achieve. Anonymization is likely to create sparsity of dataset and may require additional layers to clean.

In such situations, machine learning and AI works great for such types of dataset.

  • Rapid growth in the dimensionality of observations 

With the rise of a computer virus, even a simple hash value can determine a malicious file. However, using advanced mutations patterns of activity, ISP/source reputation, a combined geo map, and other multiple parameters need to be closely determined.

And with the increase of variables, the ability of rule which helps identify malice from the usual ones is not always direct, this is where the intrusion of AI and machine learning becomes a preferred choice.

  • Unique identities get wiped out 

With the rapid growth in internet services, most systems could create device and network identifiers like MAC address and IPv4 address. Thus, leading to the explosion of these devices which eventually reduced the number of network addresses available. Now with the help of technology, such addresses can be located using the same IP address spread across network hierarchies.

Owing to this, it is seen that multiple identities are lost. To curb this issue, we can use AI and ML to find similar patterns and refine them until they have more knowledge about the source.

  • Conversational system

Perhaps we’re all aware of how the user interface in sync with digitized services is extensively used today. Well, such conversations are not structured – chatbots, product inquiries, and e-mail based automated quotes.  And to safeguard these automated devices, the same level of conversational intelligence needs to be maintained. This can only be achievable using AI and ML. These are certain factors an AI expert or a specialist needs to consider before entering the AI realm.

AI and machine learning in cybersecurity 

There are multiple ways through which AI and machine learning can be used in cybersecurity. Not to mention, assaulters and attackers are already using AI for assaults. Therefore, we need to be extra cautious in utilizing these technologies.

Analyst at ABI Research predicts, by 2021, the usage of machine learning in cybersecurity will accelerate spending in AI, big data, and analytics to USD 96 billion.

  • Combat AI threats 

Hackers have become intelligent by being able to detect points entering enterprises using AI. This is one major reason why organizations need to accelerate the speed at which they are using cybersecurity.

During the past years, organizations have faced serious ransom ware attacks like WannaCry and Notepeta. Such attacks are likely to spread fast affecting a significant number of computers. The only way for organizations to fight such threats is by adopting AI for faster scalability.

  • ML used to boost human analysis 

Machine learning in security helps analysts working in all aspects of jobs to detect malicious attacks, assess vulnerability or analyze the network even before it can happen. For instance, MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) in 2016 created a system known as AI2. It is an adaptive ML security platform aimed at helping analysts find vulnerabilities after reviewing millions of logins per day.

Besides these, AI and machine learning also help in user behavior modeling, email monitoring, network threat identification, and anomaly detection.

We’re all certain of cybersecurity being one of the biggest trends in AI, we need to ensure about safely using our data online.

Keeping pace with cybersecurity trends and gearing up your security game is a great way to stay safe amid the chaos.

 

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