In today’s age, it is impossible to implement effective cybersecurity technology without depending on innovative technologies like machine learning and artificial intelligence.
Machine learning in the field of cybersecurity is a fast-growing trend. The analysts at ABI Research predicted that machine learning in cybersecurity would increase the spending in artificial intelligence, analytics, and big data to 96 billion USD by 2021.
However, some of the world-famous technology giants are taking a stand to protect their customers.
Machine learning- a subset of AI is helping the business organizations to analyze the threats and respond to attacks and security incidents.
It also helps to automate more boring and tedious tasks that were previously carried out by under-skilled security teams.
Now, Google is also using machine learning to examine the threats against mobile endpoints running on Android along with detecting and removing malware from the infected handsets.
Similarly, vendors of enterprise security are working harder to incorporate machine learning into new and old products and services, in a race to improve malware detection.
This goes for companies you haven’t yet incorporated ML. Coming back to the main topic, we’ve compiled this article for you to know how machine learning is being used to control security vulnerabilities.
But, first, we’ll briefly discuss the importance of ML in cybersecurity. Let’s go on!
Why is Machine Learning Crucial for Cybersecurity?
Cybersecurity is a crucial area where machine learning is becoming increasingly significant and famous.
There are several reasons for ML being essential for cybersecurity. Because of ML, cybersecurity systems can determine patterns and learn from them to prevent similar attacks and respond to the changing behavior.
Moreover, machine learning helps the security teams to become more proactive in preventing threats and responding to active attacks in real-time.
It can reduce the time spent on daily routine tasks and allow organizations to use their resources strategically.
To sum up, machine learning can make cybersecurity less expensive, more proactive, and more productive.
However, it can do all these things if the underlying data that supports the machine learning provides the entire picture of the environment.
How Machine Learning Manages Security Vulnerabilities/ Attacks?
Both artificial intelligence and machine learning are a boon to the cybersecurity industry. They can categorize millions of files and detect the potentially harmful ones.
Machine learning is used to uncover threats and prevent them before they become havoc. Several cybersecurity experts tell that cybercriminals or hackers use hazardous malware to infect hundreds and millions of computers.
But, these attacks can be stopped by deploying multiple layers of machine learning algorithms.
In addition to early detection, AI and ML do scan for vulnerabilities in the network and can prevent adware attacks and other cyber-attacks.
As per the sources, more than 30% of the CSO depends on AI as ethical hackers are looking for new ways to exploit security vulnerabilities.
The following mentioned below are different cases/ cyber-attacks/ or vulnerabilities that machine learning handles efficiently.
1. Spear Phishing
The phishing campaigns are the most prevalent and successful attack vectors in today’s era.
These attacks take complete advantage of the victim’s familiarity with the communication tools such as social media and email to send to unknown recipients’ malicious content by an attachment or a link.
The efficiency of the attack depends on the ability of the attackers to misguide the individual into clicking or download malicious payloads and bypass the internal controls.
The latest additions of destructive and ransomware payloads make the attack even more severe.
Organizations can identify these threats by storing the metadata from emails without compromising on user’s privacy.
By looking at these email headers, the machine learning algorithms can detect the patterns that reveal malicious sender’s emails.
By pulling out and labeling these micro behaviors, we can train the models to discover if any phishing attempt has occurred.
2. Watering Hole
Watering holes appear similar to phishing attacks; the difference is that they pretend to come from a legal website or applications.
But, these sites or apps are real and have been compromised previously, or they are fraudulent sites or apps designed to attract unsuspecting visitors to give away their personal information.
Such attacks also rely on the part of the ability to mislead the users to serve exploits effectively.
Machine learning can assist the organizations to benchmark the web application services by determining the data like path traversal statistics.
The algorithms that learn with time can detect interactions that are common to those of attackers or other malicious apps and websites.
Moreover, machine learning also monitors the behavior of rare or unusual redirect patterns to and from the site’s host along with other risk indicators.
3. Covert Channel Detection
Now, attackers are using covert channels to transfer information through channels that are not intended for communication.
Using covert channels allows the attackers to maintain the control of compromised assets and to use tactics that permit the execution of attacks over time.
The attacks using the covert channels depend on the visibility of all domains across a given network.
With machine learning technology, statistics about the rare areas can be ingested and analyzed. With this information, the security operation teams can work to cloud attackers’ visibility.
Without possessing a holistic view of the network, they aim to attack and it becomes difficult for cybercriminals to keep their attacks moving forward.
4. Credential Theft
Credential theft is launched by using tactics such as phishing or watering holes. In such attacks, the attacker extracts the login details from the victim in an attempt to access the sensitive information on the organization maintains.
Few of the high-profile attacks, including the VPN compromises, are a result of credential theft.
All internet users do leave behind the login patterns. Several applications and websites do track the location and login times of the consumers.
Machine learning can also follow these patterns and the data that comprises those patterns to learn about what kind of user behavior is typical and which represents possible harmful activity.
5. Lateral Movement
The lateral movement attack vectors represent an attacker’s movement across a network as they are looking for vulnerabilities and tries different techniques to exploit those vulnerabilities.
It is particularly indicative of risk escalation along with the kill chain. It means that an attacker’s movement from investigation to data extraction, especially when attackers make a move from low-level users’ machines to those of more critical personnel.
The network traffic input logs can reveal a lot about the visitors’ interactions with a website. Machine learning informs the contextualization of the data and can provide a dynamic view of standard traffic data.
With a better understanding of the typical traffic flow, algorithms can perform change-point detection to detect the possible risk of threats.
It includes probing networks for vulnerabilities. Before launching an attack, hackers perform an extensive reconnaissance on a targeted group.
Cybercriminals perform recon at the perimeter of a network or within the LAN (local area network).
Reconnaissance detection includes signature-matching technology that searches through network activity logs for repeated patterns that represent malicious behavior.
Machine learning can be a well-known compass for the topology of network data. The trained algorithms can make a graph of this topology to detect the spread of new patterns more rapidly than the signature-based methods.
Deploying machine learning lessens the number of false positives, enabling the security experts to spend time addressing the alarm that does matters.
7. Remote Exploitation
Most of the attack patterns use remote exploitation. These attacks operate through a series of malicious events that target a system to recognize vulnerabilities and then deliver a payload to destroy the vulnerability. Once the attack drops the payload, it enforces code within the system.
Machine learning can examine system behavior and identify cases in which subsequent action does not relate to typical network behavior.
Algorithms that have learned with time can then warn the security analysts about the expected delivery of an exploitation payload.
Towards the end, it is quite clear that all those security teams that not just adopt but also implement the machine learning technology can quickly address the attacks or vulnerabilities mentioned above.
As the technology is going to become more widespread in the upcoming years, we can expect that machine learning along with AI technology will play a more significant role in combating cyber-attacks.