How Artificial Intelligence (AI) and Machine Learning(ML) Transforming Endpoint Security?

Introduction :

Artificial intelligence (AI) and machine learning (ML) are transforming endpoint security by enabling faster and more accurate threat detection, improved threat prevention, and more efficient incident response.

Endpoint security refers to the protection of endpoints, such as desktops, laptops, servers, and mobile devices, from cyber threats such as malware, viruses, and hacking attempts. Traditionally, endpoint security has relied on signature-based detection, which involves comparing files against a database of known threats. However, this approach is no longer effective against modern and advanced threats that are constantly evolving and mutating.

AI and ML are now being used to enhance endpoint security by enabling more advanced threat detection and prevention techniques. Machine learning algorithms can analyze large amounts of data, such as network traffic, system logs, and user behavior, to detect anomalies and identify potential threats. These algorithms can learn from this data and become better at detecting and preventing threats over time.

One example of how AI and ML are transforming endpoint security is through the use of behavioral analysis. Behavioral analysis involves monitoring user behavior and identifying anomalies that could indicate a potential threat. For example, if a user suddenly starts accessing sensitive files at unusual times or from unusual locations, this could be a sign of a compromise. Machine learning algorithms can be trained to recognize these patterns and alert security teams when they detect suspicious behavior.

Another example is the use of AI and ML in threat hunting. Threat hunting involves proactively searching for threats that may have evaded traditional detection methods. AI and ML can be used to analyze large amounts of data, such as system logs and network traffic, to identify patterns and anomalies that may indicate a potential threat. This can help security teams detect and respond to threats before they can cause damage.

Endpoint security refers to a methodology of protecting devices like laptops, mobiles and other wireless devices that are used as endpoint devices for accessing the corporate network. Although such devices create potential entry points for security threats still endpoints are becoming a more common way to compute and communicate than local or fixed machines. Such attacks tend to occur because a lot of data is outside the corporate firewall that exposes it to security threats. Some such threats to which our system is exposed constantly are phishing, spoofing, vishing, etc. Below you will find in detail description about the security attacks and the solutions provided by both Machine Learning and Artificial Intelligence.

1. Social Engineering

In such types of attacks, a person pretends to be someone else in order to trick users into disclosing confidential data, information or both. In order to prevent any kind of unauthorized access gain to confidential information, a cloud-based stack can protect against highly targeted script-based attacks including malware. ML and AI enhance the capabilities of this cloud network by supporting real-time blocking of new and unknown threats.

2. Phishing

It is one of the most common types of attacks aimed at stealing the victim’s personal information like banking account details. Attackers usually use spoofed emails that contain links directing the user to a malware-infected site. Such sites replicate genuine sites and trick the user into entering confidential details like passwords. AI and ML co-ordinate very well with each other in order to identify potential anomalies in emails. By analyzing the metadata, content, context of emails the system makes suitable decisions on how to tackle the malicious email. Using words like urgent and promotion in an email are picked by the AI systems as suspicious but the final decision is made after analyzing the email as a whole based on the following parameters. Whether there was a previous conversation, a connection between the subject and the content of the email, along with misspelled domains if any. ML-based protection continuously learns from such scenarios along with feedback data given to it by the user making the protection more accurate day by day.

3. Spear Phishing

It is a type of phishing but done in a more planned way by the attacker. The attacker first tends to do a background check on the user and then according to the users’ most common interests, most common visited websites and social media feeds the user is analyzed and is sent so-called credible mails which ultimately lead the target to open up little by little. Ultimately the user ends up downloading the malicious file. However, ML and AI make consistent efforts to tackle such kind of attacks. AI is used to understand the communication patterns which take place and if the system identifies an attack the ML-powered AI system block it before they cause any damage.

4. Watering Hole

Such attacks are based on the principle that a hunter uses for the prey to fall into the trap. In such attacks, the attacker tends to exploit the vulnerabilities of a website that is visited again and again by the user. ML and AI her us the path traversal algorithms for detecting any kind of malicious data. These traversal algorithms analyze if a user is directed to any kind of malicious website. For plotting such kind if attacks a lot of data from email traffic, proxy and pocket are required which is thoroughly scanned by the ml systems.

5. Network Sniffing

It is the process of capturing and analyzing the data packets that travel across the network. The network sniffer monitors all the data with the use of clear and readable messages being transmitted over a network. The best countermeasure to prevent sniffing is the use of encrypted communication between the hosts. VPNs are particularly used for encrypting the data. ML and AI-powered VPNs have however taken the protection to another level. ML-powered VPNs are equipped with a sophisticated learning algorithm that creates a private tunnel in the open networks like WiFi encapsulating and encrypting all the data sent on the network. This is done to prevent an attacker from deciphering the contents even if the data packets have been intercepted

6. DDOS Attack(Distributed Denial of Service Attack)

The principle of this attack although remains too straightforward but still, is effective today. It aims at causing interruption or suspension of a specific host or server by flooding it with large quantities of useless traffic(data) so that the server is not able to respond. Such flooding is done by multiple botnets(infected systems) simultaneously. DDOS is very effective because they are of lower bandwidth and hence they tend to bypass the detection quite easily and are often mixed with other attacks that also prevent them from the detection. However, AI-powered ML systems can instantaneously distinguish good traffic from bad traffic. This detection takes place within a few seconds that is the reason that such systems are preferred because they are quick, accurate and can analyze huge chunks of data in a very short interval of time. Although Machine learning and Artificial intelligence have revolutionized the security systems there is no denying the fact that they have drawbacks in certain areas. One of the drawbacks is that dealing with AI AND ML systems requires a lot of financial resources which a medium scale industry cannot bear to spend. Sometimes hackers may exploit artificial intelligence and use it against the user if a hacker is successfully able to foil the system tricking it into misidentifying or misclassifying certain objects due to modified inputs by an attacker. In simple terms, the attacker may trick the system into thinking about the absence of a particular security check and manage to open a device without a face id or a password. Certain ML-powered software can also mimic a person’s voice after listening to the voice for just some time. Such software is used for vishing. Vishing is a technique in which phishing is combined with voice. This attack involves caller ID spoofing that masks the real phone number with that similar to the target, making them believe in the genuineness of the caller and thus successfully carrying out the attack. Thus we can say AI AND ML act as double-edged swords while transforming the endpoint security.

Advantages :

Transforming endpoint security with artificial intelligence (AI) and machine learning (ML) offers several advantages:

1. Improved Threat Detection: AI and ML can analyze large amounts of data from various sources to identify potential threats. These algorithms can learn from this data and become better at detecting and preventing threats over time. This can lead to faster and more accurate threat detection, reducing the time it takes to detect and respond to cyber attacks.
2. Advanced Threat Prevention: AI and ML can be used to create more advanced threat prevention techniques that can proactively identify and prevent cyber attacks. By identifying patterns and anomalies in user behavior and network traffic, these algorithms can help security teams detect and respond to threats before they can cause damage.
3. Efficient Incident Response: AI and ML can help security teams respond to incidents more efficiently by providing real-time insights and automating repetitive tasks. This can free up security personnel to focus on more critical tasks, such as investigating and mitigating the impact of a cyber attack.
4. Cost-Effective: AI and ML can help reduce the cost of endpoint security by automating tasks that would otherwise require human intervention. By automating routine tasks, such as patching and updating systems, security teams can reduce the risk of human error and save time and money.
5. Scalable: AI and ML can be scaled to analyze large amounts of data from multiple endpoints and sources, making them ideal for large organizations with complex IT environments. This can help ensure that all endpoints are adequately protected and reduce the risk of a cyber attack.

Dis-advantages :

1. False Positives: AI and machine learning algorithms can sometimes produce false positives, which means they flag an event or activity as malicious even though it is not. This can lead to unnecessary alerts and require more time and resources from security teams to investigate and resolve.
2. False Negatives: Similarly, AI and machine learning algorithms can sometimes produce false negatives, which means they miss a malicious activity or threat that should have been detected. This can leave the organization vulnerable to a cyber attack, especially if the threat is advanced and persistent.
3. Dependence on Data: AI and machine learning algorithms require large amounts of high-quality data to operate effectively. If the data used to train the algorithm is incomplete or biased, the algorithm may not work as expected and produce inaccurate results.
4. Complexity: AI and machine learning algorithms can be complex and require specialized skills and knowledge to develop, deploy, and maintain. This can create a challenge for smaller organizations that may not have the resources or expertise to implement these technologies.
5. Cost: Implementing AI and machine learning technologies can be expensive, especially if the organization needs to invest in new hardware or software to support these technologies. Additionally, ongoing maintenance and training costs can add up over time.