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  • How Artificial Intelligence and Machine Learning Transform Endpoint Security?

    The term endpoint security relates to the methodology to protect devices like laptops, smartphones and other wireless devices which are used as endpoint devices for accessing our corporate network. Many would argue that these devices are the entry points for security breaches, but endpoints are now becoming a more common way to communicate and compute than fixed or local machines. We are subject to these breaches and threats because a lot of data is outside the corporate firewall that is exposed to these threats. Some of the most common threats that our system is exposed to are vishing, phishing, spoofing etc.

    Below mentioned are some information about security threats and the solution provided by Artificial Intelligence (AI) and Machine Learning (ML).

    1. Social Engineering
    In these types of security breaches, people are conned by criminals as they pretend to be someone else in order to extract some vital information, confidential data or both. To combat any kind of unauthorized access gain to sensitive information, a cloud-based stack should be placed to protect against highly targeted script-based attacks including malware. AI and ML enhance the capabilities of this cloud system as it supports real-time blocking of new and unknown threats. Nowadays many AI application development services are aimed at the prevention of such breaches.

    2. Phishing
    Phishing is now one of the most common types of attacks aimed at stealing the victim’s personal information like their banking details. Attackers often use fake emails that contain links directing the users towards a malicious and inflicted site. These sites are usually a replica of genuine sites and trick the users into entering confidential information such as passwords. AI and ML work very well together in order to identify any discrepancies in emails. Since AI and ML can efficiently go through huge amounts of data, they are ideal to analyze metadata, content and context of these emails and take suitable actions against these malicious emails. Keywords like urgent and promotion are picked up by the AI system as suspicious emails, however, the decision is made only after analysing the whole email and the following parameters. Whether there was a prior conversation between the subject and the content of the email, and if there are any misspelt domain names if any. ML-enabled protection continuously learns from such scenarios along with feedback given to it by the user. This makes security more and more strong day by day.

    3. Spear Phishing
    Spear Phishing is a much more organized way to attack. The attacker in this case has already done a background check on the user, knows the users most common interests, most common sites visited and analyzed the social media feeds. The users are then sent so-called credible mails which lead the victim to open little by little. Ultimately the user ends up downloading the malicious file. AI and ML help in tackling these kinds of attacks. AI is used to understand the communication patterns between the victim and the attacker, and if the system suspects an attack the ML-powered AI system blocks it before causing any harm.

    4. Watering Hole
    A watering hole is a type of attack based on the principle used by a hunter where its prey falls into a trap. Here the attacker tends to exploit the weakness and vulnerabilities of a genuine website which is visited again and again by the user. ML and AI use path traversal algorithms for detecting any kind of malicious data. The traversal algorithms analyse if a user was directed to any type of malicious website. To plot such a type of attack a lot of data from a proxy, email traffic and pocket are required which can be prevented when scanned inside out by the ML system.

    5. Network Sniffing
    Network Sniffing is the process of analyzing the data packets that travel across a given network. The network sniffers regularly monitor all the data with the readable and clear message being transmitted over a network. The best way to tackle this problem is by using encrypted communication between the hosts. VPNs (Virtual Private Network) are used for encrypting the data. With ML and AI-powered VPNs the protection has elevated to a whole new level. These VPNs are equipped with a smart algorithm that creates a private passage in the open network like WiFi encapsulating and encrypting all the data sent on the network. This is done to stop the attacker from deciphering the information even when the data packets have been intercepted.

    6. DDOS Attack (Distributed Denial of Service Attack)
    DDOS Attack to this day remains straightforward but still is effective. Its goal is to cause interruption or suspension of a specific host or server by overwhelming it with large amounts of useless traffic (data) making the servers unresponsive. Such flooding is done by simultaneously using multiple botnets (infected systems) DDOS is effective as they lower the bandwidth and tend to bypass the detection easily and are often compiled with other attacks that also stop them from detection. AI-powered ML systems can easily differentiate between good and bad traffic. This detection works in a matter of seconds and that is the reason that such systems are being opted as they are accurate, quick and can easily analyze huge amounts of data in a short period of time.

    Conclusion
    AI and ML are changing the total landscape of Endpoint Security for its betterment, however, it goes without saying that there are some drawbacks in certain areas. One of the major drawbacks is that dealing with these systems requires a lot of financial support, which is an uncommon sight for the medium scales industry. If you looking to improve your endpoint security, do book a Free, no-obligation consultation with us, today.


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