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. You may also like this How AI and Space Technologies are Improving Daily Lives
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. You also like this 6 Ways AI and ML Together Transforming Endpoint security in 2020?
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.
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.
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.
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.
Imagine a scenario where you are visiting some websites and one of them seems to be a little slow. You might blame their servers to improve their scalability as they might be experiencing a lot of user traffic on their site. Most of the sites already take this issue into account beforehand. Chances are, they might be a victim of what is known as DDoS attack
In DDoS attack, the attacker tries to make a particular service unavailable by directing continuous and huge traffic from multiple end systems. Due to this enormous traffic, the network resources get utilized in serving requests of those false end systems such that, a legitimate user is unable to access the resources for himself/herself.
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