** Abstract **
Cyberattack is becoming a critical issue of organizational information systems. Several cyber-attack detection and classification methods have been introduced with different levels of success that are used as a countermeasure to preserve data integrity and system availability from attacks. The classification of attacks against computer networks is becoming a harder problem to solve in the field of network security.
Introduction -: The rapid increase in connectivity and accessibility of computer systems has resulted in frequent chances for cyber attacks. Attack on the computer infrastructures is becoming an increasingly serious problem. Basically, the cyber attack detection is a classification problem, in which we classify the normal pattern from the abnormal pattern (attack) of the system. The subset selection decision fusion method plays a key role in cyber-attack detection. It has been shown that redundant and/or irrelevant features may severely affect the accuracy of learning algorithms.
What is Confusion Matrix ?
When we get the data, after data cleaning, pre-processing, and wrangling, the first step we do is to feed it to an outstanding model and of course, get output in probabilities. But how can we measure the effectiveness of our model? Better the effectiveness, better the performance and that is what we want. And it is where the Confusion matrix comes into the limelight. Confusion Matrix is a performance measurement for machine learning classification.
The true positive state is when the IDS identifies an activity as an attack and the activity is actually an attack. A true positive is a successful identification of an attack.
The true negative state is similar. This is when the IDS identifies an activity as acceptable behavior and the activity is actually acceptable. A true negative is successfully ignoring acceptable behavior.
Neither of these states is harmful as the IDS is performing as expected.
False-positive is the term used to indicate a file or item that is marked as malicious, but, in fact, isn’t.
False-negative is the opposite. It happens when a malicious file or item is labeled as secure, clean.
False-positive and false-negative are errors and failures found in protection solutions that fail to label files and items correctly. Now let's talk about the above two in little detail as they are very important. False-negative in information security
In the case of a false negative, a malicious file or item gained access to your system or network because it was classified as legitimate by your protection solution. Let’s make a comparison using email.
Imagine that your company received an email that contained a virus or ransomware attached. Since you received the message, obviously, the email security solution that your company uses didn’t detect the threat. But why didn’t my email security solution issue an alert? How did the threat go unnoticed? The main reason for false-negative occurrence refers to a new threat or, as we say, a zero-day attack.
That is, recent attacks are more difficult to combat, as cybercriminals are constantly searching for new ways to attack, lure and lie. False-positive in information security
As we have said, a false positive is a flaw that scanning and protection software generates when a legitimate activity is classified as an attack.
Invariably, false-positive results in a website, file, or item being quarantined, blocked, or deleted. At first, a false positive may not seem as harmful as a false negative. But think long term. What losses would you have, for example, if your email protection solution blocked emails from new customers?
There is a good comparison between a false positive and a fire alarm. Imagine that the fire alarm went off, everyone ran, but it was nothing. False alarm. Now count the time and energy that was spent on this process. That’s why, in the long run, a false positive can be as harmful as a false negative.
The most common cause of false positives is when the software identifies a signature or behavior of a file as being similar to that of a threat, such as malware. How to prevent false positive and false negative
There are several approaches to consider when it comes to reducing the number of bogus security threats, including network analysis, enacting policies that reduce the opportunity for cyber-attacks, beefing up your overall security measures, and taking a look at how modern AI technology could help.
Analyze Network Traffic
Look through information in your network logs to spot unfamiliar usernames, odd connection details, and suspicious trends in the duration and frequency of communication to uncover security threats the old-fashioned way. You may be able to detect more false negatives than if you hadn’t looked, but this process is prone to suffer from human error and can become quite time-consuming.
Limit Network Access on IoT Devices
As a matter of protocol, consider implementing a policy that limits the network access of IoT devices. These devices have become common targets for cybercriminals looking for a way in, and they typically don’t require much access to function properly. When IoT devices are given restricted network access, your security software is more likely to recognize unusual behavior and should issue more accurate alerts.
Use Web Application Firewalls
A large percentage of data breaches are targeted at web application vulnerabilities. While the commonly deployed Web Application Firewall can reduce these instances, this type of firewall can hog network resources when used to detect false negatives and positives. The related slowdown can reduce the firewall’s effectiveness in quickly alerting staff to authentic threats or slow network traffic to an unacceptable level.
Research Artificial Intelligence Solutions
Tackle the problems associated with false positives and negatives through the power of modern AI advancements. Its context-aware AI monitors your network to gain a baseline understanding of your systems and how they have been used. Equipping your network security team with a tool that will help them better analyze emerging trends and widespread security threats will leave your company less vulnerable to actual security breaches.