In this day and age, identity theft is a real worry. With data breaches happening on a regular basis, your personal information is at risk.
Data breaches can happen when hackers gain access to computer systems and sensitive data. Many times, these breaches include records of personal information like social security numbers and banking information.
Companies that experience a data breach are forced to notify their customers that their information has been compromised. This is due to laws put into effect for consumer protection.
Identity protection services offer protection and recovery for individuals in the event of a data breach. They provide you with updated credit reports, new social security numbers, and more to keep you safe.
However, there is something called an false positive that happens in identity protection. This article will discuss what an false positive is, how it can happen, and how to prevent it.
Causes of false positives
There are several common causes of false positives. Some of these are vulnerabilities that are not currently being exploited but are likely to be in the near future, inefficient and incomplete patches, and new, unknown threats.
Vulnerabilities that are not currently being exploited but are likely to be in the near future may be detected by IDPSs, but since there is no known exploit for it, the system will report it as a false positive.
Incomplete patches can also result in false positives. If a company patches a certain vulnerability, but does not install the full fix provided by the company that discovered and released it, then there is a chance that your IDPS will report a false positive.
Unknown threats can also cause false positives. If an organization does not know about an emerging threat and thus does not include it in their IDPS, then your organization may detect a false positive because of this.
How to reduce the risk of false positives
While some false positives may be unavoidable, you can take several steps to reduce the risk of idps false positives. First, you can set up a training mode where the device receives simulated attacks so it can learn how to respond appropriately.
Second, you can set up a feedback system where other devices check if the device is responding correctly or not. If it is not, then it is notified that there was a problem and needs to respond differently next time.
Third, you can have a very high threshold for what constitutes an attack. For example, if an attack sends 100MB of data per second, then the IDP could only allow 100MB per second of legitimate traffic. This would effectively lower the chance of false positives due to traffic being blocked.
Understanding the definition of a false positive
A false positive is defined as a system or device incorrectly identifying an event as a threat. This means that there is actually no threat, but the system thinks there is.
For example, a security system that alerts you when someone enters your property believing it is a trespasser when actually it is a wild animal would be considered a false positive.
False positives can be very dangerous in the world of cybersecurity. Due to the growing number of cyberattacks and the ever-evolving nature of this domain, most anti-virus software updates several definitions every few hours.
This means that your computer or device is constantly being checked for something new that could be a threat. If your system did not have the ability to recognize this as not a threat, then your entire device would be blocked.
Unfortunately, this can also go the other way. An idps system may fail to react to an actual attack event.
Understanding the causes of false positives
A false positive can occur for a variety of reasons.
Avoiding common mistakes with idps systems
While your security strategy should always be dynamic, it’s important to learn from others’ mistakes in order to avoid common errors that may put you at risk.
There are many examples of security breaches, some of which were mentioned above, so take some time to reflect on them and see if there is any new knowledge you gain from them.
For example, since social engineering is such a prevalent threat, one thing you can do is have a policy in place that requires two people to confirm a new employee’s identity prior to granting them access to any secure areas or information.
This way, if someone tries to use fake ID’s or forged documents, there are two people who verified their identity, making it more difficult for a malicious actor to succeed with their plan.
Similarly, having safety protocols in place for responding to an attack will help prevent further damage and casualties.
Using software to detect attacks and reduce false positives
As cybersecurity measures and threats continue to evolve, so must the solutions designed to protect users.
One of the most promising developments is the use of artificial intelligence (AI) software in IDPS systems. AI detects attacks by “learning” how to recognize normal network activity and behavior.
If there is a change in this behavior or activity, AI can detect it and respond appropriately. This ability to learn makes AI extremely powerful and applicable across many situations.
For example, if a user has a certain routine for logging into their account, the AI software can recognize this pattern and confirm that it is actually that user. If there is any deviation from this pattern, then it may flag it as a potential attack.
Similarly, if an attack occurs that results in abnormal network activity, the AI will be able to recognize this and respond accordingly.
Using hardware to detect attacks and reduce false positives
Another way to reduce false positives is to use hardware-based intrusion detection systems (HIDS). These systems use computer hardware to monitor system activity.
They log events in memory, so they cannot be erased. Many deployed worms and viruses attempt to erase the logs of their activity, but HIDS store them in memory, so this is impossible.
Because they rely on memory, however, a cyberattack that wipes out the system may cause problems with the HIDS. There are solutions for this problem, but it is something that needs to be addressed.
A third type of IDS is a hybrid system. This uses both software and hardware components to detect attacks. By combining the two, you can further reduce false positives while still having a solid IDS system.
Using human review to detect attacks and reduce false positives
Another method for reducing the number of false positives in your IDPS system is to use human review. Once your system has automatically determined an event is a cyberattack, you can have a team of people review it to be sure.
This team can be made up of people from various departments within your organization, including IT, security, and communications.
They can then decide whether the event was truly a cyberattack or if it was just something normal that the IDPS flagged as suspicious.
This process is called whitelist filtering and is very effective at reducing the number of false positives in your system.
Whitelist filtering works by having people on the team check if an action is normal or not before flagging it as a cyberattack. If it is found to be normal, then it is not flagged as a false positive.
