The other side of the coin
To tell the truth, there are many breeds of dogs, they are entrusted with the protection of a wide variety of lands, and there is simply no unique recipe for one dog to protect a house and garden of five hundred square meters. Everything is individual. In some places, one dog can handle it, in others, you will have to have five or ten of them. There are many questions for trainers. Yes, and thieves are becoming more and more resourceful and cunning. In the system of protection against cyberattacks, everything is about the same. The accuracy of detecting abnormal behavior on the Internet using neural networks can vary depending on many factors, including the type of data, the nature of the anomalies, and the quality of the training data. Much depends on specific systems and solutions. For example, in network anomaly detection, where neural networks analyze traffic, the accuracy can be high, especially when using deep learning. However, it is worth noting that the result may not be so significant in cases where abnormal behavior is difficult to identify due to high variability in the data or due to new and unknown types of threats. In such situations, it is important to appropriately train neural networks and systematically update their models to adapt to new threats.
There is another very important aspect! In denmark mobile database for the accuracy of anomaly detection, we accept certain risks. It is important to understand that achieving 100% results in cybersecurity is almost impossible due to the constant evolution of cyber threats and potential uncertainty in data. Therefore, it is often important to implement additional measures and incident response systems. This will minimize the impact of attacks even with imperfect anomaly detection.
Let's try to put everything in its place. Ensuring cybersecurity using artificial intelligence, unfortunately, faces several significant challenges and obstacles:
Evolving threats. Cyber threats are constantly evolving, and attackers are becoming more sophisticated. AI systems must constantly adapt to new types of attacks and threats to remain effective.
Data reliability. ML and AI require large amounts of data to train models. However, if the data is inaccurate or subject to manipulation, it can lead to unreliable results and incorrect conclusions.
False positives. Using AI in cybersecurity can cause false positives, where legitimate activity or traffic is incorrectly identified as abnormal or malicious. This can create unnecessary anxiety and the cost of additional testing.
Data privacy. Handling large amounts of data in cybersecurity can raise privacy and confidentiality issues. Compliance with data protection regulations and laws is inherently a challenge, especially when using personal data.
Well, and finally, a few words about the role of personality. Or personalities. Unfortunately, today we can see a shortage of experts in the cybersecurity market. The development and configuration of AI systems requires qualified specialists. However, the lack of professional personnel in this area can complicate the implementation of all the above solutions. In addition! Ill-wishers do not sit still and use every opportunity. For example, fake training and attacks on models. Attackers can try to attack AI systems by deceiving models, subjecting them to attacks, or presenting false data that the model may accept as correct during training.