The crisis created by the sudden increase in automated phishing attacks is not ruled out. And this is happening with more concrete content and greater accuracy due to Artificial Intelligence (AI), machine learning and Big Data. And when IT leaders are using AI for security at the next level, what if this technology falls into the wrong hands – the bad guys?
The dawn of the Internet and advancing computing means that we are able to trigger precise solutions to complex problems in various fields – from astrophysics and biological systems to automation and precision. In this fast-paced world where new ways to wink, cyber security is at the top, especially for companies with data rich transformations such as the Internet of Things (IoT).
To a large extent, malware to detect network abnormality, and cyber signature to detect rule-based systems rely on file signatures. Security often stems from real virus outbreaks – as security experts isolate malicious files and identify unique signatures that help other systems become alert and immune. The same is true for a rule-based system: rules are set based on the experience of potentially malicious activity, or the system is shut down to restrict any access to remain on the safe side. The only problem with these approaches is their reactive nature. Hackers always find innovative ways to bypass known rules. It is often too late before a security expert detects a breach.
Cyberspace shaken by AI
Traditional malware is designed to perform its harmful functions on every device in which they find their way. An example is the outbreak of noteti ransomware, in which hundreds of thousands of computers were infected in no time. This method works when the attacker’s goal is to inflict maximum damage. It is not as effective if an attacker has a specific goal in mind.
But the advent of disruptive technologies like Artificial Intelligence mean that our tools and applications are understanding us better. For example, an iPhone X uses AI to automatically detect faces. While this is a major feature, it creates a complex puzzle where sensitive data is more likely to go into the wrong hands. Today, hackers are seen using the same technology to develop smart malware that can pinpoint millions of users and target targets.
A: Game-changer in security
With each passing year, attacks are becoming more personal, with a greater chance of success. Hackers have also started using AI to speed up polymorphic malware, causing frequent changes in the code and making it undesirable. The advanced strategy allows hackers to work around security to bypass face protection and spam filters, promote fake voice commands, and anomaly detection engines.
The good news is that this intelligence is also used to protect infrastructure. What makes AI cybercity unique is its adaptability. Intelligent cyberspace does not need to follow specific rules. Rather, it can see and learn patterns. Even better, AI can be directly integrated into everyday security devices – such as spam filters, network intrusion and fraud detection, multi-factor authentication, and incident response.
AI has truly become a game-changer for cyberspace. To make cyber security more effective, here are several specific areas where artificial intelligence can help:
Machine learning – AI and machine learning (ML) are two different worlds; In fact, machine learning can be considered a subset of AI, which is mainly used to increase intelligence. When it comes to improving its cyber security, it automatically fills skills gaps to prevent cyber attacks. If any malicious software is detected on the network, an automated incident response is sent. In addition, specific AI bots completely block access to websites. By preventing such actions, AI improves the security of an organization or individual on the Internet.
. Artificial intelligence has the ability to identify the correct data that yields the best results.
Merged Technological and Humanitarian Approach – The most powerful security approach combines the power of AI and human intervention. Machine learning is a great example. Artificial intelligence helps to break down complex automated processes for detection and proper response to attacks. But the ultimate challenge is producing measurable results in ways that can then predict and detect attacks – and later analyze and prevent them.