Why is the future of cyber security artificial intelligence?
As the world goes digital, security is becoming an increasingly important student. Threat actors use smarter, more automated attacks, and businesses are forced to resort to advanced technology. This is where artificial intelligence (AI) comes into play.
It is a sad reality that there is a growing picture of cyber security threats in enterprises and beyond. As cyber attacks increase in volume and complexity, cyber security officers are struggling to catch up. As of January 17, 2021, Google has registered more than 2.1 million phishing sites. This statistic is an increase of 27% in the last 12 months. Ransomware attacks have increased exponentially since the beginning of 2021, with an increase of 41% in attacks and 93% annually (checkpoint).
The Challenges of AI
Surprisingly, another concern is artificial intelligence itself. It is not easy to forget that in 2019, cyber attackers used artificial intelligence-based software to imitate the voice of the CEO and demand $ 243,000 in counterfeit money transfers. This is just one example of artificial intelligence attacks. Take a look at MIT Tech Review's report on "offensive artificial intelligence", which is included in the specifications and is expected to grow and become more complex as an exponent. It is worrying that individualized attacks will be under the auspices of deep learning, which allows artificial intelligence to further increase individualization.
The concern about the relevance of ransomware is that artificial intelligence allows cybercriminals to create malware that can self-detect system vulnerabilities and independently determine which payloads will be more successful.
The relationship between cyber security and AI
The relationship between cyber security and artificial intelligence can be divided into four globally accepted and widely accepted categories: "Attacks using AI"; "Autonomous attacks by AI"; "Attacks on AI"; and "Security measures using AI."
Attacks using AI
Cyber attackers use artificial intelligence for cyber attacks. Such attacks actually occur in the real world.
Autonomous attacks by AI
Artificial intelligence can carry out cyber attacks autonomously without human intervention. However, under the current AI model, this category does not yet exist. Since it is technically possible for artificial intelligence to carry out cyber attacks autonomously without human intervention, one of the challenges will be to share responsibility for civilian damage caused by cyber attacks.
Attacks on AI
This category includes cyber-attacks against artificial intelligence and so-called "competitive learning"; for example, a cyber attacker can transmit fake information to artificial intelligence. Such an attack could be realistic in the future if people's participation in artificial intelligence monitoring is reduced and the use of artificial intelligence for important decisions (eg, medical diagnostics and investment decisions, etc.) is generalized.
Security measures using AI
This category includes defenders who use artificial intelligence against cyber attacks. Various attempts have already been made, such as automating the detection of malware. Today, people continue to be responsible for identifying issues that need to be addressed by artificial intelligence and for AI to interpret decisions. Therefore, it is necessary to develop human resources that can fully use artificial intelligence. "Security measures using AI" are discussed in more detail below.
Benefits of using artificial intelligence:
Artificial intelligence can detect threats in a network or system early. It can analyze user behavior faster and more widely than humans. A recent study by Capgemini reinforces this point, showing that 69% of organizations view artificial intelligence as an integral element in responding quickly and in time to cyberattacks. Here, AI can draw an example and a point when behavior is out of the norm. Self-mapping (SOM) algorithms essentially "model" normal data to determine whether a particular activity in a network or computer system is normal or abnormal. SOM is a type of machine learning to map connections - some often refer to this type of learning as creating a signature to capture behavior between input data. This helps identify areas of exposure in the system or network, keeping attackers dead in their tracks.
Artificial intelligence can speed up the detection of online threats. For example, machine learning can help detect fraud because machine learning algorithms can learn from historical fraud patterns and detect them in future operations. This is not just a case of fraud. Take malware as an example. Artificial intelligence trained in various malware previously detected can predict what types of malware will appear online in the future. When the system learns to detect similarities in patterns, it can cross-reference a new type of malware against the database used, and thus react depending on previously successful block attempts.
Artificial intelligence is the best competitor to AI-controlled cyberattacks. Unlike traditional cybersecurity approaches, which often remain stable once implemented, AI (like in-depth learning) can be highly scalable. This is important, for example, for AI-based malware that can spread and mutate frequently. Artificial intelligence can reach hundreds, thousands, and even millions of learning patterns, meaning that as the set of trained data grows, the AI solution can continuously improve its ability to detect anomalies.
Not surprisingly, many people see the future of cybersecurity as a "war of algorithms" and an "artificial intelligence attacking defense AI." Attacks will be wider and more complex in the future. Thanks to artificial intelligence, they will grow more secretively, faster and more efficiently. Finally, if businesses can incorporate artificial intelligence, especially machine learning, into their cyber defenses, they will be able to protect themselves not only from today's diverse and high-level cybersecurity attacks, but also from tomorrow's extremely intelligent threats.