Use AI to fight AI-powered cyber-attacks

April 6, 2021  |  Irfan Shakeel

This blog was written by an independent guest blogger

"AI is likely to be either the best or worst thing to happen to humanity.” ~Stephen Hawking

Cyber-attacks are commonly viewed as one of the most severe risks to worldwide security. Cyber-attacks are not the same as they were five years back in aspects of availability and efficiency. Improved technology and more efficient offensive techniques provide the opportunity for cybercriminals to initiate attacks on a vast scale with a higher effect. Intruders employ new methods and launch more comprehensive strategies based on AI to compromise systems. Similarly, organizations have started using robust defense systems that use Artificial Intelligence (AI) to fight AI-powered cyber-attacks.

AI in the security world

Security professionals spent a lot of time researching how to use AI to exploit its abilities and integrate them into technology solutions. AI enables defense methods and services to identify and respond to cyber threats. The use of AI in security has proven to be beneficial. According to many IT professionals, security is the main reason for AI adoption in corporations. Not only does artificial intelligence increase overall cybersecurity, but also it automates identification and mitigation operations.

According to a Capgemini Research Institute, 69% of corporations agree that AI is vital for security because of the growing number of attacks that traditional methods cannot prevent.  According to the findings,

  • 56% of companies say that security experts are overstressed.
  • 23% say they are unable to prevent all attacks.

According to a TD Ameritrade study, registered investment advisors (RIAs) are more ready to spend in emerging artificial intelligence security projects. With these funding possibilities, the AI cybersecurity industry will grow at a 23.3% CAGR from $8 billion in 2019 to $38 billion in 2026.

Organizations use security information event management (SIEM) for threat detection to capture a large amount of data from across organizations. It is impractical for a user to go through such information to identify possible vulnerabilities. Moreover, artificial intelligence helps search for anomalies throughout technology and user tasks. AI-based methods efficiently scan across the system and compare different information sources to detect vulnerabilities. Anomaly detection is a domain where AI is helpful in a companys’ security defense. It also finds various functionalities to prevent attacks by looking at past incidents (Machine Learning).

Applications of AI in security

AI in Antivirus Services

Antivirus software with artificial intelligence detects network oddities of processes that behave suspiciously. AI antivirus detects and prevents network assets from exploit when malicious software is launched in a network.

Modeling user behavior

AI simulates and assesses the behavior of network users. The aim of evaluating how users engage with the system is to spot overthrow attempts. AI also observes the users’ actions and identifies odd behavior as anomalies. When a new user logs in, AI-powered machines can identify suspicious activities and respond by disabling the user or by notifying system administrators.

Automated network and system analysis

Automated analysis of network information assures continual assessment and early detection of suspected cyberattacks. Attackers use command and control techniques to avoid detection by network security. For example, to get around firewalls and IDS/IPS, information is embedded in DNS queries. Anomaly detection, pattern comparison, and data tracking are all used by AI-enabled cybersecurity. Therefore, they can detect a large number of network or system attacks.

Cybercriminals use AI to maximize their hacking results

AI Risks

AI flaws can result in overflowing, wherein AI acts independently - either too particular or too generic. Inadvertent prejudice happens in AI technologies, and developers or specific data sources can embed it. Such risks originate from unintended development and execution mistakes. However, when individuals try to circumvent artificial intelligence or use them as weaponry, a new set of concerns arises.

These risks can be remedied by creating human supervision, rigorously evaluating AI systems during the layout stage, and actively observing those technologies once they are up and running.

AI manipulation

Hackers can tamper with sets of data used to train AI or create circumstances that can cause suspicions while slowly training AI in an intended way.  When hackers do not have accessibility to datasets, they can use deception to compel errors via altering inputs.

AI systems manipulate users into misidentification by altering input information to make passable authentication difficult. Hackers try reverse engineering  AI systems to determine the dataset used to train AI systems. Hackers get insights into confidential information, enabling poisoning datasets, or replicating AI systems.

AI weaponization

AI also helps attackers increase the scope and efficiency of social engineering. AI tries to identify anomalies in users’ behavior, allowing it to persuade them to undermine systems and hand over confidential information by convincing them that a clip, call, or mail is legal. AI enhances all social strategies presently attackers use.

AI detects potential threats in networks, computers, and applications as they arise. Finding potential opportunities for human hackers makes the task of data security much more difficult. Real-time analysis of all network access and operation, and rapid patching, are crucial in combating these attacks.

AI-powered cyber-attacks

According to a Forrester survey, 88% of security experts believe AI-powered attacks will become common in the coming years.

  • Attackers targeted TaskRabbit, an online platform for freelancers, one of the most prevalent AI-powered cyber-attacks. In April 2018, 3.75 million website users had their credit card and banking information stolen from their user database. Attackers used massive zombies controlled by AI to launch a DDoS attack on TaskRabbits’ servers.
  • Last year, an attacker used AI to imitate a CEOs’ voice in a phone call, defrauding a UK energy organization of £200,000.
  • For social media behemoths, AI-manipulated “deepfake” material built to propagate disinformation is a significant concern.

Comparison with traditional security

  • Regularly, organizations struggle to prioritize and handle the vast number of emerging loopholes they experience. Conventional vulnerability management techniques recommend waiting for elevated loopholes to be exploited before addressing them. A traditional vulnerability database is vital for managing and containing identified vulnerabilities. Although AI methods such as User and Event Behavioral Analytics (UEBA) analyze baseline behavior of user-profiles and servers, an unusual activity can indicates an unknown zero-day attack.
  • Developing cybersecurity policies and knowing an organizations’ network topology are two time-consuming factors of traditional network security. Using AI to enhance network security by understanding network traffic behavior is useful, suggesting both operational aggregation of tasks and privacy policy.
  • Many vital data center procedures, such as cooling filters, energy consumption, backup power, and bandwidth consumption, can be optimized and monitored with AI. AI analytical skills and constant monitoring skills reveal which values will enhance the efficiency and protection of infrastructure. AI will lower the price of system maintenance as it will notify us whenever the machinery needs to repair. After introducing artificial intelligence inside data centers in 2016, Google revealed a 40% decrease in cooling efficiency and a 15% reduction in energy usage at their site.
  • Conventional security methods rely on signatures or indicators of vulnerability to detect attacks. This approach may be efficient against already known attacks, but it is ineffective against attacks that have yet to be found. About 90% of attacks can be detected using signature-based methods. Conventional methods can be substituted with AI to improve tracking rates by up to 95%, but there will be an eruption of false positives. Combining traditional security methods with AI is the best approach.

AI is the answer to AI-based attacks

To attack organizations and undermine their information, attackers employ new and highly innovative methods. The use of innovations such as artificial intelligence (AI) in cyber threats is becoming more prevalent. Regrettably, AI is not only accessible to good hackers or those protecting their systems and computers. It is accessible for unethical hackers too.

Simulating user behavior is one of the fascinating methods that cybercriminals utilize in AI. They can prevent detection if they can blend in with the distortion; making their operations seem to be usual user behavior. AI-based attacks identify and imitate authentic user behavior to hide threats from conventional security controls. Actions suggested:

  • Security experts need to plan for a futuristic AI software system that can evaluate all potential threat vectors, choose the right strategy, implement effectively, and locate malware.
  • Use AI software to combat AI when tracking logs.
  • AI security log analysis is a great technique to look for anomalies. It can find for and generate predictive insight using a very large number of factors, resulting in predictive capabilities.
  • Organizations need to assess how AI attacks are likely to be used against their AI system. And then develop response plan strategies to mitigate the impact.
  • Natural language processing can be used to gather data on previous and current cyberattacks, collect threat data, and improve privacy features.
  • AI automation of operations relieves the human experts’ workload and optimizes response time.

All the evidence suggests that only AI can fight or stop AI based cyber-attacks.

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