The unexpected growth of online systems and higher online traffic levels due to COVID-19 has led to a significant increase in malicious network activity. The same machine learning technologies that were used to improve systems security are now being used to attack them.
AI and machine learning are now becoming increasingly important to information security, as these technologies are capable of swiftly analyzing millions of data sets and tracking down a wide variety of cyber threats — from malware menaces to suspicious behaviors and phishing attacks. These technologies continually learn and improve, using past data and experiences and predicting new attacks that are likely to occur today or tomorrow.
TechRepublic reports: a midsized company gets over 200,000 cyber security alerts every day. An average team of security experts cannot deal with such a volume of threats. Therefore, some of these threats might naturally go unnoticed and this can result in severe network damages.
AI is the ideal online cybersecurity solution for businesses today. It provides security professionals with strong support and advanced technologies, thus, helping them to perform better and successfully protect their organizations from numerous cyber attacks. This article looks into the most common cybersecurity threats in 2021, the benefits of integrating AI into a company's cybersecurity, along with the experts’ and executives’ opinions on this matter.
Ransomware
Speaking of COVID-inspired increase in systematic criminal cyber security incidents, you can’t but mention Ransomware.
SonicWall’s 2021 report found that between 2019 and 2020, ransomware attacks rose by 62% worldwide, while in North America - by 158%, with such big events as the attack against US information technology firm Kaseya in July 2021.
Numerous sources report about 800-1,500 global businesses being affected by the attack, highlighting the need for improved recognition and automated response systems. A traditional target for ransomware presents hospitals and high value industries critically dependent on transport infrastructure. These include the mining, energy, construction industries, and even the transport industry itself, which has strong secondary effects on the local economy.
Phishing
The well-known phishing is a type of social engineering where an attacker sends a fraudulent message created to trick a potential victim and get their sensitive information or to install malicious software like ransomware on the victim's infrastructure.
The F5 Labs 2020 Phishing and Fraud Report estimated that the frequency of phishing attacks exploded by 220% during the lockdown peak.
With many attacks occurring in real time in time-critical multi-factor authentication (MFA) systems.
Network attacks
According to VMWare's 7th annual global report, the destructive cyber-attacks seeking to manipulate or destroy data choose organizations as their target half of the time, while the number of attempted network incursion incidents has almost doubled during the pandemic.
But even without the influence of the pandemic, Nation States, Cyberconflict and the Web of Profit study (HP Wolf Security, 2021) concluded that nation state attackers are 'stockpiling' zero-day vulnerabilities (some unknown weaknesses in network systems), and that over 40% of incidents had attacks on both physical and digital assets. This approach is commonly called 'hybridization'.
Meanwhile, the cyber security market is responding to the challenge with high investment. Mordor Intelligence predicts the cyber security market to grow to over $350 billion (at a CAGR of 14.5%) by the end of 2026; Gartner also has a positive forecast: enterprise investment in cloud security solutions will rise from $636 million in 2020 to $1.63 billion by the end of 2023. While Forrester predicts a great increase in spending on cloud security tools between 2018-2025: from $5.6 billion to $12.6 billion.
Artificial intelligence is at the top of this growth. The software side of machine learning for cyber security assurance is estimated to grow from $8.8 billion in 2019 to $38.2 billion by 2026.
Machine learning systems can analyze and recognize patterns of behavior using vast amounts of historical data with the help of a whole range of applications and processes in the cyber security sector. But nowadays, new threats may come from completely unexpected channels like a phone call or a VoIP chat, novel architectures, or can even be found inside machine learning systems themselves. Verizon's 2021 Data Breach Investigation report says that 85% of cyber security attacks target human credibility. While authentication systems have integrated numerous verification of biometric characteristics such as video or voice data, fingerprints and movement recognition, these advances are constantly pushing forward and creating new methods of data falsification. This signifies the response will need equal commitment and intensity of purpose.
The Advantages of AI in Cybersecurity
With new evolving cyberattacks and rapid growth in the number of devices happening today, AI and machine learning seem to be the only way to successfully deal with skillful cybercriminals, automate threat detection, and respond more efficiently than traditional software-driven or manual techniques.
Here are a few applications and advantages of using AI in the world of cybersecurity:
-Detecting New Threats
Traditional software systems simply cannot keep pace with the growing number of new attacks created every day, while that is where AI can do it better.
With its sophisticated algorithms, AI systems allow company systems to notice malware, run pattern recognition, and detect even the smallest signs of malware or ransomware attacks.
AI possesses predictive intelligence with natural language processing that effectively processes data also through articles, news, and studies on cyber threats available on the Internet - after all, cybercriminals also follow trends that constantly change.
AI-based cybersecurity systems are capable of better formulating vital prioritization decisions, while they are mostly based on what is most likely to be used to attack your network or systems.
-Dealing with Bots
From stolen credentials to bogus account creation and personal data usage, bots making up a huge part of internet traffic today can be a real threat.
It is hard to fight automated threats with just manual responses, while AI and machine learning build a thorough understanding of website traffic and know how to differentiate good bots (like search engine crawlers) from bad bots, and recognize real users.
AI helps us analyze huge sets of data and allows cybersecurity teams to update their strategy according to a constantly altering landscape.
-It Accelerates Detection and Response Times
Start with threat detection to start protecting your company’s network. It is better done through AI algorithms detecting things like untrusted data quickly, which saves you from the possible irreversible damage. AI scans the entire system and identifies threats extremely early while also simplifying your security tasks.
-It Secures Authentication
Once an attacker enters one user account, the whole network could be at risk. Most websites have a user account feature or contact forms where one puts personal information to get access to their services or buy products. Since it involves personal data and sensitive information, an extra security layer is essential to run such a site safely while also ensuring that your visitors are protected while browsing your network.
AI secures authentication anytime a user wants to log into their account, it uses various tools such as facial recognition, CAPTCHA, or fingerprints for identification. These procedures and the information collected helps to detect whether a log-in attempt is secure or not.
-Fighting with fakes
The civil and state authorities have been actively encouraging various methods to detect deep fakes since 2018, when the technology initially appeared.
Among the latest innovations in video deep fake detection there is examining the shape of pupils and eye shape. While for voice deep fakes AI algorithms search for artificial dialogue and key characteristics of audio fakes, even where notable background noise is present. But deep fakes are not necessarily limited to audio and facial data. New AI detection systems are being devised to discern faked road footage and even satellite maps.
-They Provide Better Endpoint Protection
The number of devices used for working remotely is fast increasing together with the need of securing all those endpoints.
Antivirus solutions and VPNs can help against remote malware and ransomware attacks, but they often work with signature definitions.
But a failure to update an antivirus solution or a lack of awareness from the software vendor can lead to serious threats. Signature protection may not be able to recognize a new type of malware attack.
AI-driven endpoint protection establishes a baseline of behavior for the endpoint using a repeated training process. If something unusual occurs, AI can take action right away (for example, sending a notification to a technician). This provides proactive protection.
-It Defeats AI-Based command and control systems
When a computer is affected by ransomware, the initiators in some way need to understand what's happening at the attack site. This means that the malware sends and receives signals — this is a traditional weakness of ransomware. Now it's possible to use AI even to manipulate random posts in fake social media accounts and keep these channels of communication open.
However, a well-organized network security framework can successfully recognize the patterns of change in the very same and potentially block any communication channels, having time to retrieve the compromised system.
What Cybersecurity Executives Think About AI
Reinventing Cybersecurity with Artificial Intelligence report created by Capgemini Research Institute strongly suggests strengthening cybersecurity defenses with AI across modern enterprises.
The majority of the survey’s participants (850 cybersecurity, IT information security, and IT operations executives from 10 different countries) agreed that an AI-enabled response is necessary, especially considering that cyberpunks are already leveraging AI technology for their cyberattacks.
Some of the report’s key results:
Networks become larger and data becomes more complex, while AI evidently provides better solutions for cybersecurity needs.
Conclusion
Experts agree that it is becoming too hard for humans to handle the growing complexities on their own, and sooner or later, the use of AI will become a necessity. It can perform high-level analysis and threat identification that significantly minimize breach risk and enhance security posture.
Moreover, modern AI helps find and prioritize risks, provide incident response, and identify malware attacks before they come into action. We suggest you take a decisive step towards your safety by adopting AI and strengthening your security infrastructure.
2021-10-28
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