Automated cyberattacks siv AI: Thaum cov tshuab ua cybercriminals

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Automated cyberattacks siv AI: Thaum cov tshuab ua cybercriminals

Automated cyberattacks siv AI: Thaum cov tshuab ua cybercriminals

Subheading ntawv nyeem
Lub zog ntawm kev txawj ntse txawj ntse (AI) thiab kev kawm tshuab (ML) tau siv los ntawm cov neeg nyiag nkas los ua kom cyberattacks zoo dua thiab ua rau tuag taus.
    • Author:
    • Tus sau lub npe
      Quantumrun Foresight
    • Cuaj hlis 30, 2022

    Cov ntsiab lus ntawm kev nkag siab

    Artificial intelligence (AI) and machine learning (ML) are increasingly being used in cybersecurity, both for protecting systems and in executing cyberattacks. Their capability to learn from data and behaviors enables them to identify system vulnerabilities, but also makes it hard to trace the source behind these algorithms. This evolving landscape of AI in cybercrime raises concerns among IT experts, requires advanced defense strategies, and may lead to significant changes in how governments and companies approach cybersecurity.

    Automated cyberattacks siv AI ntsiab lus

    Artificial intelligence and ML maintain the ability to automate nearly all tasks, including learning from repetitive behavior and patterns, making a powerful tool to identify vulnerabilities in a system. More importantly, AI and ML make it challenging to pinpoint a person or an entity behind an algorithm.

    In 2022, during the US Senate Armed Services Subcommittee on Cybersecurity, Eric Horvitz, Microsoft’s chief scientific officer, referred to the use of artificial intelligence (AI) to automate cyberattacks as “offensive AI.” He higlighted that it’s hard to determine if a cyberattack is AI-driven. Similarly, that machine learning (ML) is being used to aid cyberattacks; ML is used to learn commonly used words and strategies in creating passwords to hack them better. 

    A survey by the cybersecurity firm Darktrace discovered that IT management teams are increasingly concerned about the potential use of AI in cybercrimes, with 96 percent of respondents indicating that they’re already researching possible solutions. IT security experts feel a shift in cyberattack methods from ransomware and phishing to more complex malware that are difficult to detect and deflect. Possible risk of AI-enabled cybercrime is the introduction of corrupted or manipulated data in ML models.

    An ML attack can impact software and other technologies currently being developed to support cloud computing and edge AI. Insufficient training data can also re-enforce algorithm biases such as incorrectly tagging minority groups or influencing predictive policing to target marginalized communities. Artificial Intelligence can introduce subtle but disastrous information into systems, which may have long-lasting consequences.

    Kev cuam tshuam

    Kev tshawb fawb los ntawm Georgetown University cov kws tshawb fawb ntawm cyber kill chain (ib daim ntawv teev npe ntawm cov haujlwm tau ua los ua kom muaj kev vam meej cyberattack) tau pom tias cov tswv yim kev tawm tsam tshwj xeeb tuaj yeem tau txais txiaj ntsig los ntawm ML. Cov txheej txheem no suav nrog spearphishing (e-mail scams qhia rau cov neeg tshwj xeeb thiab cov koom haum), taw qhia qhov tsis muaj zog hauv IT infrastructures, xa cov lej phem rau hauv tes hauj lwm, thiab zam kev tshawb pom los ntawm cybersecurity systems. Kev kawm tshuab kuj tseem tuaj yeem ua rau muaj feem cuam tshuam txog kev sib raug zoo engineering ua tiav, qhov twg tib neeg raug dag ntxias kom nthuav tawm cov ntaub ntawv rhiab lossis ua haujlwm tshwj xeeb xws li kev lag luam nyiaj txiag. 

    Tsis tas li ntawd, cyber kill chain tuaj yeem siv qee cov txheej txheem, suav nrog: 

    • Kev soj ntsuam dav dav - autonomous scanners sau cov ntaub ntawv los ntawm lub hom phiaj network, suav nrog lawv cov kev sib txuas, kev tiv thaiv, thiab kev teeb tsa software. 
    • Kev siv riam phom loj - AI cov cuab yeej txheeb xyuas qhov tsis muaj zog hauv kev tsim kho vaj tse thiab tsim cov cai kom nkag mus rau cov qhov khoob no. Qhov kev tshawb nrhiav tsis siv neeg no tuaj yeem tsom rau cov ecosystems lossis cov koom haum tshwj xeeb. 
    • Kev xa lossis nyiag khoom - AI cov cuab yeej siv automation los ua spearphishing thiab social engineering txhawm rau tsom ntau txhiab tus neeg. 

    As of 2023, writing complex code is still within the realm of human programmers, but experts believe that it won’t be long before machines acquire this skill, too. DeepMind's AlphaCode is a prominent example of such advanced AI systems. It assists programmers by analyzing large amounts of code to learn patterns and generate optimized code solutions​

    Kev cuam tshuam ntawm automated cyberattacks siv AI

    Kev cuam tshuam dav dav ntawm kev siv cyberattacks siv AI tuaj yeem suav nrog: 

    • Cov tuam txhab sib sib zog nqus lawv cov peev txheej tiv thaiv cyber los tsim cov kev daws teeb meem cyber siab heev txhawm rau txheeb xyuas thiab nres kev siv cyberattacks.
    • Cybercriminals kawm ML txoj hauv kev los tsim cov algorithms uas tuaj yeem nyiag nyiag kev lag luam thiab pej xeem cov txheej txheem.
    • Cov xwm txheej nce ntxiv ntawm cyberattacks uas yog qhov zoo-orchestrated thiab tsom ntau lub koom haum txhua lub sijhawm.
    • Kev tawm tsam AI software siv los txeeb tswj cov tub rog riam phom, tshuab, thiab cov chaw ua haujlwm hauv tsev.
    • Kev tawm tsam AI software siv los nkag mus, hloov kho lossis siv lub tuam txhab cov txheej txheem los txo cov pej xeem thiab ntiag tug infrastructure. 
    • Qee lub tseem fwv muaj peev xwm rov txhim kho cov kev tiv thaiv digital ntawm lawv cov kev lag luam ntiag tug hauv kev tswj hwm thiab kev tiv thaiv ntawm lawv cov koom haum cybersecurity hauv lub tebchaws.

    Cov lus nug los xav txog

    • Dab tsi yog lwm qhov yuav tshwm sim ntawm AI-enabled cyberattacks?
    • Lwm cov tuam txhab yuav npaj li cas rau kev tawm tsam zoo li no?

    Insight references

    Cov nram qab no nrov thiab cov koom haum txuas tau raug xa mus rau qhov kev pom no:

    Center for Security thiab Emerging Technology Automating Cyber ​​​​Atacks