لخّصلي

خدمة تلخيص النصوص العربية أونلاين،قم بتلخيص نصوصك بضغطة واحدة من خلال هذه الخدمة

نتيجة التلخيص (55%)

Response.In response, attackers develop toolkits that obfuscate the nature and sources of the malware, making it harder to recognize by its digital fingerprint.Although security companies are increasingly introducing AI features and behavioral analytics into their products, a lot of antivirus and end-point protection software still rely largely on signature-based detection.For example, today we have technology to deploy semiautonomous, intelligent lures or "traps" that create a duplicate of the environment to be infiltrated to make attackers believe they are on the intended path and then use the deceit to identify the culprit. AI-enabled response systems can segregate networks dynamically to isolate valuable assets in safe "places" or redirect attackers away from vulnerabilities or valuable data.AI can reduce the workload for cybersecurity analysts by helping to prioritize the risk areas for attention and intelligently automating the manual tasks they typically perform (such as searching through log files for signs of compromises), thus redirecting human efforts toward higher-value activities.AI algorithms tend to be public, often open-source, software that is widely available on the internet and increasingly easy to use.This knowledge could be thwarted by an AI algorithm that adds to the stealthiness of a malware kit over time, masking the malware's identity based on what it learns defense systems are detecting.Just as companies can use AI to automate and improve business processes, hackers can automate the identification of vulnerabilities and exploit-writing.Moreover, open-source AI libraries and software, which give companies a new source of fast and inexpensive innovation, can also be a source of new vulnerabilities.There is even a high degree of competition among cybercriminal vendors (which can leverage AI and machine learning for competitive advantage) to create superior malware.(See the exhibit.)


النص الأصلي

Response. AI can reduce the workload for cybersecurity analysts by helping to prioritize the risk areas for attention and intelligently automating the manual tasks they typically perform (such as searching through log files for signs of compromises), thus redirecting human efforts toward higher-value activities. AI also can facilitate intelligent responses to attacks, either outside or inside the perimeter, based on shared knowledge and learning. For example, today we have technology to deploy semiautonomous, intelligent lures or “traps” that create a duplicate of the environment to be infiltrated to make attackers believe they are on the intended path and then use the deceit to identify the culprit. AI-enabled response systems can segregate networks dynamically to isolate valuable assets in safe “places” or redirect attackers away from vulnerabilities or valuable data. This can help with efficiency as analysts can focus on investigating high-probability signals rather than spending time finding them.

Implementation of automated AI-driven response will require careful design and strategic planning. This will be especially true when it comes to users that should be isolated or quarantined and systems that work at the digital-physical interface (such as critical links in manufacturing or supply chains, or critical-care medical devices in hospitals or emergency settings).

THE RACE IS ON
Cybersecurity has always been an arms race. In 2016, then-US President Obama talked to Wired magazine about his fears of an AI-enabled attacker accessing the US nuclear codes. “If that’s its only job, if it’s self-teaching and it’s just a really effective algorithm, then you’ve got problems,” he said. AI increases attackers’ speed, resilience, opportunities, and chances ofsuccess. Because AI algorithms are self-learning, they get smarter with each attempt and failure; their endeavors are continuously better informed and more capable. Just as companies can use AI to automate and improve business processes, hackers can automate the identification of vulnerabilities and exploit-writing.

AI algorithms tend to be public, often open-source, software that is widely available on the internet and increasingly easy to use. Just like the software-as-a-service that many companies use, malware-as-a-service is commonplace and a viable business for criminal players. There is even a high degree of competition among cybercriminal vendors (which can leverage AI and machine learning for competitive advantage) to create superior malware. Moreover, open-source AI libraries and software, which give companies a new source of fast and inexpensive innovation, can also be a source of new vulnerabilities.

In addition, AI can actually help malware avoid detection. Although security companies are increasingly introducing AI features and behavioral analytics into their products, a lot of antivirus and end-point protection software still rely largely on signature-based detection. In response, attackers develop toolkits that obfuscate the nature and sources of the malware, making it harder to recognize by its digital fingerprint.

On the dark web today, anyone can buy a tailor-made virus guaranteed not to be detected by the 10 or 20 or so major antivirus tools. But defensive systems gain knowledge over time. This knowledge could be thwarted by an AI algorithm that adds to the stealthiness of a malware kit over time, masking the malware’s identity based on what it learns defense systems are detecting. (See the exhibit.)

تلخيص النصوص العربية والإنجليزية أونلاين

تلخيص النصوص آلياً

تلخيص النصوص العربية والإنجليزية اليا باستخدام الخوارزميات الإحصائية وترتيب وأهمية الجمل في النص

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