Table of contents

• Recent AI-assisted cyberattacks
• What is driving AI-powered cyber attacks?
• The flip side of AI in cybersecurity and why you should implement AI in cybersecurity
• Some early adopters of Artificial Intelligence in cybersecurity
• Challenges in implementing AI in cybersecurity
• Conclusion

On April 29th, 2021, hackers breached the networks of Colonial Pipeline Company through a virtual private network account. About a week later, the hackers sent a ransomware note that demanded cryptocurrency. What followed was a complete shutdown of the entire gas pipeline system in its 57-year history. The Colonial pipeline supplies nearly half of the East Coast’s gasoline and because of the shut down it had for several days, there was gas panic-buying, shortages, and price spikes. The attack happened due to a breach through a leaked password to an old account that had access to the virtual private network (VPN) used to remotely access the company’s servers. It was undoubtedly one of the largest cyberattacks ever on an American energy system and definitely brought to light how vulnerable the world is when it comes to cybersecurity. Businesses, governments, and individuals are taking measures to become more resilient to such attacks in the future and adopting various smart solutions as well. But guess what? Cyberattacks are getting smarter, too.

Recent AI-assisted cyberattacks

AI-assisted cyberattacks are becoming a rising security concern for everyone around the world. While cyberattacks are quite known, one may feel that AI cyberattacks are not as common. But the fact is that Artificial Intelligence cyberattacks have happened in the past and continue to happen. Hackers are leveraging machine learning algorithms and botnets and launching them across the web.

• Taskrabbit, IKEA’s Gig-Economy Home Service Marketplace, Gets Hit By Hackers

  Social security numbers and bank account details of nearly 3.75 million people were extracted by hackers in the attack on TaskRabbit in April 2018. The hackers used an AI-powered botnet that performed a DDoS attack on TaskRabbit’s servers. The consequences? The entire site was disabled until security was restored and an additional 141 million users were affected.

• A botnet of over 20,000 WordPress sites is attacking other WordPress sites

  Not long ago, around 20,000 WordPress sites were infected with a botnet which was feared to have granted the hackers access to critical personal information of users as well as their credit card numbers.

• New Instagram Bug Raises Security Questions

  In 2019, many Instagram users reported that they were locked out of their accounts. Instagram didn’t release detailed information or confirm the root cause but it was speculated that hackers used AI to scan potential vulnerabilities and carry out the attack.

These are just a few of the many incidents that have taken place over the past. And there’s no stopping yet. Just in case if you believe that having the basics of security checks such as firewalls, malware scanners, and secure CMS covered will guard you against cyberattacks successfully, think again. AI-assisted cyber attacks will only get worse.

What is driving AI-powered cyber attacks?

AI-powered cyberattacks are known to “supercharge” cyber attacks. Over time, they learn what types of attacks are more successful. The notorious Emotet Trojan is a fine example of that. Once considered rare, AI attacks are increasing every day. The reason behind this increase is simple- the same reason why AI is preferred over human efforts when it comes to solving problems. AI can reach (read: attack) far more computers than humans in the same amount of time. If one is using machine language, the cyber-attacks become more unpredictable, efficient and can surpass planned security efforts as well by learning on their own. AI cyberattacks are so fast that they don’t give cyber security teams the time to respond to incidents timely and can go on with repeated attacks without getting tired, unlike humans. As for the cost involved, AI bots are less costly as they don’t need to get paid. Another key reason is that it allows hackers to remain hidden and operate remotely, keeping them safe from getting caught. All these reasons are driving the use of AI in cyber attacks.

The flip side of AI in cybersecurity and why you should implement AI in cybersecurity

According to a report by meticulous research, Artificial Intelligence in cybersecurity is expected to grow at a CAGR of 23.6% to reach $46.3 billion by 2027. Implementing AI security measures help in taking preventive measures, proactively, making cyber security efforts more effective, and reducing the time taken to respond to incidents. Artificial intelligence is already changing how cybersecurity is addressed across all major industries by providing solutions that can analyze and respond to attacks in real-time, thereby improving the overall security. Here are more reasons why you should consider implementing artificial intelligence in your cybersecurity efforts:

• Automation: AI can learn and be proactive. It can automate repeatable tasks and eliminate the element of fatigue which may arise in security personnel
• Up-to-date arsenal: AI is an effective tool that continually learns and updates itself. This makes it a very useful security tool in your technology arsenal
• Deeper investigations: One of the biggest advantages of AI is that it can read both unstructured and structured data, thereby giving more information you need to reduce time to detect and time to respond to incidents
• Thorough investigations in less time: AI allows thorough and consistent investigations through automated data mining quickly and allows analysts to focus on strategic threat investigations
• Alert prioritization: AI enables quick discovery of false negatives and false positives which helps analysts attend to alerts effectively by focusing on the most critical ones first and also reduces the chances of missing out on critical incidents
• Better Vulnerability Management: AI quickly identifies vulnerable points in business networks and helps businesses take preventive measures by focusing on important security tasks
• High data center uptimes: AI has the capability to alert before there is any major software or hardware fault so that it can be taken care of before any damage is done. This ensures a high uptime of network and security systems linked with it

In one of our reports titled The Secrets to Building a $100 Million IoT Business within 5 Years, and More!, we have extensively covered how smart digital solutions can be secured against various risks. We have also highlighted potential security weaknesses and targets and how you can start securing your solutions right from the development stage. Know all about how you can adopt top-notch security measures in this free report.

Some early adopters of Artificial Intelligence in cybersecurity

We’ve been seeing how Artificial Intelligence is leveraged across various industries to overcome many challenges, what we are yet to see with the same intensity is its application in cybersecurity. A lot of leading companies have already implemented AI to secure their ecosystem from threats and vulnerabilities. While it hints at their far-sightedness, it also creates an example and a proven model for other businesses to follow without giving thoughts on how it could benefit them. Here are few examples of early adopters of AI in cybersecurity:

• Google: We have already seen how Google puts suspicious emails in the spam folder and alerts you if it finds anything suspicious in terms of login or data access. Not so surprisingly, Google has been using machine learning to filter suspicious emails ever since it was launched. Since then, deep learning technology has allowed it to evolve and get better
• Balbix: Balbix is an AI-powered solution that delivers continuous and real-time predictions and analysis and alerts for vulnerabilities making it easier and more effective in controlling breaches. This also makes cybersecurity teams more efficient and potent by taking care of repetitive tasks and allowing them to focus on other critical tasks
• Darktrace: A leading AI company, Darktrace, provides cybersecurity software powered by ML to analyze network traffic information and understand the behavior of each user and device. Based on the inputs and other training datasets, their software detects even a vital deviation from normal baseline user behavior and alerts the company

Challenges in implementing AI in cybersecurity

Even though it is ideal to implement Artificial intelligence in cybersecurity, it can sometimes get a bit challenging to do so especially if some common constraints are not considered initially.

• Cost considerations: In the long term, the RoI from a robust AI-powered security system will be quite high. However, deploying and maintaining AI cyber security systems might require a lot of investment, depending on the size of your organization
• Lack of available data sets: AI cyber security models rely heavily on different data sets to evolve and be more effective. If you do not have an initial data set available, you might need to buy different types of data sets which would be an additional cost
• AI works both ways: Just like you, hackers are also making their algorithms more advanced by using AI. that can successfully breach your AI secured systems. Chances are that their AI-driven attacks might become resistant to your security algorithms
• Can’t be completely relied on: Even though AI increases cybersecurity a lot more than human monitoring alone, they are still advanced enough to operate independently and require some level of human intervention

Conclusion

Undoubtedly, business leaders and cybersecurity professionals are acknowledging the scope of Artificial Intelligence in cybersecurity. But as of today, the solution can’t address all the problems and threats posed by hackers. Artificial Intelligence and Machine Learning are improving cybersecurity but at the same time, they are also being leveraged by hackers to commit cybercrimes to an extent that may no longer require human intervention from their side at all. The ideal thing to do would be a mix of both AI-powered cybersecurity as well as a human intervention that can synergize the intelligence to operate not just on a technology level but a strategic level as well.

The post Artificial Intelligence in Cybersecurity: AI Cyberattacks, Securing Your Ecosystem with AI, and More appeared first on [x]cube LABS.

Table of contents

• What is GPT-3?
• How does GPT-3 work?
• How is different from its predecessor GPT-2?
• Why is GPT-3 such a big deal?
• What do the early adopters have to say about it?
• A new breakthrough for artificial intelligence

“This is mind blowing. With GPT-3, I built a layout generator where you just describe any layout you want, and it generates the JSX code for you. W H A T.” The tweet by Sharif Shameem about an experiment he did with GPT-3 left thousands in the technology community astonished- for all the obvious reasons. How is it possible for an AI to write complex computer code from a request in simple English, despite never having been conditioned to write code in the first place – or even comprehend English?

What is GPT-3?

The third generation of OpenAI’s Generative Pretrained Transformer, GPT-3, is a general-purpose language algorithm that uses machine learning to translate text, answer questions and predictively write text. It analyzes a sequence of words, text and other data, then elaborates on those examples to produce entirely original output in the form of an article or an image.

We’re releasing an API for accessing new AI models developed by OpenAI. You can “program” the API in natural language with just a few examples of your task. See how companies are using the API today, or join our waitlist: https://t.co/SvTgaFuTzN pic.twitter.com/uoeeuqpDWR

— OpenAI (@OpenAI) June 11, 2020

How does GPT-3 work?

“By ingesting terabytes and terabytes of data to understand the underlying patterns in how humans communicate,” as shared by Sharif Shameem. GPT-3 processes an enormous data bank of English sentences and extremely powerful computer models called neural nets to identify patterns and determine its own rules of how language functions. GPT-3 possesses 175 billion learning parameters which enable it to perform almost any task it is assigned, making it larger than the second-most powerful language model, Microsoft Corp.’s Turing-NLG algorithm, which has 17 billion learning parameters.

How is it different from its predecessor GPT-2?

In February 2019, OpenAI published their findings and results on their unsupervised language model, GPT-2, which was trained in 40Gb texts and was capable of predicting words in proximity. GPT-2, a transformer-based language applied to self-attention, allowed researchers to generated very convincing and coherent texts. The system, which is a general-purpose language algorithm, used machine learning to translate text, answer questions and predicatively write text. However, it created a controversy because of its ability to create extremely realistic and coherent “fake news” articles based on something as simple as an opening sentence, making it unavailable for the public initially.

Why is GPT-3 such a big deal?

As people still wonder why GPT-3 is so hyped, the answer is simple- it is the largest model trained yet. Its 175 learning parameters are 10 times more than any previous non-sparse language model. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning. It only requires few-shot demonstrations via textual interaction with the model. This giant breakthrough for deep learning and natural language processing has enabled GPT-3 to accomplish all of the following and much more:

• Answer trivia puzzles correctly
• Predict the last word of sentences by recognizing the context of the paragraph
• Select the best ending out of many for a story
• Translate common languages, which was initially difficult for GPT-2
• Apply reasoning involving common sense
• Perform 5 digital arithmetic with accuracy
• Write news articles from a title with human-like essence

A research paper on GPT-3 titles “Language Models are Few-Shot Learners” highlights the results of testing GPT-3 on tasks mentioned above against fine-tuned state-of-the-art models. In most of the tests, GPT-3 performed better than those models at zero-shot configurations.

Reasons enough to create a buzz, isn’t it?

What do the early adopters have to say about it?

Soon after publishing GP-3 research, OpenAI gave select public members access to the model via an API. And since then, we can see a number of samples of text generated by GPT-3 widely on social media- leading to the hype we are sessing currently.

Founders Fund Principal, Delian Asparouhov, shared an excellent example of GPT-3 where he fed the algorithm half of an investment memo he had posted on his company website. He then gave GPT-3 half of an essay on how to run effective board meetings. In both cases, GPT-3 generated coherent and new paragraphs of text that followed the earlier formatting in such a manner that made it almost indistinguishable from the original text.

In another example, GPT-3 successfully showcased its capability to deceive people on almost any topic by writing about itself. Manuel Araoz, CTO, Zeppelin Solutions GmbH, used GPT-3 to create a complicated article about a faux experiment on the Bitcointalk forum by applying a basic prompt as a guideline. The article, “OpenAI’s GPT-3 may be the biggest thing since bitcoin,” details how GPT-3 deceived forum members into believing that its comments were genuine and human-written. Not just that, Araoz also tested GPT-3 in many other ways and made complex texts easier to understand, wrote poems in Spanish in Borges style, wrote music in ABC notation and much more.

A new breakthrough for artificial intelligence

In their mission to ensure that artificial general intelligence (AGI)-outperform humans at most economically valuable work-benefits to all of humanity, Open AI’s GPT-3 has been a major leap in achieving it by reaching the highest stage of human-like intelligence through ML and NLP. This is backed by experiments conducted by early testers who are left astounded by the results. We can only wonder what the next-gen of their developments can be capable of achieving.

Currently, the latest version of their GPT-3 general-purpose natural language processing model is available in private beta, and OpenAI is providing access to its API by invitation only. There’s still a long waiting list for the paid version, which is expected to be released in the next two months.

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