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Artificial Intelligence Techniques to detect Cyber Crimes

When we talk about artificial intelligence, many imagine a world of science fiction where robots dominate. In reality, artificial intelligence is already improving current technologies such as online shopping, surveillance systems and many others.

 

In the area of ​​cyber security, artificial intelligence is being used via machine learning techniques. Indeed, the machine learning algorithms allow computers to learn and make predictions based on available known data. This technique is especially effective for daily process of millions of malware. According to AV-Test statistics, security analysts must examine more than 400,000 new malicious programs every day.

 

Security experts affirms that the traditional detection methods (the signature-based systems) are no longer really proactive in most cases. The task is even more difficult as, in a world dominated by copy-paste exploit cloning, security vendors must also manage third-party services, and focus on detecting the obfuscated exploit variant, to be able to provide protection to their customers. Attackers are numerous, but the automatic learning balance the chances of struggle.

 

Applying Artificial Intelligence to cyber Security: More and more technology companies and security vendors are beginning to look for ways to integrate artificial intelligence to their cyber security arsenal. Many clustering and classification algorithms can be used to quickly and correctly answer the crucial question: “This file is it healthy or malicious?” For example, if a million files must be analyzed, the samples can be divided into small groups (called clusters) in which each file is similar to the others. The security analyst only has to analyze later, a file in each group and apply the results to others.

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More importantly, machine learning gets a high detection rate for new malicious software in circulation as the famous ransomware malware and zero-day, and against whom, a security solution must be as efficient as possible. In order to be practical, each machine learning classifiers used for malware detection must be set to obtain a very small amount, preferably zero, of false positives. It is also a way to form with very large databases (using the graphics processor or parallelism).

The fundamental principle of machine learning is to recognize the trends of past experiences, and make predictions based on them. This means that security solutions can react more effectively and more quickly to new invisible cyber threats compared to traditional techniques and automated cyber-attack detection systems that were used before. Artificial Intelligence is also suitable to fight against sophisticated attacks such as APT (Advanced Persistent Threats), where attackers take special care to remain undetected for indefinite periods of time.

 

Man against the machine:  breaking the boundaries between man and machine, artificial intelligence is a very important cyber weapon, but cannot alone take on any fight against cyber threats. As I’ve mentioned in previous paragraphs, the machine learning systems can get false positives, the decision of a human is needed to sort algorithms with appropriate data.

Les algorithmes d’apprentissage automatique sont, dans l’ensemble, plus précis dans l’évaluation des menaces potentielles de malwares au sein de grandes quantités de données de renseignement, que leurs homologues humains. Ils savent aussi repérer plus rapidement les intrusions.

The machine learning algorithms are, overall, more accurate in assessing potential malware threats in large quantities of intelligence data, than humans. They also know how to quickly detect breach. The current hybrid approach that is generally used today is to oversee automatic learning by human analysts. This allowed better results so far.

 

Regarding the future of AI, it is almost impossible to predict the future. Who knows that may be next year, machine learning will most likely focus on the creation of specific profiles for each user. Where an action or a user’s behavior does not correspond to the predefined templates, the user will be informed. For example, a peak of downloads in a short time will be marked as suspect, and analyzed closely by a human expert.

Google reveals five security issues concerning Artificial intelligence

In a recent article published by Google, they’ve revealed five major security problems related to Artificial Intelligence. From now on, companies will have to follow a guide on their future Al system to control robots before they can interact with humans.

 

The artificial intelligence is designed to mimic the human brain, or at least its logic when it comes to making decisions. Before worrying about whether an artificial intelligence (AI) could become so powerful that can dominate humans, it would be better to make sure that robots (also called our future colleagues and household companions) are trustworthy. That’s what Google has tried to explain to us. Google’s artificial intelligence specialists have worked with researchers from the Universities of Stanford and Berkeley (California, USA) and with the Association OpenAI on concrete security issues that we must work to resolve.

 

In white paper titled “Concrete Problems in AI Safety” this team describes five “practical problems” of accidents in artificial intelligence-based machine could cause if they aren’t designed properly. Al specialists define accidents as “unexpected and harmful behavior that may emerge from poor design of real world machine learning systems”. In short, these are not potential errors of robots we should be feared but those of their designers.

 

To concretely illustrate their point of view, the authors of this study voluntarily took a random example of a “cleaning robot”. However, it’s quite clear that the issues apply to all forms of AI controlling a robot.

 

 

Pour prévenir ce cas de figure, la solution pourrait consister à créer des « contraintes de bon sens » sous la forme de pénalités infligées à l’IA lorsqu’elle cause une perturbation majeure à l’environnement dans lequel le robot évolue.


  • A robot may disrupt the environment :

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The first two risks identified by the researchers from Google and their acolytes are related to a poor coordination and allocation of the main objective. There is first what they call “Avoiding Negative Side Effects”. Specifically, how to avoid environment related problems caused by a robot while it’s accomplishing its mission. For example, the cleaner could well topple or crush what is on his way because he calculated the fastest route to complete its task. To prevent this scenario, the solution may be to create “constraints of common sense” in the form of penalties imposed on the IA when he causes a major disruption to the environment in which the robot moves.


  • The machine can cheat :  

Second risk of Al based machines is to avoiding reward hacking. For IA, the reward is the success of the goal. Avoid the quest reward from turning into a game and the machine trying to get by all means, even skip steps or cheat. In the case of cleaning robot, it would for example to hide the dirt under the rug in order to say “that’s it, I’m done.”
A difficult problem to solve as an Al can be interpreted in many different ways a task and the environment it meets. One of the ideas in the article is to truncate the information so that the program does not have a perfect knowledge of how to get a reward and thus does not seek to go shorter or easier.


  • How to setup the robot go to the basics?

The third risk is called scalable oversight. More the goal is complex, AI will have to validate his progress with his human referent, which would quickly become tiresome and unproductive. How to proceed so the robot can accomplish itself certain stages of its mission to be effective while knowing seek approval in situations that he will know how to interpret? Example: tidy and clean the kitchen, but ask what to do in the saucepan on the stove. It would simplify to the maximum step of the cooking task so that the robot goes to the point without coming to disturb you during your nap every time.


  • How much independence can you give to an AI?

The next identified problem is the safe exploration of Al. How much independence can you give an AI? The whole point of artificial intelligence is that it can make progress by experimenting different approaches to evaluate the results and decide to keep the most relevant scenarios to achieve its objective. Thus, Google says, if our brave robot would be well advised to learn to perfect its handling of the sponge, we wouldn’t want it to clean an electrical outlet! The suggested solution is to train these Al with simulated environment in which their empirical experiments will not create any risk of accident.


  • Does AI will adapt the change?

Fifth and final problem: robustness to distributional shift or how to adapt to change. “How to be ensured that AI recognizes and behaves properly when it is in a very different environment from the one in which it was being driven? It is clear that we wouldn’t want the robot who was trained to wash the floor of a factory with detergent products does not apply the same technique if asked to clean home.

The article ends by saying that these problems are relatively easy to overcome with the technical means currently available but it’s better to be prudent and develop security policies that can remain effective as the autonomous systems will gain in power. Google is also working on an “emergency stop” button for all menacing AI, if eventually one or several of these risks were not fully mastered,

IoT: Biggest Revolution in Retail

If the IoT represents a huge opportunity for almost every facet of the business, this is particularly true for supply chain specialists, operations and analysis. The leaders of e-commerce and traditional commerce see an opportunity of competitive advantage in IoT.

 

Even though I’ve already wrote about IoT in my previous posts, let me give you again a quick definition of it. In 1999, Kevin Ashton (MIT Auto-ID Center) describes the Internet of Things as a network of interconnected objects that generates data without any human intervention. Today, Gartner describes the IoT as “the network of physical objects containing embedded technology to communicate, detect or interact with their internal states or the external environment.”

 

estimates for IoT revenue by region in 2020

For some IoT is only a new name of an old concept, the only thing which has recently changed in this existing concept, is the evolution of Cloud technology. According to a recent survey by Gartner, IoT is one of the fastest-growing technological trend. Estimation says that by 2020, the number of connected objects will be multiplied by 26 to $ 30 billion. Main reason behind IoT success is the development of solutions based in clouds; which allows to actually have access to the data generated by the connected objects.

 

The growth of IoT relies on three levers: reduction in integrated chips costs, technologies supported by a cloud platform and powered by analyzing Big Data and finally the Machine Learning. A case study of IBM named “The smarter supply chain of the future” revels that in near future the entire supply chain will be connected – not just customers, suppliers and IT systems in general, but also parts, products and other smart objects used to monitor the supply chain. Extensive connectivity will enable worldwide networks of supply chains to plan and make decisions together.

 

The main objective of such connective supply chain is to gain better visibility and to reduce the impact of volatility in all stages of the chain and get better returns by being more agile product flow. Several developments are already underway in the IoT and are revolutionizing the retail supply chain at various levels:

 

– At the client side: integration of end consumer in the IoT. The main objective of this step is to collect customer data to create customized product, personalized offers while simplifying the purchasing process. Devices such as health trackers, connected watches etc. continuously collect the data from consumers, prescribers. The collected data represents a great opportunity of positioning product/services. For example, from a person’s browsing history, its culinary tastes and influences on social networks, information on a nutrition bar can be offered to him. Recommendations may also be appropriate if the person enrolled in a sports club or acquired a fitness tracker and so on.

 
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– As for retailers: Beyond the preparation of the assortment by merchants, there are smart shelves and organization of sales outlet. Moreover, we are witnessing a rapidly changing purchasing behavior so with smart shelves a retailer’s system can analyze inventory, capacity and shipment information sent by suppliers. Via the predicted system retailers and suppliers can avoid costly out-of-stocks or missed sales.

To take the example of nutrition bar, time spent in front of a specific category of products (yogurt lightened for example) can be an early indicator to change suggestions or promotions. In addition, the integration of the retail IoT can allow the line to automatically trigger orders. The whole environment can be configured to access a library of planograms, to store inventory data and related warehouses to automatically run restocking. As the elements of this environment are already used independently, we can predict that we are at the dawn of IoT in retail.

 

If the store are at a less advanced stage in the application of IoT, transportation and warehousing are well connected. The integration of RFID shows a first generation data-oriented machine. Integrated tracking systems have long been used in transport and warehouse systems. RFID tagging of pallets has to have better visibility on the status of stocks and the location. The convergence of demand signals and increased visibility on the state of stocks and their location results in scenarios such as the anticipated shipment for which Amazon has filed a patent. Increasing integration of IoT can lead to efficient use of robots for material handling and delivery by drones. These innovations are challenging the effectiveness of existing systems by optimizing the machine learning an effective alternative.

 

Even with all the benefits it promises to offer companies, IoT is still a gamble, with big risks and unsolved problems. For any organization that decided to embark on the IoT, a number of questions remain open whether in technology, integration with file distribution systems to traditional ERP API to communicate with sensors and application languages ​​(Python, ShinyR, et AL.)

 

There are several interfaces that work well in specific areas, but it needs more standardized platforms. Industry experts have launched PaaS (Platform as a Service) to integrate this growing IoT technology. Despite these challenges, the technology seems a surmountable obstacle. Only the legislation on collected data is a real problem so far. Even the customer acceptance remains a challenge. In 2013, Nordstorm had to backtrack on his program which was to track customer movements by the Wi-Fi use on smartphones and via video analysis due to customers demand.

 

Finally, the important thing to remember is that the IoT is a revolutionary technology. A lot of expert retailers, e-commerce players and technology solutions providers will rethink and adapt the model and evolve in processes designed for organizations wishing to adopt the IoT. Retailers that take the lead in this space stand to gain an important advantage in an already competitive environment. Early adopters will be positioned to more quickly deliver IoT-enabled capabilities that can increase revenue, reduce costs and drive a differentiated brand experience. The IoT will be a disruptive force in retail operations.

 

 

Sources:

The Smarter Supply Chain Of The Future

The CEO Perspective: IOT for Retail Top Priorities to build a Successful Strategy

Machine Learning: An Artificial Intelligence approach

I’ve heard a lot of people saying that Machine Learning is nothing else than a synonymous of Artificial Intelligence but that’s not true at all. The reality is that Machine Learning is just one approach to AI (in fact it’s called the statistic approach).

 

Let me first give a definition of Machine Learning. It’s a type of artificial intelligence that gives computers the ability to learn to do stuff via different algorithms. On the other hand Artificial Intelligence is used to develop computer programs that perform tasks that are normally performed by human by giving machines (robots) the ability to seem like they have human intelligence.

 

If you are wondering what it means for a machine to be intelligent, it’s clear that “learning” is the KEY issue. Stuffing a lot of knowledge into a machine is simply not enough to make it intelligent. So before going far in the article, you must know that in the field of Artificial Intelligence, there are 2 main approaches about how to program a machine so it can perform human tasks. We’ve a Statistical Approach (also known as probabilistic) and Deterministic Approach. None of these two approach are superior to the other, they are just used in different cases.

 

The Machine Learning (=Statistic AI) is based on, yes you’ve guessed right, statistics. It’s a process where the AI system gather, organize, analyze and interpret numerical information from data. More and more industries are applying AL to process improvement in the design and manufacture of their products.

 

There’ll be around 5 to 20 billion connected devices within 3 years and so many capture points will be used to make live decisions, to recommend, provide real-time information and detect weak signals or plan of predictive maintenance. Whether it’s at the level of business uses, the sectors of industry and services (health, distribution, automotive, public sector …) or even the use of Business Intelligence, everything is changing! With the Machine Learning and voice recognition technology based on AI, even the Big Data technology might be quickly overtaken by real-time information.

 

In a preview of an upcoming e-book, “AI & Machine Learning”, UMANIS talks about The Data, machinery and men. In the e-book they have elaborated problems and expectations that different companies are facing in the technological era.

 

Based on the responses of 58 participants who responded to the survey “AI & Machine Learning”, here below you’ll find identified trends and indicators.

 

  • 44% of companies believes that AI and Machine Learning have become essential and latest trend in various fields including education, healthcare, the environment and business sector,
  • One company on two is curious about the technological innovations in order to understand the collection of data (via machine)
  • 1/3 of companies are currently on standby on AL & Machine Learning topics,
  • 21% of IT decision makers were informed about Cortana suites (Microsoft) and Watson (IBM)
  • 36% want to go further on this type of technology,
  • 88% are planning to launch an AL project within more than 6 months,
  • 50% of respondents are unaware of the purpose of these technologies in the company.

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TOP 5 issues:

  • Detect abnormalities
  • Using machine learning to optimize the automation
  • Integrating a Learning Machine module into an existing SI
  • Remodeling of the real-time Data architecture to gather big volumes with high computing power
  • Finding a permanent solution of storage and backup of the collected data

 

There’s no doubt that machine learning area is booming. It can be applied to high volumes of data to obtain a more detailed understanding of the implementation process and to improve decision making in manufacturing, retail, healthcare, life sciences, tourism, hospitality, financial services and energy. The machine learning systems can make precise result predictions based on data from training or past experiences. By gathering relevant information for making more accurate decisions, machine learning systems can help manufacturers improve their operations and competitiveness.

Artificial intelligence and connected objects, trends of the upcoming years?

Artificial intelligence

Artificial intelligence, great topic of the moment? Yet the term dates back to the 1950s! AI is the term used most commonly for Artificial Intelligence. The concept is to develop computer programs that perform tasks that are normally performed by human. The goal is to give machines (robots) ability to seem like they have human intelligence. I’m pretty sure that at this point we all have seen robots doing the grunt work in factories, intelligence driverless cars, and companies are using AL to improve their product and increase sales.

 

Since 2007, Gartner has been predicting key strategic technology trends for the coming years – not an easy task considering the rapid change in the IT market. According to a classification made by Gartner, Artificial Intelligence, Big Data, Cloud Computing, sensors, connected objects, smart machines and modern 3D printing are the key trends of the years 2016-2020. Automation and artificial intelligence figure prominently in the top 10 technology trends of the future presented by Gartner at its conference Symposium / ITxpo 2015.

 

Multiple devices, mobile to electronic devices via the connected devices and sensors are the first big trend mentioned by the research company. More and more devices are becoming connected and “resulting in smarter homes, smarter cars, smarter everything. IoT is leading to a point where “no object will just be an object—it will all be wirelessly connected to something else.

 

 Gartner expects more interaction between these connected devices via different networks in the coming years and beyond (via 4G + 5G technologies). The user experience and virtual environments comes in second position. According to Gartner, this presents a big opportunity and competitive advantage to IT developers and enterprises. 3D printing are third in this ranking, which isn’t yet a mature market, but getting stronger. Thus, global shipments of 3D printers for businesses should show 64% of an average annual growth rate until 2019.

 

Information on the massive data processing era (Big Data), followed by advanced machine learning and deep learning are also on the top 10.

 

“The explosion of data sources and complexity of data classification makes traditional (manual) analysis almost impossible and unprofitable for organizations. With artificial intelligence, the chances of error are almost zero in addition to that greater precision and accuracy is achieved. Plus according to Gartner, in 2018, 20% of all business content and documents will be produced by machines.

 

Gartner have published results of a survey on the topic Big Data, artificial intelligence and the relationship between the two domains. Without further ado, here are the main results:

  • 69% of respondents says that artificial intelligence will improve with the massive use of data
  • 68% think that Big Data will be used very long term by public authorities and businesses
  • 67% believe that the Big Data presents long-term benefits for the health and well-being
  • Finally, 65% approves that they use avatars that are digital assistants who interact with the users in order to save the need of human resources.

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Artificial intelligence can provide unexpected business intelligence for organizations, enhance knowledge on their customers and improve customer interaction with the company, and in some case even replace entire departments as intelligent, learning machines perform tasks until now strictly reserved for humans. Not surprisingly, demand for solutions made possible by artificial intelligence is increasing in the private sector as well as in the public sector. “In every organization, IT experts should explore how to use intelligent agents and these autonomous connected objects to improve the activity” said David Cearley, vice -President and associated Gartner.

 

Source: Webbmedia Group – 2016 Tech Trends

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