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AI slashes treatment time

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ANESHAN RAMALOO, Senior Business Solutions Manager at SAS, looks into how 2018 will see how technology trends will shape developments through the year.

It used to take months to be able to say whether a particular treatment for cancer was working – wasting precious time which might otherwise have been used to save a patient’s life. Now using analytics, we can predict that treatment’s effectiveness within days.

When addressing the question of what to expect in the tech space in 2018, there is no limit. AI is already doing things we never before would have dreamed possible. From writing music to creating videos, we are achieving milestones which we previously would have considered strictly human.

And yes, it is even helping to save lives.

One of the major forces driving the world of tech and AI is the increasing volume and availability of data. Think of devices like the Fitbit, which provides a wealth of data concerning your health, such as heart rate and sleeping patterns.

At the same time, we’ve developed technology that allows us to analyse more data than ever before. And thanks to a massive improvement in compute power, analytical solutions can now analyse these massive volumes of data at blistering speed. Data scientists can develop machine learning models in minutes, which can enable businesses to deliver results quickly.

A great example of the technology that allows this is SAS VIYA, which is an end-to-end analytical platform. The platform fuels the analytics life cycle from data preparation to model development and finally deployment. This is all done in a single interface

One feature of the SAS platform that I’m particularly excited about is the ability to analyse images. This capability is already helping when it comes to wildlife conservation. In the past game rangers had to manually take pictures of particular species of animals and tag them. While this wasn’t part of their core focus, it absorbed a great deal of time. But using SAS’s new technology they can simply take the picture and allow the AI to classify, not only the species of the animal, but other helpful traits such as the sex as well. At the end of the day this frees up the rangers to tackle more important tasks.

More accurate predictions

While the algorithms used in machine learning have been relatively unchanged for decades, we are now seeing the emergence of new algorithms, such as extreme gradient boosting, which have proven to be very successful in data mining competitions like Kaggle. Extreme gradient boosting is a significant development in analytics because it generalises well, enabling more accurate predictions.

While we’ve been drawing on structured data sources like transactional data for some time, no-one has really been tapping into unstructured data sources. For example, customer complaints, reviews and other text data sources.

But these two sources when combined together can be extremely powerful. Say, for instance, you wanted to develop a customer churn prediction model. By including data sources like customer complaints, as opposed to just structured and traditional data sources, you can develop a model that is more accurate at predicting churn.

Deep learning has created a lot of hype, and for good reason.

It is a type of machine learning, based on a set of algorithms that model high-level abstractions in data, by using multiple processing layers with complex structures.Instead of organising data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognising patterns using many layers of processing. This means it can train computers to perform human-like tasks, such as recognising speech, identifying images or making predictions.

Deep learning is already being used to make significant inroads into areas such as image recognition, fraud detection and the highly regulated credit risk modelling. In fact, SAS is currently working with credit bureau, Equifax, using deep learning techniques in credit risk modelling. The results are promising as the accuracy of the models has improved traditional techniques.

Bots that understand emotion

Another exciting space in AI is bot technology. Chatbots are programmes that use natural language processing and AI to create conversations between machines and humans.

Instead of having a human respond to complaints or queries, this can now be done by a chatbot to save time and money on mundane and repetitive tasks. For example, responses to queries on bank accounts. Some banks are using bots to advise customers on financial advice and investments.

Until now, AI has generally been designed to do specific things like fraud detection. The human ability to perform tasks has always been greater than machines as we can generalise and perform a much wider set of functions.

But incredibly we’re starting to see AI train itself to learn.

In 2016 Google created a programme called AlphaGo. It was capable of beating even the most skilled human players at the ancient Chinese strategy game, Go – considered to be one of the most complicated games on earth.

But this was taken a step further through the creation of AlphaGo Zero, a programme provided with a very limited amount of training data. The idea was that it would learn by playing against itself. Over a period of time, AlphaGo Zero beat AlphaGo.

Essentially it had taught itself to think.

On the threshold of a future in which machines can think and learn: as we step into 2018, one could say nothing is impossible.

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Which IoT horse should you back?

The emerging IoT is evolving at a rapid pace with more companies entering the market. The development of new product and communication systems is likely to continue to grow over the next few years, after which we could begin to see a few dominant players emerge, says DARREN OXLEE, CTOf of Utility Systems.

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But in the interim, many companies face a dilemma because, in such a new industry, there are so many unknowns about its trajectory. With the variety of options available (particularly regarding the medium of communication), there’s the a question of which horse to back.

Many players also haven’t fully come to grips with the commercial models in IoT (specifically, how much it costs to run these systems).

Which communication protocol should you consider for your IoT application? Depends on what you’re looking for. Here’s a summary of the main low-power, wide area network (LPWAN) communications options that are currently available, along with their applicability:

SIGFOX 

SigFox has what is arguably the most traction in the LPWAN space, thanks to its successful marketing campaigns in Europe. It also has strong support from vendors including Texas Instruments, Silicon Labs, and Axom.

It’s a relatively simple technology, ultra-narrowband (100 Hz), and sends very small data (12 bytes) very slowly (300 bps). So it’s perfect for applications where systems need to send small, infrequent bursts of data. Its lack of downlink capabilities, however, could make it unsuitable for applications that require two-way communication.

LORA 

LoRaWAN is a standard governed by the LoRa Alliance. It’s not open because the underlying chipset is only available through Semtech – though this should change in future.

Its functionality is like SigFox: it’s primarily intended for uplink-only applications with multiple nodes, although downlink messages are possible. But unlike SigFox, LoRa uses multiple frequency channels and data rates with coded messages. These are less likely to interfere with one another, increasing the concentrator capacity.

RPMA 

Ingenu Technology Solutions has developed a proprietary technology called Random Phase Multiple Access (RPMA) in the 2.4 GHz band. Due to its architecture, it’s said to have a superior uplink and downlink capacity compared to other models.

It also claims to have better doppler, scheduling, and interference characteristics, as well as a better link budget of 177 dB compared to LoRa’s 157 dB and SigFox’s 149 dB. Plus, it operates in the 2.4 GHz spectrum, which is globally available for Wi-Fi and Bluetooth, so there are no regional architecture changes needed – unlike SigFox and LoRa.

LTE-M 

LTE-M (LTE Cat-M1) is a cellular technology that has gained traction in the United States and is specifically designed for IoT or machine‑to‑machine (M2M) communications.

It’s a low‑power wide‑area (LPWA) interface that connects IoT and M2M devices with medium data rate requirements (375 kb/s upload and download speeds in half duplex mode). It also enables longer battery lifecycles and greater in‑building range compared to standard cellular technologies like 2G, 3G, or LTE Cat 1.

Key features include:

·       Voice functionality via VoLTE

·       Full mobility and in‑vehicle hand‑over

·       Low power consumption

·       Extended in‑building range

NB-IOT 

Narrowband IoT (NB‑IoT or LTE Cat NB1) is part of the same 3GPP Release 13 standard3 that defined LTE Cat M1 – both are licensed as LPWAN technologies that work virtually anywhere. NB-IoT connects devices simply and efficiently on already established mobile networks and handles small amounts of infrequent two‑way data securely and reliably.

NB‑IoT is well suited for applications like gas and water meters through regular and small data transmissions, as network coverage is a key issue in smart metering rollouts. Meters also tend to be in difficult locations like cellars, deep underground, or in remote areas. NB‑IoT has excellent coverage and penetration to address this.

MY FORECAST

The LPWAN technology stack is fluid, so I foresee it evolving more over the coming years. During this time, I suspect that we’ll see:

1.     Different markets adopting different technologies based on factors like dominant technology players and local regulations

2.     The technologies diverging for a period and then converging with a few key players, which I think will be SigFox, LoRa, and the two LTE-based technologies

3.     A significant technological shift in 3-5 years, which will disrupt this space again

So, which horse should you back?

I don’t believe it’s prudent to pick a single technology now; lock-in could cause serious restrictions in the long-term. A modular, agile approach to implementing the correct communications mechanism for your requirements carries less risk.

The commercial model is also hugely important. The cellular and telecommunications companies will understandably want to maximise their returns and you’ll want to position yourself to share an equitable part of the revenue.

So: do your homework. And good luck!

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Ms Office hack attacks up 4X

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Exploits, software that takes advantage of a bug or vulnerability, for Microsoft Office in-the-wild hit the list of cyber headaches in Q1 2018. Overall, the number of users attacked with malicious Office documents rose more than four times compared with Q1 2017. In just three months, its share of exploits used in attacks grew to almost 50% – this is double the average share of exploits for Microsoft Office across 2017. These are the main findings from Kaspersky Lab’s Q1 IT threat evolution report.

Attacks based on exploits are considered to be very powerful, as they do not require any additional interactions with the user and can deliver their dangerous code discreetly. They are therefore widely used; both by cybercriminals looking for profit and by more sophisticated nation-backed state actors for their malicious purposes.

The first quarter of 2018 experienced a massive inflow of these exploits, targeting popular Microsoft Office software. According to Kaspersky Lab experts, this is likely to be the peak of a longer trend, as at least ten in-the-wild exploits for Microsoft Office software were identified in 2017-2018 – compared to two zero-day exploits for Adobe Flash player used in-the-wild during the same time period.

The share of the latter in the distribution of exploits used in attacks is decreasing as expected (accounting for slightly less than 3% in the first quarter) – Adobe and Microsoft have put a lot of effort into making it difficult to exploit Flash Player.

After cybercriminals find out about a vulnerability, they prepare a ready-to-go exploit. They then frequently use spear-phishing as the infection vector, compromising users and companies through emails with malicious attachments. Worse still, such spear-phishing attack vectors are usually discreet and very actively used in sophisticated targeted attacks – there were many examples of this in the last six months alone.

For instance, in late 2017, Kaspersky Lab’s advanced exploit prevention systems identified a new Adobe Flash zero-day exploit used in-the-wild against our customers. The exploit was delivered through a Microsoft Office document and the final payload was the latest version of FinSpy malware. Analysis of the payload enabled researchers to confidently link this attack to a sophisticated actor known as ‘BlackOasis’. The same month, Kaspersky Lab’s experts published a detailed analysis of СVE-2017-11826, a critical zero-day vulnerability used to launch targeted attacks in all versions of Microsoft Office. The exploit for this vulnerability is an RTF document containing a DOCX document that exploits СVE-2017-11826 in the Office Open XML parser. Finally, just a couple of days ago, information on Internet Explorer zero day CVE-2018-8174 was published. This vulnerability was also used in targeted attacks.

“The threat landscape in the first quarter again shows us that a lack of attention to patch management is one of the most significant cyber-dangers. While vendors usually issue patches for the vulnerabilities, users often can’t update their products in time, which results in waves of discreet and highly effective attacks once the vulnerabilities have been exposed to the broad cybercriminal community,” notes Alexander Liskin, security expert at Kaspersky Lab.

Other online threat statistics from the Q1, 2018 report include:

  • Kaspersky Lab solutions detected and repelled 796,806,112 malicious attacks from online resources located in 194 countries around the world.
  • 282,807,433 unique URLs were recognised as malicious by web antivirus components.
  • Attempted infections by malware that aims to steal money via online access to bank accounts were registered on 204,448 user computers.
  • Kaspersky Lab’s file antivirus detected a total of 187,597,494 unique malicious and potentially unwanted objects.
  • Kaspersky Lab mobile security products also detected:
    • 1,322,578 malicious installation packages.
    • 18,912 mobile banking Trojans (installation packages).

To reduce the risk of infection, users are advised to:

  • Keep the software installed on your PC up to date, and enable the auto-update feature if it is available.
  • Wherever possible, choose a software vendor that demonstrates a responsible approach to a vulnerability problem. Check if the software vendor has its own bug bounty program.

·         Use robust security solutions , which have special features to protect against exploits, such as Automatic Exploit Prevention.

·         Regularly run a system scan to check for possible infections and make sure you keep all software up to date.

  • Businesses should use a security solution that provides vulnerability, patch management and exploit prevention components, such as Kaspersky Endpoint Security for Business. The patch management feature automatically eliminates vulnerabilities and proactively patches them. The exploit prevention component monitors suspicious actions of applications and blocks malicious files executions.
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