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How far is the future?

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The customer service environment is being shaped be technologies like AI, machine learning and augmented reality, but how long will it take for this to become mainstream? MICHELLE OSMOND from 1Stream, sheds some light.

In the movie Big Hero 6, the inflatable Baymax robot is a healthcare companion who can diagnose and suggest treatment based on the 10,000 medical procedures he has learnt, all within a two-second body scan. So, how far off are we from using such technology for customer service?

We are in fact already using artificial intelligence (AI) and machine learning on a daily basis when we use the Uber app to call for a taxi, or when Netflix suggests a series we may enjoy based on our viewing habits.

In the contact centre environment, this technology is being used to automate certain functions to enhance the customer experience, giving rise to the use of customer-facing chatbots and digital assistants that provide an initial layer of support that is accessible 24/7. The customer speaks to a machine and not a human agent.

Instead of going through long menus that force users to choose inadequate options and repeat their queries at every step, the chatbot uses automatic speech recognition (transcription) and text-to-speech (automated responses), to handle the initial contact and deal with basic interactions.

Intelligent routing

A chatbot must be able to correctly identify the intentions of the customer, and will have hundreds of possible scenarios available to it. It knows the entities involved, and what kind of immediate help can be provided. Ongoing training of the chatbot enables it to expand the range of interactions it can manage. It must also be able to detect the emotional state of the customer, and based on the interaction, transfer the call to a human representative if necessary.

For instance, a chatbot can handle a basic interaction such as an airport shuttle booking, but it will transfer the call to a human agent if there is a query it cannot handle, such as whether or not the shuttle will be able to accommodate a bicycle.

Machine learning

All the information and context from the contact is passed on to the human agent in order to swiftly answer the query and finalise the booking, and the chatbot will stay on the call to learn the correct response for future reference. This is machine learning being used to expand the chatbot’s knowledge.

Social media integration

When the power of AI and machine learning is combined with the integration of the contact centre function with social media, a powerful customer engagement is possible.

When a customer’s luggage does not arrive and they are frustrated, they may turn to the travel company’s Facebook Messenger to complain. A messenger bot will be able to respond with a view of the full history of the customer journey. The chatbot will be able to detect the tone and urgency of this interaction and will transfer it to a human agent if they are unable to resolve the query effectively.

Augmented Reality

These technologies combine and enable us to link all the data we have for customers and make it available to both virtual and human agents. By creating this dialogue between the customer, the human agent, and the chatbot, agents have the ability to access useful data previously inaccessible in real-time, bringing Augmented Reality to the heart of the contact centre.

The contact centre agent of the future

This shift to integration of all channels and the use of chatbots will not make agents redundant, but rather allow them to focus on developing their communication skills and manage the more nuanced interactions that chatbots are not able to cope with. Contact centre agents will become super agents, with sophisticated social interaction and people management skills.

This will make for a better customer experience with swifter responses on whichever channel suits that particular customer best.

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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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