If you aren't aware of AI's influence over all aspects of tech at the moment, that's probably because it does a good job of stepping in seamlessly to pick up the slack and take on tasks that could theoretically be handled by humans, but rarely are. Cyber security is an example of an arena in which artificial intelligence is a powerful ally for end users, but how does this come across in a mobile context? Threat detection is one of the most significant aspects of cyber security since there are all too many examples of breaches that have gone unnoticed for months on end, leaving data and systems exposed to exploitation for protracted periods. AI-enhanced security tools are better equipped to detect threats of all types in less time because they can draw on vast pools of data from a deluge of different devices, and identify suspicious signs of an infection in a flash. This is part of a broader big data trend for cloud-based AI analysis benefitting individual end users and devices which might otherwise not have the onboard processing grunt or storage capacity to handle threat detection efficiently.

THE HAGUE, Netherlands – Artificial intelligence will be a requirement for securing carrier 5G networks – which is shaping up to be a technology juggernaut that presents unique challenges unlike any ever seen in the world of telecom until now. That was the assessment at the GSMA Mobile 360 Security for 5G conference, taking place here this week. To understand the challenges and the drivers for artificial intelligence (AI), it's important to understand that existing telecom networks, even today's 4G LTE networks, are built from a hardware-centric perspective, using the vertical-stack Open Systems Interconnection (OSI) model. Features include a heavy reliance on hardware big routers and switches with device-specific software to run them. Functions are hard-coded and largely siloed.

How Machine Learning Finds Malware Needles in an AppStore Haystack Theodora Titonis, Vice President of Mobile Security at Veracode Machine learning techniques are becoming more sophisticated. Can these techniques be more affective at assessing mobile apps for malicious or risky behaviors than traditional means? This session will include a live demo showing data analysis techniques and the results machine learning delivers in terms of classifying mobile applications with malicious or risky behavior. The presentation will also explain the difference between supervised and unsupervised algorithms used for machine learning as well as explain how you can use unsupervised machine learning to detect malicious or risky apps. What you will learn: Understand the difference between advanced machine learning techniques vs. traditional means.

There's a lot of talk these days about how artificial intelligence (AI) and machine learning will transform every industry, from agriculture (as CNN Tech recently reported) to zoo-keeping (as reported by the BBC). AI proponents speak in glowing terms of the many ways AI systems will empower security teams to respond faster and more effectively to cyberthreats. One of the reasons AI is generating so much buzz in the cybersecurity space is the explosion of data in the enterprise that needs to be protected. Much of this data is coming from the ever-increasing number of mobile devices that are connected to the enterprise. In turn, all of these devices are connected to apps, cloud services, websites, data repositories and more.

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If you are fortunate to work as a corporate slave, then you're probably familiar with the acronym BYOD which stands for "Bring Your Own Device". The idea was that since everyone was carrying around their personal smartphone and their work issued smartphone, why not just combine the two so that employees have a better experience? That's the reason HR gives, but the reality is that it's just a big scam to get you to actually pay for a device that your company uses. Plus, since you're now checking work emails on your personal device, guess what? You're now working more (in some cases) to improve those shareholder returns.

Your email address will not be published. Imagine checking in at the airport, buying a cup of coffee at a local café, even paying for your clothes or groceries at the store's register… all with a quick wireless scan of your smartphone. It's all possible today, thanks to a new type of tech called Near Field Communications (NFC). No receipts to sign and then stuff into your pocket. The spread of NFC technology is a win-win for the customer and the merchant alike.

Securing your digital assets is a clear need for any business and individual, whether you are looking to protect your personal photos, your company's intellectual property, your customers' sensitive data, or anything else that can harm your reputation or business continuity. Although billions of dollars are spent on cybersecurity, the number of reported cyberattacks and the magnitude of breaches keep rising. There are many frontiers where harnessing the predictive power of AI might give the upper hand to security vendors -- and to us all, including individuals and businesses. Cisco forecasts that the number of connected devices worldwide will rise from 15 billion today to 50 billion by 2020. A high percentage of these devices do not have basic security measures due to limited hardware and software resources.

Securing your digital assets is a clear need for any business and individual, whether you are looking to protect your personal photos, your company's intellectual property, your customers' sensitive data, or anything else that can harm your reputation or business continuity. Although billions of dollars are spent on cybersecurity, the number of reported cyberattacks and the magnitude of breaches keep rising. There are many frontiers where harnessing the predictive power of AI might give the upper hand to security vendors -- and to us all, including individuals and businesses. Cisco forecasts that the number of connected devices worldwide will rise from 15 billion today to 50 billion by 2020. A high percentage of these devices do not have basic security measures due to limited hardware and software resources.

Securing your digital assets is a clear need for any business and individual, whether you are looking to protect your personal photos, company's intellectual property, customers' sensitive data or any other aspect that can harm your reputation or business continuity. This need will continue to grow massively over the next few years as the amount of generated and aggregated data is exploding (IDC predicts that by 2020, the volume of digital data will reach 44 Zettabytes, 1,000,000,000,000 GB 1ZB). The greatest challenge, in all disciplines of Cybersecurity, is to be able to recognize new threats efficiently without relying on any signatures or easy to bypass heuristics, which rely on known, previously-seen malicious activities. Supporting this trend, although billions of dollars are spent on cybersecurity (the latest estimate by Garter, worldwide information security spending will reach 81.6 billion in 2016), we keep seeing the growing number of reported cyber-attacks and the higher magnitude of breaches every day, for example the recently published high-magnitude cyber-breaches -- Yahoo 500M accounts data breach is among the biggest in the history, Dropbox confirmed 68M accounts details leaked. There are many Cybersecurity frontiers where harnessing the predictive power of AI might bring the upper hand to security vendors and to us all, individuals and businesses.