By Doros Hadjizenonos
Regional Director – SADC at Fortinet

As the threat landscape continues to evolve rapidly, it now includes increasingly sophisticated, zero-day malware that traditional security approaches can no longer keep pace with.

As a result, security researchers estimate that the cost of cybercrime will outpace security spend by over 16X, reaching $2.1 trillion by the end of 2019.

Staying ahead of today’s accelerated cybercrime trends requires adding artificial intelligence (AI) to an organisation’s network security strategy.

The Rise of Artificial Intelligence
The goal of AI is to replicate the analytical processes of human intelligence but to enable decision making at machine speeds. The most effective AI uses a deep-learning model built around an artificial neural network (ANN).

This network is comprised of hardware and software configured after the neuron patterns in the human brain. This design not only accelerates data analysis and decision making but also enables the network to adapt and evolve based on new information.
To accomplish this, an ANN goes through a machine learning (ML) training process where implanted learning models are carefully fed vast and increasingly complex amounts of information on an ongoing basis.

Once the system has identified patterns and problem-solving strategies, it is then provided with new information that enables it to adjust its algorithms so that it can adapt to and identify new tactics and capabilities adopted by malware or an attack vector.

Fortinet and AI
As an early adopter of AI, Fortinet began developing a self-evolving threat detection system over six years ago.

This system leverages a custom-designed ANN comprised of billions of nodes, and we have been meticulously training it with new threat data every day since, giving us a significant competitive threat intelligence advantage over every other vendor in the security marketplace.
Our FortiGuard Labs team now uses this advanced AI technology to analyse files and URLs and label them as clean or malicious—at machine speeds and with a high degree of accuracy.

Training an AI
The most crucial element of any AI solution is the methodology used to train its analysis and decision-making algorithms. The ML model used to train FortiGuard AI leverages the three essential learning model strategies endorsed by the AI community:

• Supervised learning. This initial model begins the training of the AI by feeding it a vast amount of labelled data, clearly identifying the characteristics of each labelled data set, and then repeatedly applying those characteristics to unlabelled data.
• Unsupervised learning. In this next phase, the algorithm has no known solution set to follow. Instead, it recognises patterns learned in phase one that enable it to label data without human help. At this point, new data can be slowly introduced to force it to deal with data it hasn’t seen before and make new decisions.
• Reinforcement learning. The results of supervised and unsupervised learning are then “tested,” by scoring the system’s performance with unlabelled files and “rewarding” the system for good results. Training then continues to cycle between these three learning strategies on an ongoing basis.

Because of the recursive requirements of machine learning, any AI system that does not use all three of these learning models is incomplete. Each learning model helps refine results and improve accuracy.

Delivering True AI to Customers
Many cybersecurity companies claim to have introduced AI capabilities into their solutions. But the reality is, most fall short of true AI because their underlying infrastructure is too small or their learning models are incomplete.

Others refuse to divulge the methods that they use, which raises concerns about the reliability of their AI. Fortinet instead opts to be more transparent about its methodology so that customers know the breadth and depth of the analysis involved.

Sharing Intelligence across the Security Fabric
Intelligence in isolation is useless. The more it is shared, the more effective your defensive systems can become.

This is why every time a threat is identified, FortiGuard AI generates threat intelligence that automatically updates defensive signatures for every solution across the entire Fortinet Security Fabric, enabling security tools to work together to defend customers with advanced threat detection and protection solutions.
And because AI powers it, all of this happens seamlessly and behind the scenes—requiring no staff time from an organisation’s security analysts. This allows the Fortinet Security Fabric to integrate, collaborate, and automate threat detection, prevention, and remediation capabilities through sandboxing by sharing threat intelligence across each security element in real time.
Because Fortinet covers the network from end to end, we have a unique and comprehensive view that includes every component needed to protect an organisation’s ecosystem—from the data centre to multiple clouds.

This approach, unique in the industry, improves operational efficiencies while dramatically mitigating risks. And because FortiGuard AI threat detection is incorporated into the Security Fabric’s centralised visibility and controls, it also enables the network security team to work proactively based on the most accurate and timely information possible.

While 2018 was a tough year for worldwide smartphone shipments, Africa experienced year-on-year growth for first time since 2015, according to the latest figures announced by International Data Corporation (IDC).

The global technology research and consulting firm’s newly released Quarterly Mobile Phone Tracker shows the African smartphone market grew 2.3% in 2018 to total 88.2 million units, spurred by the strong performance of the continent’s three biggest markets – Nigeria, South Africa, and Egypt.

Overall mobile phone shipments were down 1.9% year on year in 2018 to 215.3 million units, with feature phone accounting for 59.0% of shipments versus 41.0% for smartphones. This overall decline mainly comes from the sluggish performance of the feature phone segment in numerous countries across the region.

2018 was the first year since 2015 that Nigeria, South Africa, and Egypt have simultaneously experienced growth in mobile phone shipments. The Nigerian and Egyptian markets recovered from declines in 2017, thanks to the relative stability of exchange rates, the stronger presence of feature phones, and the introduction of new affordable smartphones.

In South Africa, the growth was driven by local brands such as Mobicel and Stylo pushing feature phones and ultra-low-end smartphones.

Transsion brands (Tecno, Infinix, and Itel) led Africa’s feature phone space in 2018, with a combined unit share of 58.7%. Nokia was next in line with 9.6% share. Transsion, Samsung, and Huawei dominated the smartphone space with respective unit shares of 34.3%, 22.6%, and 9.9%.

However, in value terms, Samsung led the smartphone space with 36.9% share, followed by Transsion (20.2%) and Huawei (12.4%).

Local and regional brands accounted for a combined 14.3% share of Africa’s overall mobile phone market in 2018. This is broadly equal to the share of all Chinese brands in the market, excluding Transsion, which is primarily focused on serving Africa and accounted for a significant 48.7% of the total market’s volume in 2018.

“A new wave of local/regional brands are emerging across the continent,” says Taher Abdel-Hameed, a senior research analyst at IDC.

“Some emerged after restrictions were placed on imports in countries like Algeria, while others have emerged to tap into opportunities in the feature phone and entry-level smartphone segments that have been almost vacated by global brands. Despite the success of Transsion brands in both the smartphone and feature phone categories, it is also worth noting the phenomenal growth enjoyed by Huawei and its sub-brand Honor in Africa’s smartphone space. Together, these two brands saw their shipments increase by a combined 47.9% year on year in 2018, spurred by their ambitious expansion plans in emerging markets and strong focus on affordable devices.”

Looking ahead, IDC expects Africa’s overall mobile phone market to decline 0.8% year on year in 2019 to total 213.6 million units. Smartphone shipments are forecast to grow 5.4% over this period, spurred by the introduction of more affordable devices in the African market that will help drive progress in this space over the coming years. However, feature phone shipments are expected to decline 5.1% as the shift to smartphone gathers momentum.

Regarding 5G deployments, while several experiments are already underway in the region, IDC expects the commercialization of 5G services to start in most countries by 2020. However, the arrival of 5G and new designs like foldable devices are not expected to create huge momentum in Africa over the short term due to the high price tag that is attached to these devices.

“There is always the possibility of technological leapfrogging in the innovation accelerators domain when Africa’s 5G markets are considered,” says Ramazan Yavuz, a research manager at IDC.

“4G-ready devices constituted only 35% all smartphones in 2016 in Africa. Considering 4G-ready devices are expected to surpass 72% of all smartphones by 2020, 5G smartphone penetration could be expected to roll out faster when the prices become more and more affordable after initial launches.”