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    • HOME
    • TECHNOLOGY
      • Applied AI
      • Cyber Security
      • Cloud
      • Consulting
    • INDUSTRY
      • HEALTHCARE
      • PHARMACEUTICAL
      • IT / ITeS / SMEs
      • TRAVEL & HOSPITALITY
      • BANKING & FINANCE
      • INSURANCE
      • EDUCATION
      • TRANSPORTATION
      • PUBLIC SECTORS
      • MANUFACTURING
      • RETAILS
      • TELCOs
      • AUTOMOBILES
      • CO-WORKING SPACES
    • PARTNERS
    • ABOUT US
    • CAREERS
    • CONTACT US
      • Business Enquiry
      • Support

  • HOME
  • TECHNOLOGY
    • Applied AI
    • Cyber Security
    • Cloud
    • Consulting
  • INDUSTRY
    • HEALTHCARE
    • PHARMACEUTICAL
    • IT / ITeS / SMEs
    • TRAVEL & HOSPITALITY
    • BANKING & FINANCE
    • INSURANCE
    • EDUCATION
    • TRANSPORTATION
    • PUBLIC SECTORS
    • MANUFACTURING
    • RETAILS
    • TELCOs
    • AUTOMOBILES
    • CO-WORKING SPACES
  • PARTNERS
  • ABOUT US
  • CAREERS
  • CONTACT US
    • Business Enquiry
    • Support

Telecommunications

Telecommunications is one of the fastest-growing industries as well as one that uses AI

  

Together with 5G, the Internet of Things and cloud computing, artificial intelligence is radically reshaping the telecommunications landscape in an era of advanced digitization and high-speed technological development. Telecommunications is one of the fastest-growing industries as well as one that uses artificial intelligence and machine learning in many aspects of their business from enhancing the customer experience to predictive maintenance to improving network reliability.

The AI Applications for Telcos

Customer Service and Satisfaction

Fraud Detection –Data-Driven Business Decisions: Predictive Analytics

Predictive Maintenance and Improve Network Optimization

Predictive Maintenance and Improve Network Optimization

Fraud Detection –Data-Driven Business Decisions: Predictive Analytics

Predictive Maintenance and Improve Network Optimization

Fraud Detection –Data-Driven Business Decisions: Predictive Analytics

Fraud Detection –Data-Driven Business Decisions: Predictive Analytics

Fraud Detection –Data-Driven Business Decisions: Predictive Analytics

Developments in Intelligent Assurance and Analytics

Fraud Detection –Data-Driven Business Decisions: Predictive Analytics

Fraud Detection –Data-Driven Business Decisions: Predictive Analytics

Model-driven approach to Data-based Machine Learning

Swift and Secure Network Performance with Artificial Intelligence

Model-driven approach to Data-based Machine Learning

Machine Learning for Automated 5G Network Monitoring

Swift and Secure Network Performance with Artificial Intelligence

Model-driven approach to Data-based Machine Learning

Swift and Secure Network Performance with Artificial Intelligence

Swift and Secure Network Performance with Artificial Intelligence

Swift and Secure Network Performance with Artificial Intelligence

Increased network efficiency

Swift and Secure Network Performance with Artificial Intelligence

Swift and Secure Network Performance with Artificial Intelligence

Predictive & Corrective Maintenance

AI Chatbots, Voicebots and Virtual Assistants

AI Chatbots, Voicebots and Virtual Assistants

AI Chatbots, Voicebots and Virtual Assistants

AI Chatbots, Voicebots and Virtual Assistants

AI Chatbots, Voicebots and Virtual Assistants

AI Transformation in Telcos & ISPs

  

We are one of the leading company  that has been actively involved in coming up with innovative solutions to drive digital transformation in many enterprise domains. We offer consulting, implementation, integration, and support for a variety of AI applications across industries.

Artificial Intelligence is transforming the way the telecommunications industry has been functioning for many generations. Many telecom companies have started utilizing AI solutions to handle increasing network complexities, expanding networks, ever-changing communication technologies and the humongous amount of data generated as such. Artificial Intelligence for Telecom sector has helped organizations realize significant improvement in process-speeds and data-handling capacities.


Telecommunication companies are often faced with challenges like complex nature of networking systems, improper utilization of resources, traffic, congestion and delay, network and transmission failures, ever-increasing bandwidth requirements and so on. AI provides intelligible answers to these problems. Application of Artificial Intelligence in Telecom sector thus helps boost growth and revenues for these organizations.

Applications of Artificial Intelligence in Telecom industry include handling large volumes of data using machine learning and analytics, automating detection and correction of failures in transmission, automating customer care services, and complementing Internet of Things(IoT), e-mail, voice call, and database storage services.


We can help telecom organizations by providing AI-powered solutions for many of their pressing challenges. We offer consulting, development, integration and end-to-end support for implementing AI applications and frameworks. We specialize in machine learning, voice recognition, NLP and the entire spectrum of AI technologies.


For telecommunications companies, finding and building customer relationships are key to creating a growing, profitable business. Leading companies know that customer experience and streamlined operations are key to their success. Companies that are making extensive use of AI are reaping the benefits of increased customer satisfaction and loyalty while decreasing fraud and improving operations which adds to their bottom line. These companies are using AI for a number of scenarios including predictive customer support, fleet management, fraud detection, customer retention, and optimized marketing. 


Telecommunications Industry on AI areas

AI for Network Optimization

  

AI is essential for helping Telcos to build self-optimizing networks (SONs), which give operators the ability to automatically optimize network quality based on traffic information by region and time zone. Artificial Intelligence applications in the telecommunications industry use advanced algorithms to look for patterns within the data, enabling telcos to both detect and predict network anomalies, and allowing them to proactively fix problems before customers are negatively impacted.

AI for Predictive Maintenance

  

AI-driven predictive analytics are helping telcos provide better services by utilizing data, sophisticated algorithms and machine learning techniques to predict future results based on historical data. This means operators can use data-driven insights to monitor the state of equipment, anticipate failure based on patterns, and proactively fix problems with communications hardware, such as cell towers, power lines, data center servers, and even set-top boxes in customers’ homes.

Virtual Assistants for Customer Support

  

Another application of AI in telecommunications is conversational AI platforms. Also known as virtual assistants, they have learned to automate and scale one-on-one conversations so efficiently that they are projected to cut business expenses. Telcos have turned to virtual assistants to help contend with the massive number of support requests for installation, set up, troubleshooting and maintenance, which often overwhelm customer service centers. Using AI, operators can implement self-service capabilities that show customers how to install and operate their own devices.

Robotic process automation (RPA) for Telcos

  

Telcos have vast numbers of customers engaged in millions of daily transactions, each susceptible to human error. Robotic Process Automation (RPA) is a form of business process automation technology based on AI. RPA can bring greater efficiency to telecommunications functions by allowing telcos to more easily manage their back office operations and large volumes of repetitive and rules-based actions. By streamlining the execution of complex, labor-intensive and time-consuming processes such as billing, data entry, workforce management and order fulfillment, RPA frees up telcos staff for higher value-add work.

Customer Service and Satisfaction

  

Nearly every telecom uses artificial intelligence and machine learning to improve its customer service primarily by using virtual assistants, voicebots & chatbots. Telecoms get a massive number of support requests for set up, installation, troubleshooting, and maintenance. Virtual assistants automate and scale responses to these support requests, which dramatically cuts business expenses and improves customer satisfaction. 

Fraud Detection

  

Machine learning algorithms are instrumental in detecting fraudulent activity such as theft or fake profiles, illegal access, and more. These algorithms learn what "normal" activity looks like so can spot anomalies from enormous data sets much quicker than human analysts can to provide nearly a real-time response to activity that needs to be investigated.

Data-Driven Business Decisions: Predictive Analytics

  

Telecoms possess enormous amounts of data from customers. With the use of AI and machine learning, telecoms can extract meaningful business insights from this data so they can make faster and better business decisions. This crunching of the data by AI helps with customer segmentation, customer churn prevention, to predict the lifetime value of the customer, product development, improving margins, price optimization, and more.

Network Operations Monitoring & Management

  

AI and ML approaches are beginning to emerge in the networking domain to address the challenges of virtualization and cloud computing. Increased complexity in networking and networked applications is driving the need for increased network automation and agility. Network automation platforms  should incorporate AI techniques to deliver ef- ficient, timely and reliable management operations. Examples of network-centric applications of AI/ML include:


  • Anomaly detection for operations, administration, maintenance and provisioning (OAM&P)
  • Performance monitoring and optimization
  • Alert/alarm suppression
  • Trouble ticket action recommendations.
  • Automated resolution of trouble tickets (self-healing)
  • Prediction of network faults
  • Network capacity planning (congestion prediction)

Customer Service & Marketing Virtual Digital Assistants

  

One of the key applications of AI/ML in the telecom sector to date has been the use of voicebots & chat- bots to augment or replace human call center agents. It helps to increase the number of agents dealing with customer enquiries directly via messaging apps such as WhatsApp.

Other examples of AI usage in customer service/support include:


  • Knowledge portals and AI assistants for human agents
  • Contact center optimization and compliance
  • Customer voice and text sentiment analysis 


Intelligent CRM Systems

  

AI can be applied to CRM in areas such as personalized promotions, cross-sell/up-sell oppor- tunity identification, and churn prediction and mitigation. 

Customer Experience Management

  

As digital touchpoints continue to grow, analytics and AI are essential tools for telco & ISPs to understand the health of the network, the customer journey (customer care, billing, etc.), and real-time service quality. As such the CEM category intersects customer service, marketing, CRM and the service assurance side of network operations and management.

5G and Smart Networks

  

5G is the next generation of network, and will enable telecoms to apply AI to networks, making them smart, optimizing CapEx. This will improve overall network performance, optimize service levels, minimize overhead costs and maximize profitability. Using AI and analytics, telecoms can analyze coverage data like percentage of land covered with services, percentage of population covered with services, average land unavailable to services, and more. AI-powered smart networks allow for network traffic analysis in real time to identify the periods when the network usage is high, and take steps to relieve the congestion, preventing potential outages (which lead to customer dissatisfaction and churn). They can also detect areas with excess network capacity, helping telco & ISPs to launch specific marketing campaigns to increase uptake in those areas. Using AI for smart network capacity optimization can save millions of dollars every year.

Cybersecurity to Telco & ISPs

  

With technologies evolving to allow increased collaboration and distribution, the attack surface of every telecommunications and Internet Service Provider (ISP) is expanding every day. Threat actors are continuously probing for vulnerabilities, so your defenses need to be robust, managed and tested. From network and mobility needs to increased compliance to maintaining the security of customer and business data,  your Telco & ISPs organization is challenged to stay at the forefront of cybersecurity.


 TELECOM COMPANIES ARE ‘NEW TARGETS’ FOR CYBER CRIMINALS


Telecoms are huge businesses that support different business verticals. They build and operate complex networks, store huge amounts of sensitive data to meet all business communication needs. This is the main reason why this sector is highly attractive to cyber criminals. There are two types of cyber-attacks that telecom providers face. Direct attacks from criminals who aim to access their organization’s network operations and data. While on the other hand, they attack indirectly where they target an organization’s subscribers. 


 VARIOUS TYPES OF MALICIOUS ATTACKS ON TELCO & ISP


Since telecom companies are a common gateway to access multiple businesses, malicious attackers use direct cyber attacks to access the core infrastructure of a telecom company. Once they have managed to gain access to the network, cyber-criminals easily access data and control and impersonate the subscribers, simultaneously. However, it still remains a difficult task for malicious cyber criminals, yet not impossible. In recent times, the cyber attacks on these companies have increased and become more sophisticated, becoming a greater threat for everyone. Organizations move towards hiring a software testing services company to secure the telecommunications industry in an effective manner. 


 COMMON THREAT TO TELCO & ISP


Telco & ISPs face particular cyber security issues as a result of their interconnectedness and the reliance upon the international standards in their operations. For instance, telco & ISPs rely on the Signaling System 7 protocol (SS7) i.e. a standard protocol by which global companies interoperate to facilitate roaming calls and text messages. It has been confirmed that these protocols contain vulnerabilities that allow access to texts, calls and location information, other than the subscriber. It also allows calls, texts and other media to be diverted from a subscriber’s handset to that of an attacker.


Another common targets of cyber-attacks include the internet routers, both used in the backbone of the Internet and the consumer routers. Backbone routers process the data of multiple firms at the same time, while targeting these routers the hackers manage to compromise many organizations at once. Similarly, telecom companies have also faced a number of distributed denial-of-service (DDoS) attacks against the internet. It also disabled some subscribers’ routers permanently, which meant they had to replace them physically.

Most of these attacks pose great challenges since protocols such as SS7 are defined by international standards, so it is critical to acquire international cooperation to resolve such vulnerabilities. Unless these vulnerabilities are resolved, and no actions are taken to mitigate these risks, telco & ISPs are faced with growing liabilities arising from the increasing cyber incidents. 


 THE INDUSTRY FIGHTS AGAINST VULNERABILITIES


With respect to the increasing cyber issues and incidents, regulators and governments tend to increase their intervention against these protocol vulnerabilities, to the extent these or other cyber issues that begin to compromise the privacy of communications networks. Telecom companies need to ensure that proactive defence and cyber incident response plans that address the risks to such incidents. It is important to have the right people on board, otherwise the cyber-crime rate will keep increasing. Thus, it is critical to address the ever-evolving and growing sophisticated cyber crime incidents with the right cyber security measures in place. 


Since communication technologies are incorporated in other industries and beyond that traditional critical infrastructure and organizations are identifying weak areas around cyber security systems in these sectors.  We  provide a flexible approach that evolves with tech innovation and security practices for any organization. As a risk based approach, telecommunication companies protect their systems by assessing threats and also by developing and implementing appropriate risk-management practices. 

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 SIP Hacking


Session Initiation Protocol (SIP), used in most voice-over-IP (VoIP) communications, is another prime target for malicious parties. Without proper security measures, hackers can easily tap into encrypter calls, distribute SIP malware and otherwise tamper with the VoIP services you are provisioning.


Here’s a list of cybersecurity threats that are common :


  • SIP trunk hacking
  • SIP toll fraud
  • Eavesdropping
  • Caller ID spoofing
  • DDoS attacks on PBX systems


 Best Practices for Protecting SIP Signalling


  • Enforce strong encryption over your Transport Layer Security (TLS) and Real-Time Protocol (RTP) to protect all data transmissions.
  • Implement anti-spoofing for SIP messages. Ensure that you have proper in-built mechanisms for challenging messages and authenticating SIP clients.
  • Maintain strong Session Border Controller (SBC) controls that perform deep packet inspection of all SIP messages and prevent unauthorized SIP traffic.


 DNS Attacks


DNS (Domain Name Security) attacks still remain a major sore point for telco & ISPs. Telecom providers have the highest volume  of sensitive customer information stolen through DNS attacks when compared to healthcare, banking, education, and public services sectors. What this data is telling us is that most telecoms are completely unprepared for the latest cyber threats from this group. 


 DNS Attack Prevention Best Practices


  • Switch from a reactive to a proactive approach to cybersecurity. Start applying adaptive countermeasures.
  • Implement real-time analytics for DNS transactions and gradually build up a behavioral threat detection suite, capable of detecting both known and emerging cyber threats and protect against data theft/leaks.
  • Enhance your firewalls with ML-driven response policies on traffic to suspicious hostnames.
  • Implement query monitoring and logging for all suspicious endpoints.


 DDoS Attacks


Telco & ISPs are the prime target for DDoS attacks. 


 How Telco & ISPs Can Protect Against DDoS Attacks


  • Set up robust Access control lists (ACL) 
  • Implement black hole scrubbing 
  • Real-time DDoS monitoring is a must. 


 IoT Network Security


Preventing unauthorized access, securing data transmissions and ensuring smooth monitoring of a much larger attack surface are the key security challenges for telco & ISPs.

Despite low adoption, IoT devices have already proven to present both internal and external threats to cybersecurity. First of all, the device itself can be exposed to various cyber threats and vulnerabilities due to manufacturing issues. Secondly, misconfiguration and lack of proper security measures make an IoT device an easy entry-point to the entire network of devices, or worse – the supporting architecture. In short, most attackers will have an easier way of finding a leeway as the surface of attack increases.


Some of the common types of cybersecurity threats happening at network level are as follows:


  • Network congestion
  • RFIDs interference and spoofing
  • Node jamming in WSN
  • Eavesdropping attacks
  • Sybil attacks
  • DDoS attacks
  • Routing attacks
  • Offering solid protection against these is a joint responsibility between network operators and IoT users.


  

IoT Cybersecurity Best Practices 


Below are some of the key best practices the association proposes against common cybersecurity threats:


  • Network operators should use mechanisms for the secure identification of IoT devices. 
  • Enable secure authentication for all devices, networks and service platforms associated with an IoT Service.
  • Offer data encryption services to IoT service providers to ensure high communication integrity and increase network resilience.
  • Deploy private networks to support various IoT networks. These can be developed using Layer Two Tunnelling Protocol (L2TP) and secured with Internet Protocol Security (IPsec)  as Application and Device Control, Security for Data Centers and Hybrid Cloud Security.


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