Intrusion Detection using Machine Learning

Telecom operators are currently using Artificial Intelligence (AI) technologies to improve their services. Cloud providers are beginning to provide AI as a service, making the technology available to all customers. Compromising an operator is valuable to the attacker, and using AI to increase their success rate is considered a technique to do so. Telecommunications providers are also under attack from two directions: direct attacks from cybercriminals attempting to compromise their organization and network operations, and indirect attacks from those attempting to steal their subscribers’ data. Many classic attack vectors are present among the top threats now attacking each of these frontlines, but with new twists in terms of complexity or size that place new demands on telecoms businesses. The following are some of the threats:

  1. Distributed Denial of Service (DDoS) attacks
  2. The exploitation of vulnerabilities in network and consumer devices
  3. Compromising subscribers with social engineering, phishing or malware
  4. Insider threat
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Business Intelligence (BI) in Telecom Sector

BI (Business Intelligence) convert raw data into meaningful information with the help of a set of process, structure, and technology for the betterment of the organization. In a Simple way, BI can be defined as the processing of historic data to make benchmarks and KPI to identify the market trends and business status in a visual way. BI impacts an organization’s strategic and operational business with the help of actionable intelligence and knowledge. BI supports fact-based real-time decisions by using real and historical data.

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