Rising Threats of Vishing (VoIP Phishing) and Voice Scam
As we advance further into the digital age, Voice over Internet Protocol (VoIP) technology remains a critical component of modern communication systems. Vishing, sometimes known as phone scams, scam calls or voice fraud has emerged as a significant threat, with a troubling upward trend that saw a staggering $1.2 billion stolen from victims in 2023 - representing a 554% increase and showing no signs of slowing down.
Vishing not only harms vulnerable individuals but also poses significant risks to businesses. For telecom companies, the stakes are especially high in countries where regulatory fines are imposed for failing to protect customers from cybercrime related to vishing attacks. To safeguard yourself and your business, it is important to gain a deep understanding of modern phone scams and their impact. Additionally, focusing on strengthening network security within VoIP systems is essential to effectively guard against these evolving threats.
In this article, we will look into:
- What is Vishing?
- Common Vishing Techniques
- The Significance of Automated Scam or Spam Detection in Voice Call Security
- Block Spam and Scam Calls Using AI and Machine Learning
What is Vishing?
Vishing is a blend of 'voice' and 'phishing'. It involves scammers using voice communication to trick targeted individuals into revealing personal information such as social security numbers, bank details, passwords, or other sensitive data. In contrast, phishing typically relies on email for this purpose. Both types of cyberattacks use social engineering tactics to achieve similar goals, often for financial gain.
Vishing attacks are becoming increasingly sophisticated. Scammers often use multi-stage hybrid methods, combining voice with creative messaging such as email and text. These cybercriminals invest significant time and resources to make their scams appear genuine, exploiting the trust victims place in these communication channels.
With VoIP technology, scammers can easily mask their true identity, making it challenging for recipients to identify scam likely calls or fraudulent phone calls. Scammers can leverage VoIP systems to disguise their origins, spoof caller IDs, and target a broad audience with sophisticated attack schemes bypassing traditional network security measures exposing users to these threats.
Common Vishing Techniques
Caller IDs Spoofing
Scammers use VoIP technology to manipulate caller ID information, making it appear as though the call is coming from a trusted organization or official entity. This can deceive recipients into believing the call is legitimate.
Impersonation of Trusted Entities
Vishing attacks often involve fraudsters pretending to be representatives from banks, government agencies, or well-known companies. They use persuasive tactics to convince individuals to disclose personal information.
Urgent Requests for Sensitive Information
Scammers may create a sense of urgency, claiming that immediate action is required to resolve an issue or prevent a problem. This pressure can lead individuals to provide confidential details without verifying the caller's legitimacy.
Ghost Calls (Wangiri Fraud)
These are calls that ring briefly (often just one or two rings) and then disconnect before the recipient answers. Often associated with Wangiri fraud, where the goal is to prompt the recipient to call back a premium-rate number, these calls can also be used to verify active numbers or manipulate call completion statistics.
Robocalls
Automated calls that deliver pre-recorded messages, robocalls are commonly used for telemarketing. However, they can also be employed in fraudulent schemes, disrupting users and contributing to the overall problem of unsolicited calls.
The Significance of Automated Scam or Spam Detection in Voice Call Security
Traditional fraud detection methods, such as manual screening and basic call filters, often fall short in addressing the scale and complexity of vishing attacks, particularly within VoIP systems. These conventional approaches struggle to keep pace with the sophisticated tactics used by fraudsters, highlighting the need for a more dynamic and adaptive solution.
Automated protection systems, powered by machine learning and real-time analytics, provide a crucial advancement in combating vishing. These systems continuously analyze call patterns, identify suspicious behaviors, and implement proactive measures to block or alert users about potential threats. In the VoIP environment, where fraudsters can easily spoof caller identities and disguise their origins, automated systems offer superior detection capabilities and quicker responses.
For telecommunications companies, ensuring the security of their subscribers is essential with their continued expansion of products and services such as mobile payments (M-payments) and e-wallets. The ability to protect customers from fraudulent activities directly impacts their personal and financial well-being. Implementing automated protection against scam calls not only shields users from immediate threats but also helps build long-term trust and satisfaction.
Block Spam and Scam Calls Using AI and Machine Learning
To address the growing threat of spam and scam calls, it is essential to have real-time response and automated detection solutions in place. AI and machine learning technologies excel in this domain given their ability to quickly analyze large volumes of call data, identify anomalies and suspicious user behaviors in real-time to mitigate potential threats. These systems detect suspicious patterns and behaviors in real-time, blocking or flagging fraudulent calls before they reach users.
Neural Technologies’ SCAMBlock enhances this approach through its real-time screening calls, adaptive learning, and predictive capabilities, fortified by multi-layered analytics. Our SCAMBlock solution incorporates network-based defense and advanced AI and machine learning capabilities that evolve with emerging scam and spam tactics to block or flag scam likely phone calls or fraudulent calls before they reach the subscribers.
- Analytics-Based Detection: Machine learning algorithms process extensive call data to identify patterns and anomalies linked to scam and spam activities. This data-driven approach enhances SCAMBlock’s fraud detection accuracy by distinguishing between legitimate and fraudulent calls based on learned behaviors and historical data.
- Network-Embedded Call Blocking: Integrating machine learning with network security infrastructure allows for seamless, automated blocking of suspicious calls. By leveraging real-time analytics, SCAMBlock can preemptively block potential threats before they reach the subscriber, reducing manual intervention and enhancing efficiency.
- Calling Line Identity (CLI) Modification: Machine learning systems can modify the Calling Line Identity (CLI) to manage how calls are presented to users. This capability helps SCAMBlock in filtering out unwanted calls and ensures that legitimate calls are more easily identifiable, providing better control over call disposition.
- Automated Voice Announcement Alerts: To further safeguard users, SCAMBlock’s automated voice announcement alerts can notify recipients of potential scam or spam calls. These alerts inform users about potential risks, adding an extra layer of protection and raising awareness about fraudulent activities.
- Personalized Call Filter: Offers subscribers the flexibility of a personalized call filter, allowing them to manage their own “Personal blocklist” for scam and spam numbers lists or “Personal allowlist” for important numbers. This feature grants subscribers the autonomy to curate their call preferences for individual needs and preferences.
Neural Technologies' SCAMBlock provides unparalleled network-based protection with integrated machine learning technology, transforming the way telecommunication companies combat sophisticated scam call tactics and fraud trends.
