IoT needs to focus on fraud detection

IoT needs to focus on fraud detection

The ongoing threat of cybersecurity breaches being exploited by unscrupulous hackers is not just scary for individuals, but for businesses as well. A strong, pre-emptive, future-proof system is necessary to deal with potential fraud risks.
Imagine losses as high as $1.5 million due to fraud - in fact, that's the average loss a company suffers from financial fraud in the US. The ongoing threat of cybersecurity breaches being exploited by unscrupulous hackers is not just scary for individuals, but for businesses as well. A strong, pre-emptive, future-proof system is necessary to deal with potential fraud risks.
Changing Fraud Detection
With the advent of the digital age, everything has changed rapidly. But unfortunately, while technological advances have changed the world around us in many ways, not always for good, but the bad that comes with it also affects us, even more profoundly. Беспроводной модем
With today's alarming rate of business-related fraud cases, digital technologies are vulnerable to large-scale cyber attacks.
IoT connects the world
With the widespread adoption of the Internet of Things (IoT) becoming a reality, we now live in a more connected and integrated world than ever before. While not every industry has embraced IoT wholeheartedly, most have and are working towards digital transformation. In order to quickly achieve digital transformation goals, many enterprises slightly ignore security threats.
While IoT infrastructure offers many convenience, collaboration, and productivity benefits, it also poses serious security threats, including direct attacks on IoT devices and data privacy concerns arising from IoT devices.
Even the most secure IoT devices, networks, and systems are vulnerable to malicious activity. Here are some looming cybersecurity threats.
1. DDoS attack
DDoS (Distributed Denial of Service) attack involves flooding the server with redundant requests, overloading the server, making the server offline, consuming the performance of the target server or network bandwidth, and causing the server to fail to provide services normally. GPRS ДТУ
2. IoT devices leak data
Sensitive company or employee information can easily be leaked this way. It is not difficult for hackers to obtain a publicly accessible device and compromise these IoT devices.
3. Poor Encryption
Communication channels may be the most vulnerable to cyber attacks. Unencrypted data shared on private or public networks can be stolen or altered. For IoT networks, it opens the door for hackers to break into corporate systems and networks.
4. Leakage of sensitive data
DNS poisoning, diversion and rerouting of traffic from legitimate application servers, and exfiltration of sensitive data without the knowledge or permission of the data owner are all significant challenges in this area.
Additionally, criminals are constantly finding new ways to compromise IoT devices and systems, yet fraud management teams are still using legacy systems and processes to manage or detect fraud.
According to a leading BPM (Business Process Management) organization, businesses are more prone to fraud cases than expected due to:
Taking a single approach to fraud management – this will never work when teams and individuals work in different parts and regions of the world. Instead of taking a centralized approach to fraud prevention and management, the approach scales across business processes, functions and locations.
Most analytics are based on human knowledge, past experience, a single rule-based analysis and intuition rather than standardized methods, best practices and systems.
Nonetheless, artificial intelligence (AI) and machine learning (ML) have injected a dose of optimism into the fraud detection and prevention market in the IoT space, providing a much-needed boost.
Hardening the IoT environment
As businesses continue to invest more in hardening their IoT environments, technologies such as AI and ML will be used to protect data and devices and prevent attacks. With AI solutions, valuable assets can be saved and the risk of fraud reduced through continuous monitoring and analysis.
In addition to tracking big data and transactions in real time, AI algorithms can use predictive analytics to help businesses understand past events and predict similar potential threats that may arise in the future.
AI in IoT applications can also automate decision-making. For example, machine learning algorithms constantly monitor all traffic flowing through IoT devices, providing a clear picture of normal IoT cycles or behavioral patterns. This can help detect any suspicious activity and identify these threats before they manifest themselves as larger problems.
Businesses look to learn from past mistakes
Learning from the mistakes or failures of others is critical for businesses looking to improve their IoT ecosystem and detect fraud early. Additionally, it is critical to understand why your application, device or IoT network is vulnerable to fraud.
Identifying these vulnerabilities and conducting regular IoT security audits are key to ensuring your IoT doesn’t fall prey to fraudsters.

Беспроводной последовательный модуль lora 17
Ebyte — национальное высокотехнологичное предприятие, специализирующееся на исследованиях и разработках беспроводных модулей и промышленных IoT-терминалов. Неза...
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