Technology is advancing at an unprecedented pace, and as a result, the arsenal of tools available to cybercriminals to carry out sophisticated attacks is growing. Next-generation attacks, from deep-seated fakes to IoT botnets, pose a significant risk to organizations around the world. Therefore, many companies around the world are turning to modern solutions for monitoring and repelling cyber attacks, despite the relative complexity of their implementation and high cost of SOC 2 certification.
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) are a type of machine learning model that can be used to create elaborate and convincing fakes. GANs consist of two neural networks working together: one generates images or videos, and the other tries to distinguish the result from its natural counterparts.
The re-verification process continues until the generated images or videos are indistinguishable from the real ones. Of course, GANs can be used for malicious purposes – to create deepfakes.
One of the real-life deep fake hacks took place in March 2019, when a cybercriminal created a convincing video in which the CEO of a British energy company allegedly instructed a subordinate to transfer €220,000 to himself. The funds were transferred to a Hungarian bank account, further complicating the investigation.
Synthetic Personalities
Synthetic identities are artificial person profiles created by combining real and fake information. Creating a new identity may involve using stolen personal data and generating fake social media accounts. Attackers use real social security numbers, dates of birth, addresses, employment records, and more to create fake or artificial identities. Once a synthetic identity is created, cybercriminals tend to use it to carry out fraud or other types of attacks.
Existing models for detecting false identities are ineffective in 85-95% of cases. Many fraud detection methods available today lack up-to-date real-time information and support for a wide database of telemetry.
Quantum Hacking
Quantum computing is a new type of computation using quantum bits (qubits) instead of traditional ones.
Quantum computers are expected to quickly replace conventional computers, as they have much greater computing power. This makes them a significant target for attacks by cybercriminals.
Attacks on quantum computers use attacks on encryption algorithms and other types of attacks that are not possible with traditional computing. CISOs should be aware of the potential impact of quantum computing on the security of their organization and take steps to protect against such attacks.
IoT Botnets
Internet of Things (IoT) botnets are networks of hacked IoT devices, from smart thermostats to industrial control systems, that are used to carry out DDoS or other cyberattacks.
An example is the infamous Stuxnet, a piece of malware designed to attack industrial control systems. It was discovered back in 2010 and used to cause significant damage to Iran’s nuclear program. Stuxnet disabled centrifuges used to enrich uranium.
In 2016, the Mirai botnet attacked IoT devices, including routers, cameras, and DVRs, using them to launch DDoS attacks on DNS providers. The attack caused massive disruptions to a number of popular websites. IoT botnets are difficult to detect and disable because many IoT devices do not have built-in security features.
Attacks On Machine Learning Systems
Machine learning models can also become a target for attackers. Attacks on machine learning include poisoning training data and introducing bias into a model to achieve a particular outcome.
Security measures include training set encryption, as well as adversarial training, defensive distillation, and model assembly to make models more resilient to attacks by intruders.
It is also necessary to regularly update the machine learning models and the software used and, of course, review the security policies to keep them up to date and effective.
How to Protect IoT And Machine Learning Systems From Cyber Threats Using SOC
SOC (Security Operations Center) plays an important role in protecting IoT and machine learning systems from cyber threats. Here are a few ways SOC can help secure such systems:
Monitoring and discovery.
The SOC continuously monitors the network and systems for anomalous activity, including activities in IoT and machine learning systems. Using specialized tools and technologies, SOC detects potential threats and security incidents related to IoT and machine learning systems.
Incident-response.
SOC responds to detected threats and incidents, taking the necessary measures to neutralize and resolve the problem. The SOC can conduct incident investigations, gather evidence, and analyze attacks to prevent future recurrences.
Analytics and analysis.
SOC uses specialized analytical tools to process and analyze threat and activity data in IoT and machine learning systems. Data analysis allows you to detect anomalies, build behavior profiles and create signatures to detect new threats.
Incident management.
The SOC develops and implements incident management processes and procedures, including prioritization, classification, tracking, and response to security incidents. SOC ensures consistency and coordination of incident management activities to effectively address threats in IoT and machine learning systems.
Threats and vulnerabilities.
SOC monitors new threats and vulnerabilities associated with IoT and machine learning systems and ensures that they are up to date-in detection and response systems. SOC analyzes threats and their impact on specific systems in order to take appropriate protective measures.
Training and awareness.
SOC trains employees on the safe use of IoT and machine learning systems, as well as the signs and methods of detecting cyber threats. The SOC raises awareness of cybersecurity by promoting the adoption of safe practices and preventive measures.
All these measures, combined with the expertise and experience of SOC, contribute to the effective protection of IoT and machine learning systems from cyber threats.
Conclusion
Next-generation attacks pose a significant risk to organizations around the world and are pushing information security professionals to expand the types of exceptions in their infrastructure. You need to stay aware of emerging threats and take steps to prepare your organization to prevent future attacks. Despite the relatively high cost of SOC 2 certification, companies need to implement comprehensive cyber-attack preparations. UnderDefense is a company that has extensive experience in SOC 2 audit, which helps companies of any size and direction to prepare for successful certification.
