Living with IoT: Are We Secure?
Recent statistics reveal that by 2030, there will be more than 29 billion IoT devices across the globe. This exponential growth highlights the increasing reliance on IoT technology in various sectors worldwide. But here is the problem—many of these devices have limited processing power and memory, and as a result, they do not have strong security.
An IoT threat analysis report highlighted that 57% of IoT devices are vulnerable to medium or high-severity attacks. This means that hackers can easily penetrate your home and office networks through these devices and steal sensitive data, track and record your activities, or disrupt major business operations.
Many of these devices are what we use every day in our homes, offices, cars, and even as wearables. Therefore, it is important to be familiar with the risks associated with them and the best ways to minimize your chances of getting hacked.
Common IoT Security Risks
Without a doubt, IoT devices are prime targets for hackers, who continually develop new methods to exploit these systems’ inherent weaknesses. Some of the common IoT security risks include:
Data Breaches
Data breaches involve the unauthorized access and retrieval of sensitive information from a system. These breaches result from hacking, malware attacks, insider threats, or accidental leaks.
What’s more, their impact can cause identity theft, financial loss, reputational damage, and legal consequences for individuals and organizations.
In the IoT landscape, data breaches are common threats due to their interconnected nature and limited security features. Additionally, a breach of one IoT device can potentially compromise the entire network and cause widespread data exposure.
Botnets
Botnets are networks of compromised devices, often referred to as “zombies,” that are controlled remotely by cybercriminals. These networks can be used to launch large-scale attacks, such as Distributed Denial of Service (DDoS) attacks, which overwhelm a target system with a flood of traffic, rendering it inaccessible.
IoT devices are particularly susceptible to being recruited into botnets due to their often weak security measures and the sheer number of devices in use. Once compromised, these devices can be directed to participate in attacks without the owners’ knowledge, creating significant security and operational challenges for targeted entities.
The Mirai botnet is an example that exploited vulnerable IoT devices to launch massive DDoS attacks, demonstrating the potential scale and impact of such threats.
Ransomware
Ransomware is a type of malicious software where an attacker penetrates a network and encrypts the victim’s files or locks them out of their system. Then, they demand a ransom payment, often in cryptocurrency since it’s untraceable. This malware can spread through phishing emails, malicious advertisements, and other IoT software vulnerabilities.
Ransomware can infiltrate IoT networks, encrypt data, or lock devices and cause severe disruptions to essential services such as healthcare systems, smart homes, and industrial operations. The proliferation of interconnected IoT devices expands the attack surface, and the critical nature of many IoT applications can pressure victims to pay the ransom quickly to restore functionality.
Skills Gap
The skill gap in cyber security is another critical threat to IoT security. Despite the increasing reliance on IoT devices, the shortage of skilled cybersecurity professionals leave these systems vulnerable to cyber threats. According to CyberSeek, there are approximately 460,000 unfilled cybersecurity jobs in the U.S. alone, with just over 1.2 million professionals currently employed in the field.
This gap leaves IoT systems exposed to various threats because the rapid evolution of cyber threats outpaces the current capacity of the cybersecurity workforce to address them. The shortage of qualified cybersecurity experts means that many IoT devices and networks may not receive the necessary oversight and protection they need. As a result, they become easy targets for malicious actors.
Addressing this skills gap requires a comprehensive approach, such as better training for existing professionals, increased educational efforts to produce more cybersecurity experts, and the meticulous use of AI and automation to enhance the capabilities of the current workforce. Without these measures, the integrity and security of IoT systems will remain at significant risk.
Practical Solutions to Enhance IoT Security
As more devices are connected to a network, the potential for exploitation increases. Each device, whether a simple sensor or a complex machine, is a potential entry point for attackers.
Hackers can exploit weak security configurations, outdated software, or shallow network protections in these devices and wreak havoc. So, how can you shrink these attack surfaces and make it difficult for an attacker to penetrate? Here are a few simple ways to do that.
Secure Log-in Credentials
Default log-in credentials are easily guessable and often well-known by hackers. For every IoT device connected to your home or office network, change the default user ID and password to something strong and unique.
Longer passwords are generally more secure. If possible, use the maximum length allowed by the system. Additionally, enable multi-factor authentication (MFA) for an additional layer of security.
Optimize Network Configuration
Create a dedicated network segment for IoT devices only. Most home routers support guest networks or DMZ areas, which can be used for this purpose.
This separation helps contain any vulnerabilities within the IoT segment alone, protecting other devices on your network. Change the default SSID, use a strong Wi-Fi password, and consider making the network non-discoverable to reduce the risk of unauthorized access.
Ensure Regular Patching and Updates
Configure your IoT devices so they can automatically receive updates as soon as they are released. This will ensure they always have the latest security patches and software versions, reducing the risk of exploitation by attackers.
Additionally, regularly verify the availability of new updates from the device manufacturer’s website or user portal to ensure no critical updates that need to be installed manually are missed.
Principle of Least Privilege
Turn off any unnecessary functions and services on your IoT devices and enable only the features that you actually use and need. Ensure that only authorized users can access your IoT devices. Additionally, evaluate the necessity of remote access features. If possible, disable remote access if it is not essential.
Power Management and Physical Security
If you don’t need an IoT device to be active, such as security cameras or other monitoring devices, turn them off or unplug them to prevent exploitation when not in use. Additionally, ensure that devices are physically secure and not easily accessible to unauthorized individuals who might tamper with them.
AI in IoT Security: Friend or Foe?
Artificial Intelligence (AI) is one of the trending topics in both technology and general discussions. Even individuals who aren’t tech-savvy are aware of its various applications. But what’s even hotter, especially after the recently concluded 2024 RSA conference, is the intersection between AI and IoT security.
From a cybersecurity standpoint, AI plays a dual role in IoT security. On one hand, cybersecurity professionals use AI to perform advanced root cause analysis and detect indicators of compromise quickly. On the other hand, cyber attackers exploit AI to craft sophisticated attacks and manipulate system data, posing new and complex threats. This dynamic interplay between AI and IoT creates both opportunities and challenges in IoT security.
Role of AI in IoT Security
AI is increasingly intertwined with the IoT security landscape. This integration offers both promising solutions. Let’s discuss how AI impacts IoT security:
Reduction in Cost and Response Time to Breaches
AI and automation are important in reducing the cost and response time associated with data breaches. According to the IBM Cost of a Data Breach survey report, the average savings for organizations that use security AI and automation extensively is $1.76 million compared to organizations that don’t.
Given that the average data breach costs approximately $4.5 million, this represents a significant cost savings.
Furthermore, AI can reduce the mean time to identify and contain a breach. IBM reported that businesses employing AI and automation extensively in their security operations were able to shorten the average data breach lifecycle by 108 days. This translated to a cost savings of $850,000 per attack—up to 30% less than the average impact.
Enhanced Data Analysis
AI excels in analyzing large datasets to identify patterns, anomalies, and outliers, which are crucial for security. Machine learning, a subset of AI, enhances this capability by continuously learning from data to improve its analysis.
It can detect unusual behavior that deviates from normal patterns, flagging potential security threats in real-time. This allows for more precise and proactive security measures, significantly reducing the likelihood of undetected breaches and enabling quicker responses to threats.
Improved Incident Response
AI can significantly improve incident response through automation and intelligent analysis. One of the key benefits is the ability to generate playbooks on the fly. Playbooks are essential for incident response, outlining the steps that need to be taken when a security incident occurs.
Generative AI can create these playbooks dynamically by analyzing the specifics of the incident in real-time, incorporating the latest threat intelligence and adapting to the unique characteristics of the threat. This ensures that the response is tailored to the specific incident.
Natural Language Processing
Generative AI models, such as large language models and foundation models, excel in natural language processing. This capability allows users to interact with AI systems using natural language queries, making it easier to obtain and understand technical information.
For example, a user can ask, “Are we being affected by this particular kind of malware?” and the AI can build and run the necessary queries to provide a detailed response quickly. This intuitive interaction simplifies the process of accessing critical security information and makes it more accessible to non-technical users.
Summarization and Trend Analysis
Generative AI can effectively summarize large volumes of information, which is particularly useful for incident summarization and case analysis. This capability enables security professionals to quickly understand the trends and commonalities among different security incidents.
AI’s ability to quickly identify patterns and connections can help security teams understand the broader context of security threats and be able to make informed decisions and conduct strategic planning to handle them.
AI-Driven Threats to IoT Security
AI technologies have the potential to significantly enhance IoT security by providing more efficient and effective methods for detecting and mitigating threats. However, the same AI technologies can also be exploited by malicious actors to launch sophisticated attacks on IoT systems. Here are some AI-driven threats to IoT security:
Phishing Attacks
Traditionally, phishing attacks were often detectable due to poor English grammar and awkward phrasing. However, AI-powered chatbots can now generate natural-sounding language with correct grammar and punctuation.
These sophisticated chatbots can quickly craft convincing phishing emails that are difficult to distinguish from legitimate communications. Although some chatbots have safeguards to prevent the generation of phishing emails, attackers have their ways to re-engineer the prompts and successfully generate them.
If an IoT device’s user credentials are compromised through such a phishing attack, the attacker can gain control over the IoT device. This could lead to unauthorized access, data breaches, and manipulation of the device’s functions.
Malware
Generative AI can quickly and effectively write code. Unfortunately, this means it can also generate malware. Attackers can utilize AI to insert malware or backdoors into the code and compromise the security of IoT systems. Once malware is introduced, the hacker can have complete control over the device.
For example, a hacker could use AI-generated code to create a seemingly legitimate software update for a smart home device, which, once installed, could open a backdoor, allowing the attacker to access and control the device remotely.
This makes it crucial to thoroughly verify AI-generated code to ensure it is pure and functions as intended. Without meticulous verification, there is a substantial risk that AI-generated code may include malicious elements, potentially leading to severe security breaches.
Data Poisoning
Data poisoning is another significant threat to IoT security involving AI. This occurs when attackers deliberately introduce corrupt data into the training datasets of machine learning models used in IoT systems. By poisoning the data, attackers can manipulate the behavior of AI algorithms, causing IoT devices to malfunction or behave unpredictably.
For example, in smart home systems, poisoned data could lead to the AI misinterpreting sensor inputs, resulting in incorrect actions such as leaving doors unlocked or failing to detect smoke or intruders.
In industrial IoT applications, data poisoning could disrupt operations by causing machinery to operate outside safe parameters, potentially leading to accidents or equipment damage. Preventing data poisoning requires rigorous data validation, continuous monitoring of AI model performance, and implementation of robust security measures to protect the integrity of the training dataset.
Deepfakes
Deepfakes are a type of synthetic media created using artificial intelligence techniques, particularly deep learning, to manipulate or generate audio, video, or images that appear authentic. These techniques enable the creation of highly realistic digital content where people can be made to say or do things they never actually did.
IoT devices, such as smart security cameras, voice-activated assistants, and biometric systems, are particularly vulnerable to manipulation by deepfakes. For example, deepfake audio could trick voice-activated systems into executing unauthorized commands, while manipulated video feeds could deceive security cameras into failing to detect intruders.
Securing the Future of IoT: A Collaborative Effort
The rapid expansion of IoT devices presents significant security challenges that cannot be ignored. Data breaches, ransomware, and deepfakes are just a few of the threats that exploit these vulnerabilities.
Practical steps like securing log-in credentials, optimizing network configurations, and leveraging AI’s strengths are essential for protecting against these risks.
This responsibility does not lie solely with users; manufacturers must also prioritize security from the design stage and ensure regular updates. By working together, we can safeguard the integrity of our interconnected world and build a more secure IoT future.
At embedUR, we believe in the power of collaboration to build a secure IoT landscape. Explore our extensive resources and articles to learn more about IoT and embedded systems.
