Industrial IoT, IoT Security
Article | July 11, 2023
For businesses to grow, they must be armed with the right technology and implement the right strategies to get a high return on their investments. With an IoT strategy, you can successfully make sense of the high volumes of data generated. IoT is about having devices with sensors communicate with other devices over the internet and share real-time data or parameters to maintain healthy system processes. Sharing and transferring data in real time over the cloud creates a lot of data that needs to be carefully managed.
Not having a streamlined method to control and manage the volume of data to capture, send, transmit, and receive over the cloud poses many space constraints as the data piles up quickly. Furthermore, deciding what data to keep and what to discard, how long you need the data, and for what purpose are all critical. Some standard IoT devices include sensors, lights, alarms, and cameras that a smartphone can control.
Learn about the importance of data management in establishing an IoT framework below.
The Top Reason for Establishing an IoT Framework Needs Data Management
Learning from past data trends to make future decisions in an IoT framework is critical. Data management acts as a layer between the IoT devices generating the data and the software accessing the data for analysis and services. It helps review, analyze, and navigate the massive amounts of structured and unstructured data. Defining which actions trigger responses to create data in your process is necessary to monitor your product and services and to keep your customers satisfied. In an IoT framework, managing the large amounts of data that are generated and collected means learning from the past and predicting what will happen in the future.
Why is Data Management for the IoT Framework Crucial for Medium and Large Enterprises?
Creating a better product is essential to add more value to your product offerings and avoid recalls, keeping your brand reputation at stake. The more data, the deeper the analysis, and the more refined the product, the greater the need to manage large amounts of data efficiently.
The future of IoT data management is promising when it comes to improving all aspects of your business processes, mainly controlling the automation and manufacturing processes and software triggers. Check out the in-depth benefits of data management in IoT.
Data management in IoT helps conduct a field test of your IoT products before deployment.
Improve the uptime of your business production lines and equipment.
Perform seamless decision-making for planning, scheduling, and execution systems to meet the changing customer and market demands using accurate and current data.
Data management helps efficiently deploy IoT solutions such as enterprise resource planning (ERP), enterprise asset management (EAM), and manufacturing execution systems (MES) in manufacturing businesses.
Data management helps remote monitoring of automation systems and robotic systems in industrial IoT needs current data and management.
Improve production flexibility and responsiveness by welcoming smart manufacturing using IoT data management.
When it comes to the data management of IoT devices, different types of data management systems take care of structured and unstructured data.
8 Data Management Systems for Your Enterprise IoT Devices
IoT device management means registering, organizing, monitoring, and remotely managing IoT-connected devices at scale. Various cloud architectures with different data management systems help with efficient IoT device management. In addition, equipment data, sub-meter data, and environmental data help track the performance of your IoT devices through IoT data collection. Let's find out how data management systems for IoT devices would help develop an IoT strategy for your large enterprise.
IoT gateway device management involves many steps in keeping your operations healthy and maximizing uptime. These are provisioning, authentication, configuration, control, monitoring, diagnostics, software updates, and maintenance. In addition, data management systems aim to make data available for analysis in the long term. The different data management systems are as under:
Querying
Production
Collection
Aggression/Fusion
Delivery
Pre-processing
Storage, updating and archiving
Processing or analysis.
These data management systems capture, organize, store, retrieve, and analyze data when required. Sorting out the data management in IoT will initiate your internet of things database scalability. An IoT data lifecycle is built around the data management systems in the data flow, which acts as guidelines or checkpoints for a smooth data flow across your IoT platform. Let us unfold them below.
Seven Guidelines for Cost-Effective IoT Data Management
• Querying: Accessing and retrieving data for temporary monitoring. For example, you could ask IoT devices or sensors for data in real time to learn more about trends and patterns.
• Production: Sensing and transferring data by the "things" or IoT devices in an IoT framework is the data production phase. Pushing the data to the cloud network and the IoT database servers and reporting it to the interested parties. This rich data has different formats such as audio, video, or image content, and is time-stamped and geo-stamped.
• Collection: Collecting and retrieving data for a predefined time interval and sharing it with the governing components within the gateways is a part of the collection. Filtering out valuable data and compressing it accordingly helps seamless data transfer. It is also a part of data collection.
• Aggression or fusion: Part of the aggression is real-time data transmission across the network to increase the rate of data streaming over the limited bandwidth. It pulls together information from different points of contact and reduces the amount of information that needs to be stored and sent.
• Delivery: Collating the data from multiple touch points across the IoT framework and summing it up for the final responses is a part of the data delivery management system. Making data ready for permanent data storage is also a part of it.
• Preprocessing: Removing redundant, missing, and incomplete data and making all the data unified is a part of preprocessing. Data cleaning is also one of the preprocessing methods applied to data mining.
• Storage, Update, and Archiving: Storing data in an organized way for long-term offline usage or big-data systems is a part of the storage data management system. It can be decentralized or centralized as per the required capabilities.
• Processing or Analysis: Retrieval of stored packets of data accessed for an efficient analysis is a part of data processing or analysis in a data management system.
Whenever handling large amounts of data, an efficient data management system will solve numerous problems concerning your IoT strategy, as discussed above. Find out exactly what can keep you from implementing IoT.
5 Growth Challenges in Data Management for IoT Technology
High Initial and Ongoing costs:
Upgrading the hardware and software infrastructure that is already in place, hiring IoT-trained staff, and building an IoT infrastructure will all require upfront and ongoing costs.
Vulnerability:
Your IoT security strategy is a critical aspect of your IoT platform strategy. Multiple data points for structured and unstructured data captured, transmitted, stored, and retrieved by software come with security risks.
Procuring Quality Hardware:
Finding compatible hardware for your requirements and building an infrastructure around them can take a while regarding decision-making for scalability. In addition, hardware must remain supportive of the quick adoption of future software innovations.
Installation and Upkeep of Hardware Infrastructure:
Setting up a complex IoT strategy with the implementation of IoT data management, infrastructure, security, and more takes time and expertise. One of the other big worries is keeping the hardware infrastructure in good shape so that security can't be broken.
Constraints on Scalability and Agility:
The humungous IoT data traffic poses a severe concern for appropriate control of the data storage, retrieval, analysis, monitoring, and everything aligned with IoT data management. Also, the fact that IoT data doesn't last as long as other types of data is a risk to the way data flows and is collected.
Now, let us figure out how to implement IoT that aligns with your business objectives.
How to Implement IoT in Line with Your Business Goals
A complete analysis of your immediate and long-term business objectives is critical as it helps decide which data to keep and which to discard after how much time. Every byte of data you hold and analyze comes with a cost for storage, retrieval, and security, which can be a barrier to implementing IoT for your business. Identifying IoT data collection helps you align your IoT implementation strategy with your business objectives. Here are a few ways to address your implementation of IoT.
Consider the use cases of IoT data management as per the processes involved in your business.
Implement security protocols for encryption and restricted access as per the type of business data.
Organize training for the existing workforce and hire skilled professionals in IoT.
Understand your business's data requirements, including the data collection process.
Allow enough budget for IoT infrastructure and resources.
Consider the design and development of the product as per the customer's behavior.
Consider the impact of the environmental conditions affecting your business.
Measure real-time performance metrics using a suitable IoT sensor to streamline your process.
Take automated decisions with the help of AI once IoT sensors recognize the performance gaps.
Choose the right IoT platform that defines how you communicate and handle data.
Understand that IoT implementation is a complex process and needs commitment.
Collect only the important data and statistics for a smooth workflow and to lower the cost of putting IoT into place.
Taking into account where your storage and production lines are located, choose the best ways to gather, organize, and analyze your data.
Use cold path analytics for the long term and hot path analytics for real-time data storage.
Building infrastructure with scalability in mind will help small businesses grab market share quickly and efficiently. As a result, medium-sized enterprises will find prominence in their industry. Using data visualization in business intelligence allows for rapid optimization of your IoT devices and for controlling data management costs in the long run without negatively impacting performance. Explore more about IoT data visualization down below.
Role of Data Visualization in IoT for Business Intelligence
With IoT data visualization, you can optimize business processes by applying visualization business intelligence to get your business ready to scale. Discover the role of data visualization in your IoT strategy.
Make sense of the data you've collected or saved.
Patterns and trends should be recognized.
Check the data for inconsistencies and errors. The output should then be visualized over time for analysis and monitoring.
IoT infrastructure and devices improve performance and streamline the IoT data flow.
Analyze real-time data correlations across multiple business verticals using the IoT communication platform.
Make future decisions based on the data captured in the past.
Get actionable insights on customer behavior and
Identify the factors impacting your business.
Once you identify the gaps in business processes, you can make changes to the process and further improvise. Creating an optimized workflow and detecting errors and faults in a process early are the primary goals of data management in an IoT strategy. Tackling vulnerabilities in data security and data redundancy helps the cost-effective implementation of IoT for small businesses, opening avenues for scalability. With IoT data management, you can also optimize your products to make customers happier and get a bigger share of the market, which is great for your business's growth.
Summarizing
With secure access control, encryption, software updates, endpoint security, and communication protocols in place, the relentless power of data visualization for analyzing and monitoring the captured data has proved to be unmatched. Bringing resilience and giving a rapid boost to the scalability of your medium and large enterprises is now becoming a norm with organized IoT data management.
FAQs:
• What is the most significant benefit of IoT?
IoT helps devices or sensors report real-time data for smooth interconnected production operations. In addition, IoT keeps healthy functions throughout and minimizes the turnaround time for troubleshooting and maintenance.
• What are the three types of IoT?
Depending upon the needs from time to time, the three types of IoT include short form, medium form, and long form. The short form meets immediate needs, the medium form meets future needs, and the long form keeps the system running smoothly.
• How does data analytics help IoT?
Effective process optimization is possible by analyzing the data generated in an IoT framework. It helps boost efficiency, and connectivity, cut costs and unlock scalability.
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IoT Security
Article | July 17, 2023
Modern computing devices can be thought of as a collection of discrete microprocessors each with a dedicated function like high-speed networking, graphics, Disk I/O, AI, and everything in between. The emergence of the intelligent edge has accelerated the number of these cloud-connected devices that contain multiple specialized sub-processors each with its own firmware layer and often a custom operating system. Many vulnerability analysis and endpoint detection and response (EDR) tools find it challenging to monitor and protect devices at the firmware level, leading to an attractive security gap for attackers to exploit.
At the same time, we have also seen growth in the number of attacks against firmware where sensitive information like credentials and encryption keys are stored in memory. A recent survey commissioned by Microsoft of 1,000 security decision-makers found that 83 percent had experienced some level of firmware security incident, but only 29 percent are allocating resources to protect that critical layer. And according to March 2021 data from the National Vulnerability Database included in a presentation from the Department of Homeland Security’s Cybersecurity and Infrastructure Agency (CISA) at the 2021 RSA, difficult-to-patch firmware attacks are continuing to rise. Microsoft’s Azure Defender for IoT team (formerly CyberX) recently announced alongside the Department of Homeland Security a series of more than 25 critical severity vulnerabilities in IoT and OT devices
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Security, IoT Security
Article | July 13, 2023
In the wake of the COVID-19 pandemic, manufacturing is roaring back to life, and with it comes a renewed focus on Digital Transformation initiatives. The industry stands on the doorstep of its much-anticipated renaissance, and it’s clear that manufacturing leaders need to not only embrace but accelerate innovation while managing critical processes like increasing capacity while maintaining product quality. Effective collaboration will be key to doing both well, but it’s even more critical as workforces have gone and are still largely remote.
As the virus swept the globe, it became apparent quickly that there would be winners and losers. Many manufacturers were caught off-guard, so to speak. Before manufacturing’s aforementioned reckoning, the industry had already been notorious for its slow adoption of the digital, data-centric mindset that has transformed other industries.
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IoT Security
Article | July 5, 2023
Explore the IoT security solutions for critical issues and proactive solutions for the safe implementation of connected devices. Delve into cross-domain interactions for secure data storage.
Contents
1. Introduction
1.1 Significance of IoT Security for Safe Implementation
2. IoT Security Landscape
2.1 Emerging Threats in IoT Environments
2.2 Importance of Proactive Security Measures
3. Challenges Posed in IoT Systems
3.1 Cross-Domain Interactions
3.2 Denial of Service (DoS) Attacks
3.3 Insecure Interfaces and APIs
3.4 Vulnerable Third-Party Components
3.5 Safeguarding Data Storage and Retention
4. Solutions to Prevent Threats
4.1 Secure Integration and Communication
4.2 Traffic Monitoring and Analysis
4.3 Robust Authentication and Authorization Protocols
4.4 Patch Management and Vulnerability Monitoring
4.5 Access Control and User Authentication
5 Conclusion
1. Introduction
1.1 Significance of IoT Security for Safe Implementation
The significance of IoT connectivity and security for safe implementation is paramount in today's interconnected world. Some essential points highlight its importance at both the business and advanced levels. IoT devices collect and transmit vast amounts of sensitive data. Without proper security measures, this data can be intercepted, leading to breaches of privacy and potential misuse of personal or corporate information. Implementing robust IoT security ensures the protection of data throughout its lifecycle. Safeguarding Critical Infrastructure is crucial as Many IoT deployments are integrated into critical infrastructure systems such as power grids, transportation networks, and healthcare facilities. A breach in the security of these interconnected systems can have severe consequences, including disruption of services, financial losses, and even threats to public safety. IoT security helps mitigate these risks by preventing unauthorized access and potential attacks.
Mitigating financial losses, ensuring operational continuity and preventing IoT botnets and DDoS attacks contribute to security as IoT devices are often integrated into complex ecosystems, supporting various business operations. In recent years, compromised IoT devices have been used to create massive botnets for launching distributed denial-of-service (DDoS) attacks. These attacks can overwhelm networks and cause significant disruptions, affecting the targeted businesses and the internet infrastructure as a whole. Robust IoT security measures, such as strong authentication and regular device updates, can help prevent these attacks.
2. IoT Security Landscape
2.1 Emerging Threats in IoT Environments
Botnets and DDoS Attacks
Botnets, consisting of compromised IoT devices, can be leveraged to launch massive distributed denial-of-service (DDoS) attacks. These attacks overwhelm networks, rendering them inaccessible and causing disruptions to critical services.
Inadequate Authentication and Authorization
Weak or non-existent authentication and authorization mechanisms in IoT devices can allow unauthorized access to sensitive data or control of connected systems. This can lead to unauthorized manipulation, data breaches, and privacy violations.
Firmware and Software Vulnerabilities
IoT devices often rely on firmware and software components that may contain vulnerabilities. Attackers can exploit these weaknesses to gain unauthorized access, execute malicious code, or extract sensitive information.
Lack of Encryption and Data Integrity
Insufficient or absent encryption mechanisms in IoT communications can expose sensitive data to interception and tampering. Without data integrity safeguards, malicious actors can modify data transmitted between devices, compromising the integrity and reliability of the system.
Physical Attacks and Tampering
IoT devices deployed in public or accessible locations are vulnerable to physical attacks. These attacks include tampering, theft, or destruction of devices, which can disrupt services, compromise data, or manipulate the functioning of the IoT ecosystem.
Insider Threats
Insiders with authorized access to IoT systems, such as employees or contractors, may abuse their privileges or inadvertently introduce vulnerabilities. This can include unauthorized access to sensitive data, intentional manipulation of systems, or unintentional actions compromising security.
Supply Chain Risks
The complex and global nature of IoT device supply chains introduces potential risks. Malicious actors can exploit vulnerabilities in the manufacturing or distribution process, implanting backdoors or tampering with devices before they reach end-users.
2.2 Importance of Proactive Security Measures
Security measures are vital for ensuring the safety and reliability of IoT environments. Organizations can mitigate risks and stay ahead of potential vulnerabilities and threats by taking a proactive approach. These measures include conducting regular vulnerability assessments, implementing robust monitoring and detection systems, and practicing incident response preparedness. Proactive security measures also promote a 'Security by Design' approach, integrating security controls from the outset of IoT development. Compliance with regulations, safeguarding data privacy, and achieving long-term cost savings are additional benefits of proactive security. Being proactive enables organizations to minimize the impact of security incidents, protect sensitive data, and maintain their IoT systems' secure and reliable operation.
3. Challenges Posed in IoT Systems
3.1 Cross-Domain Interactions
Cross-domain interactions refer to the communication and interaction between IoT devices, systems, or networks that operate in different domains or environments. These interactions occur when IoT devices need to connect and exchange data with external systems, platforms, or networks beyond their immediate domain. Incompatibilities in protocols, communication standards, or authentication mechanisms can create vulnerabilities and potential entry points for attackers.
3.2 Denial of Service (DoS) Attacks
Denial of Service attacks are malicious activities aimed at disrupting or rendering a target system, network, or service unavailable to its intended users. In a DoS attack, the attacker overwhelms the targeted infrastructure with an excessive amount of traffic or resource requests, causing a significant degradation in performance or a complete service outage. Protecting IoT devices and networks from DoS attacks that aim to disrupt their normal operation by overwhelming them with excessive traffic or resource requests becomes challenging. The issue here lies in distinguishing legitimate traffic from malicious traffic, as attackers constantly evolve their techniques.
3.3 Insecure Interfaces and APIs
Insecure interfaces and application programming interfaces (APIs) refer to vulnerabilities or weaknesses in the interfaces and APIs used by IoT devices for communication and data exchange. An interface is a point of interaction between different components or systems, while an API allows applications to communicate with each other. Insecure interfaces and APIs can be exploited by attackers to gain unauthorized access to IoT devices or intercept sensitive data. Ensuring secure authentication and authorization mechanisms, proper encryption of data in transit, and secure storage of API keys and credentials, thus, becomes a challenge.
3.4 Vulnerable Third-Party Components
Vulnerable third-party components refer to software, libraries, frameworks, or modules developed and maintained by external parties and integrated into IoT devices or systems. These components may contain security vulnerabilities that attackers can exploit to gain unauthorized access, manipulate data, or compromise the overall security of the IoT ecosystem. Pain points arise from the challenge of assessing the security of third-party components, as organizations may have limited visibility into their development processes or dependencies.
3.5 Safeguarding Data Storage and Retention
Data storage and retention refers to the management and security of data collected and generated by IoT devices throughout its lifecycle. Safeguarding stored IoT data throughout its lifecycle, including secure storage, proper data retention policies, and protection against unauthorized access or data leakage, poses a threat. Ensuring secure storage infrastructure, protecting data at rest and in transit, and defining appropriate data retention policies include safeguarding data and maintaining the privacy of stored data. Failure to implementing strong encryption, access controls, and monitoring mechanisms to protect stored IoT data leads to this issue.
4. Solutions to Prevent Threatsc
4.1 Secure Integration and Communication
Implement secure communication protocols, such as transport layer security (TLS) or virtual private networks (VPNs), to ensure encrypted and authenticated communication between IoT devices and external systems. Regularly assess and monitor the security posture of third-party integrations and cloud services to identify and mitigate potential vulnerabilities. Organizations need to invest time and resources in thoroughly understanding and implementing secure integration practices to mitigate the risks associated with cross-domain interactions.
4.2 Traffic Monitoring and Analysis
Deploy network traffic monitoring and filtering mechanisms to detect and block suspicious traffic patterns. Implement rate limiting, traffic shaping, or access control measures to prevent excessive requests from overwhelming IoT devices. Utilize distributed denial of service (DDoS) mitigation services or hardware appliances to handle volumetric attacks. Organizations must deploy robust traffic analysis and anomaly detection mechanisms to identify and mitigate DoS attacks promptly. Additionally, scaling infrastructure and implementing load-balancing mechanisms become essential to handle sudden surges in traffic during an attack.
4.3 Robust Authentication and Authorization Protocols
Apply secure coding practices and implement strong authentication and authorization mechanisms for interfaces and APIs. Utilize secure communication protocols (e.g., HTTPS) and enforce strict access controls to prevent unauthorized access. Regularly update and patch interfaces and APIs to address any known vulnerabilities. Organizations must conduct regular security audits of their interfaces and APIs, implement strong access controls, and regularly update and patch vulnerabilities to address these effectively.
4.4 Patch Management and Vulnerability Monitoring
Conduct thorough security assessments of third-party components before integration, verifying their security track record and ensuring they are regularly updated with security patches. Establish a process for monitoring and addressing vulnerabilities in third-party components, including timely patching or replacement. Establishing strict vendor evaluation criteria, conducting regular security assessments, and maintaining an up-to-date inventory of third-party components can help address these issues and mitigate the risks associated with vulnerable components.
4.5 Access Control and User Authentication
Encrypt stored IoT data to protect it from unauthorized access or leakage. Implement access controls and user authentication mechanisms to restrict data access based on role or privilege. Establish data retention policies that comply with relevant regulations and securely dispose of data when no longer needed. Clear data retention policies should be established, specifying how long data should be stored and when it should be securely deleted or anonymized to minimize data leakage risks.
It's important to note that these solutions should be tailored to specific organizational requirements and constantly evaluated and updated as new threats and vulnerabilities emerge in the IoT security landscape.
5. Conclusion
Ensuring the safe implementation of IoT requires overcoming various security challenges through proactive measures and a comprehensive approach. By implementing proactive security measures, organizations can mitigate risks and maintain the safety and reliability of IoT environments. Overcoming these challenges requires organizations to invest in certain integration practices, traffic analysis, authentication mechanisms, encryption protocols, and vendor evaluation criteria. Overcoming IoT security challenges for safe implementation necessitates a proactive and comprehensive approach encompassing vulnerability management, monitoring and detection, incident response preparedness, secure design practices, compliance with regulations, and robust data storage and retention mechanisms.
The emergence in IoT security encompasses the incorporation of machine learning and AI for improved threat detection, the application of blockchain for secure transactions and device authentication, the integration of security measures at the edge through edge computing, the establishment of standardized protocols and regulatory frameworks, the adoption of advanced authentication methods, and the automation of security processes for efficient IoT security management. These trends aim to address evolving risks, safeguard data integrity and privacy, and enable IoT systems' safe and secure implementation.
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