It Was Not Until The 1960s That The Spark For The Concept We Now Call The Internet Of Things Was Sparked. However, In The Early 1980s, IoT Ecosystem Commercial Products Became Widely Available, With Carnegie Mellon University Student Mike Kazar Being The First Flagship Device To Be Able To Connect To The Internet. 

“What we did was like a joke, and we never imagined that our invention would connect billions of devices to the Internet,” said Kazar, who now works as a network and Internet of Things engineer at Microsoft. »

The beating heart of the Internet of Things is microcontrollers, which have the same functionality as computer chips. They have less processing power than computer CPUs and even smartphones. Therefore, they do not have the ability to perform complex tasks such as pattern recognition locally on IoT equipment.

To solve this problem, IoT equipment must send the collected data for analysis to data centers and cloud-based services, which is a time-consuming process and creates many security challenges, the most important of which is eavesdropping or manipulating information.

Utilizing powerful mechanisms to identify the dangerous activities of nodes is the first step to protecting IoT networks. Security experts have suggested various solutions to these security threats, including fast fuzzy pattern tree methods to detect and classify malware.

IoT companies use common mechanisms such as encryption, authentication, access control, implementing multi-layered security mechanisms, and reviewing embedded application source code to minimize hidden vulnerabilities in devices to overcome security problems.

 However, another powerful complement is needed for security mechanisms to more effectively protect the IoT ecosystem.

These are powerful supplements to machine learning and deep learning that have undergone significant advances in recent years, with machine intelligence and functionalities moving from laboratory environments to real-world machinery. The ability to intelligently monitor IoT devices creates a powerful defense umbrella around IoT equipment so that hackers can not easily sacrifice IoT equipment.

Machine Learning and Deep Learning Using powerful data mining mechanisms, they try to distinguish between normal and abnormal behavior of sensors and embedded systems to detect any suspicious activity of devices and sensors, send alerts to relevant operators, or automatically take precautions. Give. This is why IoT devices today are significantly different from their predecessors in security and a secure connection with other devices.

This article will look at the machine learning solutions and recent developments in this field that have better the security of IoT systems than before.

Machine Learning and Deep Learning Using powerful data mining mechanisms, they try to distinguish between normal and abnormal behavior of sensors and embedded systems to detect any suspicious activity of devices and sensors, send alerts to relevant operators, or automatically take precautions.

This is why IoT devices today are significantly different from their predecessors in security and a secure connection with other devices. This article will look at the machine learning solutions and recent developments in this field that have better the security of IoT systems than before.

Machine Learning and Deep Learning Using powerful data mining mechanisms, they try to distinguish between normal and abnormal behavior of sensors and embedded systems to detect any suspicious activity of devices and sensors, send alerts to relevant operators, or automatically take precautions.

This is why IoT devices today are significantly different from their predecessors in security and a secure connection with other devices. This article will look at the machine learning solutions and recent developments in this field that have better the security of IoT systems than before.

The security challenge in the IoT ecosystem!

IoT networks have tremendous potential for magnification. In the short term, we encounter large networks of intelligent equipment and sensors that manage them, process the large amount of data they produce, and the media needed to store that amount of data and the communication mechanisms.

Having the ability to transmit information momentarily and providing security are important issues that need to be addressed.

In general, hackers attack three groups of IoT services and applications:

1.  Attacks include injecting commands into databases, XSS scripting, and directory navigation. This group includes well-known attacks against web applications over the past decade. These kinds of vulnerabilities are easy prey for attackers who want to attack an IoT device. The attack can be performed semi-automatically using existing open-source tools. Attackers can use standard security scanners to identify vulnerabilities.
2.  Bottom line utilization of the device software to detect buffer overflows or memory problems that cause the device to be disabled or execute arbitrary code. Exploiting this type of vulnerability requires reverse engineering skills at the machine code level and familiarity with assembly language and CPU instructions. Thus, carrying out such attacks in the real world, although potentially causing more damage, is more difficult to implement than group I attacks.
3.  Attacks based on vulnerable protocols aimed at breaking the mechanisms of non-authentication, encryption, and verification. An attacker could use this type of attack to steal information and manipulate sensitive network data. Over the past few years, vulnerabilities related to these three groups have been identified in IoT equipment.

Artificial intelligence enters the field.

Companies have turned to common security solutions such as data encryption and security analytics solutions to address IoT security challenges. Still, over time, traditional solutions have found it difficult to protect IoT networks.

Accordingly, companies have moved to machine-based solutions that perform better than traditional patterns. Machine learning and deep learning come in many forms around us.

Today, these powerful technologies are used by big companies like Google and Facebook to monitor all the activities of users on social networks and search engines. Whenever users enter dialogs into search fields, they show them the results of past activities.

However, the field of activity of intelligent algorithms is increasing day by day and entering various industries. Research shows that traditional solutions have failed to secure IoT networks well, and therefore the IoT ecosystem needs a double layer of intelligent security to improve equipment security.

Hackers can use search engines to identify vulnerable devices connected to the World Wide Web in a short period of time.

Obvious or hidden threats and dangers in IoT networks have led organizations to resort to machine learning to identify and block cyberattacks that target traffic on these networks. Machine-based security solutions focus on analyzing behavioral patterns and identifying suspicious activity rather than access control and encryption.

In the first group, which are network-based machine learning techniques, organizations can use the following solutions:

• Machine learning with an observer and algorithms such as CNN and AdaBoost has few computational limitations but a high data loss rate.
• Unsupervised machine learning and the use of algorithms such as DBSCAN, which have low computational constraints and high data loss rates.
• Learning machine amplification and algorithms such as DQN and SARSA have low computational constraints and moderate data loss rates.

The second group includes host-based machine learning techniques, which are divided into the following three groups.

1. Machine learning with an observer and algorithms such as SVM and k-NN has many computational limitations and a low data loss rate.
2.  Unsupervised machine learning and algorithms such as K-Means and GGMs have many computational limitations and low data loss rates.
3. Machine learning and the use of algorithms such as Q-Learning, which have many computational limitations and a moderate data loss rate.

As you may have guessed, in a network-based model, data loss rates are high and, therefore, not a good choice for large IoT networks.

Some important research on how to apply machine learning to improve the security of the Internet of Things ecosystem

To use artificial intelligence in the IoT ecosystem, researchers conducted extensive research that includes how to use machine learning and in-depth learning in classifying intrusion detection systems.

For example, the PriModChain framework, which aims to maintain the confidentiality and reliability of IoT data, uses a combination of machine learning and blockchain algorithms.

One of the biggest features of this feasibility framework is based on the five criteria of confidentiality, security, reliability, security, and retrieval.

MCUNet system

Researchers at MIT have developed a system called MCUNet that integrates machine learning into microcontrollers. This great achievement can improve the security and performance of IoT devices and sensors.

Ann Jill, a Ph.D. student in MIT’s Department of Electrical Engineering and Computer Science, described the breakthrough as a milestone in countering hacking attacks and improving systems performance.

“How can we implement neural networks directly on small IoT devices?” Says Anne Lane. This is a completely new and hot field of research because you need to know enough about the IoT ecosystem, infrastructure, artificial intelligence, and deep learning concepts.

Companies like Google and Logo are doing extensive research in this area.

The system can insert deep neural networks into IoT ecosystem equipment, small chips designed for medical and wearable equipment, and home appliances. The new system, called MCUNet, can make the most of IoT equipment’s limited memory and processing power, allowing smart devices to analyze data more quickly and accurately and perform some tasks automatically. This technology helps to improve equipment security while saving energy.

Because deep neural networks are made up of millions of nodes and neural cells. They transfer large amounts of data between different layers to process and make decisions, requiring large processing power and storage media.

This research team has designed two specific components for Tiny Deep Learning that can implement deep neural network performance on microcontrollers.

The first component of TinyEngine is an inference engine that manages resources and functions like an operating system, or more precisely, a middleware.

TinyEngine is optimized to execute the structure of a specific neural network selected by MCUNet. The second component is called TinyNAS, which is an active algorithm.

The intelligent glass-broken access control mechanism

Yang Tang, a professor at Fuzhou University in Singapore, and a team of researchers at the university have developed a security system that provides a two-tier access control mechanism for securing patients’ medical data capable of normal or emergencies.

Compatible to work. Under normal circumstances, health center staff will have access to the required data with secret keys. Still, in emergencies, such as when the ecosystem is exposed to a hacker attack, employees will access the patient’s medical records using the advanced lightweight break access mechanism. -glass) which is password-based to retrieve information.

This access control mechanism has significant multifaceted features such as sharing cross-platform data, managing emergencies and defining shared access-based policies.

Non-parametric sequence-based learning approach to detect suspicious activity in the Internet of Things ecosystem

In another example, Nashreen Nesa and its team designed an intelligent mechanism for detecting junk and unhealthy data in the IoT ecosystem. The team uses a non-parametric approach that does not require large storage space to store input data and provides a convenient solution for in-network data.

This technique uses a sequence-based observer learning approach to detect outdated data. Experiments show that the correct detection rate of this algorithm was 98.53 to 99.65%.

Securely distributed computing for smart IoT networks

In another study, Jee Young Lee and his team developed an access control mechanism with a new decryption method for IoT equipment. Using an unsupervised machine learning approach, the research team was able to drive computational complexity from sensor nodes to the gate to improve the performance of embedded applications. In addition, they use blockchain to ensure access control.

Strengthen IoT security by validating wireless nodes using machine learning 

Baobab Chatterjee and his team have developed an artificial neural network (ANN) authentication mechanism for IoT networks. Authenticity based on the physical function can perform satisfactorily on IoT networks that analyze the physical properties of the sender. Researchers tested the neural network on 4,800 transmitters to measure system error.

This test showed that the system error rate is less than one-tenth of a thousandth, and with increasing the number of transmitters by 10,000, this error rate reaches 0.1%. In addition, in terms of performance, the systems did not generate any additional overhead.

This smart model uses the asymmetric Radio-Frequency (RF) communication framework and does not require any additional circuitry to extract the features. The mechanism of this intelligent algorithm is very similar to the hearing of the human brain.

last word

The Internet of Things has connected billions of intelligent devices and sensors in such a way that they can interact with each other with minimal human intervention.

Providing a wide range of services, optimizing energy consumption, and reducing costs are the most important achievements of the Internet of Things. On the other hand, the cross-cutting nature of IoT systems and the components for which multiple applications are defined have led to new security challenges.

The Internet of Things has grown rapidly in recent years, with widespread applications leading to the transfer and processing of significant amounts of large amounts of data on IoT networks.

In a situation where cloud computing has played an influential role in this regard, it has created various security risks and concerns.

Edge computing, which is used to decentralize, distribute, and transfer computing to IoT nodes, has led to serious gaps in security solutions.

In addition, IoT nodes, which run mostly built-in applications, are the primary target vectors that hackers use to access IoT networks. For this reason, researchers have begun to look at ways to secure IoT networks based on artificial intelligence technology.

Because traditional solutions focus on access control and encryption, they do not provide a comprehensive solution for protecting IoT networks, which is why researchers consider machine learning and deep learning to be the best option.