IoT Market Growth: A Detailed Walkthrough
IoT systems consist of interconnected devices, machines, and related software services. Prepare for a surge in IoT adoption, particularly in health applications, propelled by performance and connectivity breakthroughs. According to the "The Impact of Technology in 2024 and Beyond: an IEEE Global Study" from October 2023, 54% of respondents believe that 5G will revolutionize telemedicine, facilitating remote surgery and seamless health record transmission. Additionally, an impressive 88% anticipate the standardization of faster 6G networks within the next three to five years. By the end of 2024, there are projected to be more than 207 billion IoT devices, and this value of the Iot healthcare market is predicted to grow around $150 billion. The market size of IoT devices is expected to reach approximately $289 billion by 2028.Since most established hardware companies and start-ups are interested in introducing new devices, they wish to gain a competitive advantage in the market. Due to this reason, most of these hardware systems do not have rigorous security measures in place.
IoT systems are also at a high risk of attack because of their ad hoc and resource constrained nature. However, cyber threats are a showstopper for IoT systems. Network layer attacks can cause heavy disruption and loss of information. IoT systems are particularly prone to security issues since they require cooperation in both the cyber domain and the physical domain.
Some of the common vulnerabilities that are faced by IoT security include insufficient authentication, insecure network services, privacy concerns, a lack of transport encryption and integrity verification, insecure software or firmware,
Machine Learning (ML) for IoT
The Internet of Things (IoT) is revolutionizing industries with its ability to generate vast amounts of data from millions of interconnected devices. Machine learning, fueled by this data, holds the key to unlocking valuable insights and predictive capabilities.
By leveraging historical data, machine learning algorithms can uncover hidden patterns within IoT data, enabling organizations to make informed decisions and anticipate future events. Through sophisticated analysis, machine learning inference can automate critical processes, enhancing efficiency and accuracy.
poor physical security, and insufficient routing protocols.
Classifying IoT Security Threats
Apart from the physical security measures, IoT threats can be ordered into three types:
- Denial of Services (DoS)- This type of risk denies or avoids a client’s asset on a system by presenting a futile or undesirable movement.
- Malware: Attackers use executable code to disturb gadgets on the IoT system. The assailant would be able to take advantages of blemishes in the firmware and run their product to disturb IoT engineering.
- Data Breaks: This is a problem where shielded or secret information is unveiled by the system. Aggressors can parody ARP parcels between companions on the system.
Securing IoT: ML/DL Strategies for Overcoming Security Challenges
In order to secure IoT systems, continuous monitoring and analysis are required. Because of the inordinately high amount of data involved in IoT systems, Machine Learning and Deep Learning methods can be highly effective.Machine Learning and Deep Learning have been successfully used to implement security systems, including IoT Authentication, access control, secure offloading, and malware detection methods. These Machine-learning approaches often help reduce the computational limitations of IoT devices, enhancing their security while maintaining or decreasing their onboard computational requirements. Key implementations include IoT Authentication, Access Control, Secure Offloading, Malware Detection, and Intrusion Detection:
IoT Authentication
Authentication allows IoT devices to distinguish between source nodes and outside attacks. To ensure low consumption of computation power, authentication techniques typically provide security protection by focusing on the features of radio channels and transmitters in the physical terrain of device. These physical characteristics are compared to the characteristics identified by the transmitter. To determine whether a transmission is authentic or not, the characteristics are compared to a threshold.
However, due to the IoT environment being unpredictable, it becomes difficult to choose an appropriate threshold to maximize accuracy.
Machine learning techniques such as Q learning, a reinforcement technique, can be used to select the optimal threshold to achieve highest accuracy of authentication by maximizing the number of attack transmissions correctly identified as attacks while minimizing the number of authentic transmissions wrongly identified as attacks. It can be used to find the optimal authentication policy that could result in the best outcome.
Supervised learning techniques such as Franke-Wolfe or incremental aggregated gradients can be used to increase spoofing resistance. Unsupervised learning methods, such as the Gaussian process, can be used to authenticate nearby devices while securing information related to the device.
Access Control
With access control, IoT devices prevent the access of resources by unauthorized users. Machine learning techniques like support vector machine (SVM), k-nearest neighbors, and neural networks have been used to detect unauthorized users. Due to the complexity of this type of security, computational limitations often constrain the security of low-grade IoT devices.
K-nearest neighbor can be used to identify outliers among the data, providing a method for unauthorized users. A multiplayer perceptron, a type of feed forward artificial neural network, can calculate a suspicion factor denoting the likelihood that an IoT device may be the target of attacks. Methods such as SVM have been abundantly used to detect attacks on internet traffic and electricity grids.
Secure IoT offloading
With secure offloading, IoT devices can use external, cloud based computation, and storage resources for tasks that require heavy computational power or for which latency must be minimized. Q learning can be used to identify the optimal rate of offloading data to combat jamming and spoofing attacks. This is done by determining the long term reward of offloading data based on the power jamming of the system, the maintenance of the task, the channel bandwidth and gain.
Malware detection
Supervised learning techniques can be used to identify malware by identifying atypical behavior. For example, one method uses KNN to cluster network traffic and then uses random forests to identify malware among the regular traffic. In order to limit the load on IoT devices and increase computational speed, malware detection can be offloaded to a server.
Intrusion Detection
All intrusion detection approaches can be traditionally classified into three categories misuse detection, anomaly detection, and specification-based detection. Additionally, hybrid detection approach is defined as a combination of misuse detection and anomaly detection.Misuse DetectionMisuse detection also known as signature based Intrusion detection is highly efficient in identifying known attacks. However, they do not work well when working with unknown or novel attacks since the system does not know their signatures.
Additionally, any modification to the signature can lead to an alarming increase in the false alarm rate, which decreases the effectiveness and reliability of the detection system. Typically this type of detection does not need any information on typical activity. However, it does require a mark database.
As an example, this framework does not mind how a worm finds the objective, how it propagates itself, or what transmission plot it employs. The framework would investigate the payload and mark the error, irrespective of whether it contained aworm or not.
Anomaly based Intrusion Detection
Anomaly based detection approach considers the normal activity profile of a device and considers any alternate behavior as a conflict with the normal activity. Ordinary peculiar behaviors that might be caught incorporate
- Abuse of system conventions, for example, covered IP fragments and running a standard convention on a stealthy port.
- Unique traffic patterns, for example, more UDP parcels when compared to TCP parcels
- Suspicious examples in application payload.
They are generally classified as rule based techniques, statistical models, biological models, and learning models. Among these, learning models have a more robust structure against unknown attacks than others.
Specification based systems
Specification based systems are based on specific rules that define a particular behavior. If specifications are violated, then the system thinks that there is an abnormal situation. This approach is effective in uncovering unseen attacks that may be carried out in the future.
However, setting particular specifications for the system can be a long and overwhelming task while trying to consider each and every different scenario.
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