The trend towards globalization has led to technological advancements in many sectors, including industries. The Internet of Things (IoT) has taken off in a big way incorporating different devices and machines bringing about high levels of automation, efficiency and convenience in the industries. On the other hand, the developments have also increased the levels of security exposures. Access control systems are therefore very important in the deployment of IoT devices because they ensure that only people or systems who have been granted permission are able to interact with the devices to reduce the chances of a breach. In this entry to the blog we will explain the need for access control to IoT devices, describe different access control models found in IoT and provide rationale for the use of lightweight access control systems for IoT devices with constraints on resources.
Why Access Control is Critical for IoT Devices
Many are the current Internet of things devices, and so do the networks they connect. These devices include those used in homes and in industries, and all communicate. IoT devices are under attack, often due to their limited processing power, and the fact that they are wirelessly connected which both makes them more accessible.
Access control is the primary method of defense in every IoT system, and it makes sure that every user or service interacting with the device/network is well authorized. Whether in a home automation system or an industrial setting, poorly managed access can lead to system vulnerabilities, making IoT networks an attractive target for cybercriminals. Most IoT systems have been observed to lack any proper access control mechanisms, thus leading to rampant exploitation, including data breach, invasion of privacy, and interference with service delivery.
Access Control Models in IoT Environments
There have been various models that have been modified for purposes of IoT access control, each one of them exhibiting its own security, flexibility and performance. These include:
Discretionary Access Control (DAC): In this one, the owner of the resource determines who else gets access to the resource and how easy or hard it is to share permissions. In other words it is quite ideal in practice but may result in loss of containment since permissions are shared by many users.
Mandatory Access Control (MAC): In this model, access does not depend on users as in DAC. Instead, users are subject to policies that have been set. Policies controlled by user’s are termed as discretionary users which is the opposite as non-discretionary considering where security is paramount.
Role-Based Access Control (RBAC): As one of the earliest and the most adopted models of access control, this model allows organizations or systems to grant or deny permissions based on corresponding roles. But in an IoT context in which devices are always in an interaction amidst a dynamic behavior, RBAC can be too inflexible.
Attribute-Based Access Control (ABAC): ABAC is an advanced model which is useful for IoT based control because it incorporates various attributes such as implemented policies in order to grant access. For example, access level may be determined by user’s location, user’s device, user’s information and many more. ABAC considers real-time conditions including the device and network state before allowing or preventing access.
Lightweight Access Control for Constrained IoT Devices
In the context of IoT networks, a major problem is that a lot of the devices, more so the ones that are used in industrial and remote locations, have limited computing capacity, memory, and energy. The challenge, therefore, is to design access control systems that are efficient and lightweight, to allow them to serve the devices without endangering security.
One goal of designing lightweight access control systems is to provide a certain degree of protection, while also considering the constraints intrinsic to the IoT devices. These systems tend to be based on:
Pre Existing Policy Trees: As designing and analyzing many such dynamic conditions is not possible for lightweight systems, simpler policy trees are utilized, which involve very little computation and memory.
Lightweight Cryptography: Traditional methods of encryption are often not effective in embedding secure channels hence lightweight forms of encryption such as elliptic-curve cryptography (ECC) are utilized.
Context-Improved Decisions: In some sophisticated models, lightweight systems perform context-based decision making such as the position of the device, or the state of the network ensuring security with little or no interactive processing requirements.
Role of Fog and Cloud Computing in Access Control
The problem of insufficient performance in IoT systems has led to the proliferation of fog computing, as one of the possible solutions which is more decentralized with resources located nearer to IoT devices. By enabling data processing and access control activity at fog nodes instead of only at a data center which is the traditional cloud, the systems are able to minimize the latency experienced and increase the performance.
For example, in this case FB-ACAAC framework employs cognitive control strategy which is cloud based and uses fog nodes or both states to carry out part of the decision making process. Such architecture supports timely access control decisions which takes into consideration the context in which the device is, for instance, what access behaviors are taking place at the time and what is the prevailing environmental condition. It also has advantages in that it lessens the processing load on limited IoT systems that allow for access control mechanisms while still achieving quick turnaround times for requests for access.
Security Implications and Future Directions
With the growth of IoT ecosystems, the corresponding security threats have grown as well. Conventional access control systems were designed to manage the typical scope and structure of a network but not that of an IoT network. It is necessary to design lightweight and adaptive access control mechanisms in order to achieve security requirements in IoT networks without degrading the performance.
In the not so distant future, access control models are most likely to include the following advanced features:
Machine Learning for Behavioral Analysis: Through the implementation of machine learning algorithms, it is possible for Internet of things systems to learn the parameters of’ normal’ undermining activities’ and allow real-time changes to access control policies based on historical evidence.
Zero Trust Architectures: With a zero trust model all devices (internal or external to the network) must be authenticated and authorized before being granted access. This model is ideal for environments like the IoT where the number of devices that connect and disconnect from the networks is always changing.
Blockchain for Decentralized Access Control: Blockchain is another emerging technology that can provide a means to handle access control in a distributed manner by allowing the devices to self-verify the access requests instead of relying on a management entity. This approach also provides improved visibility and accountability.
Conclusion
The expansion of IoT networks calls for stronger measures with regard to access management, particularly with respect to the devices. Unrestricted access comes with many benefits such as ABAC flexibility or lightweight cryptography strategy but still, the future of access control in the IoT environments remains in the intersection of security, efficiency and flexibility. The emerging concepts of fog computing and machine learning as part of access control policies will enable more secured IoT networks adaptable to varying conditions. With the advent of more IoT devices, the development of new ways of controlling access to devices and information will be crucial in protecting the next generation of appliances that will be internet enabled.
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