Qazi Mamoon Ashraf

Autonomic schemes for threat mitigation in Internet of Things

Internet of Things (IoT) refers to the expansion of Internet technologies to include wireless sensor networks (WSNs) and smart objects by extensive interfacing of exclusively identifiable, distributed communication devices. Due to the close connection with the physical world, it is an important requirement for IoT technology to be self-secure in terms of a standard information security model components. Autonomic security should be considered as a critical priority and careful provisions must be taken in the design of dynamic techniques, architectures and self-sufficient frameworks for future IoT. Over the years, many researchers have proposed threat mitigation approaches for IoT and WSNs. This survey considers specific approaches requiring minimal human intervention and discusses them in relation to self-security. This survey addresses and brings together a broad range of ideas linked together by IoT, autonomy and security. More particularly, this paper looks at threat mitigation approaches in IoT using an autonomic taxonomy and finally sets down future directions.

TOPSIS-Based Service Arbitration for Autonomic Internet of Things

Recent research on Internet of Things (IoT) has focused on the adaptation of the autonomic computing paradigm to make IoT self-sufficient. Service arbitration is one aspect which can greatly benefit from the adoption of the autonomic theory. Instead of allowing all deployed devices to be active, only a selected set of devices can be utilized to provide a particular service. This paper proposes a dynamic service arbitration scheme for this purpose. The approach for the service arbitration scheme is based on the technique for order preference by similarity to ideal solution (TOPSIS) algorithm. This method supplements existing autonomic frameworks with the aim to minimize user intervention as well as imparting self-configuration in the system. The analysis through TOPSIS can be extended to any number of permutations and combinations of alternatives and system policies.

Device discovery and configuration scheme for Internet of things

The term “Internet of Things” (IoT) refers to the expansion of Internet technologies to include smart objects by extensive deployment of exclusively identifiable distributed communication devices. The management of configuration in these devices, in particular, is a challenge due to the high heterogeneity in different communication protocols, communication sequences, and processing capabilities. We propose a device configuration management scheme for IoT inspired by IBMs autonomic control loop. This scheme is capable of discovering sensors despite their heterogeneity in their offered services. Furthermore, by enabling configuration management, dependence of IoT systems on user interference can be minimized.

Jammer Localization Using Wireless Devices with Mitigation by Self-Configuration

Communication abilities of a wireless network decrease significantly in the presence of a jammer. This paper presents a reactive technique, to detect and locate the position of a jammer using a distributed collection of wireless sensor devices. We employ the theory of autonomic computing as a framework to design the same. Upon detection of a jammer, the affected nodes self-configure their power consumption which stops unnecessary waste of battery resources. The scheme then proceeds to determine the approximate location of the jammer by analysing the location of active nodes as well as the affected nodes. This is done by employing a circular curve fitting algorithm. Results indicate a high degree of accuracy in localizing a jammer has been achieved.

Autonomic Internet of Things for Enforced Demand Management in Smart Grid

Recent research in the field of Internet of Things (IoT) has concentrated on the adaptation of the autonomic computing theory to make IoT self-sufficient and self-managing. The smart grid is one popular IoT application which can greatly benefit from the adoption of autonomy. In this paper, we propose the idea of enforced demand management (EDM) in smart grid as an implementation of the autonomic computing framework. Instead of allowing all consumer appliances to be active, the smart grid can actuate and control selected appliances remotely and autonomic-ally. This will allow the smart grid to be able to exercise some control over the load and consequently the demand it faces during peak hours of usage. Subsequently, the smart grid will be able to enhance efficiency and reliability. Furthermore, cellular network requirements for enabling such a method are also highlighted for the case of Long-Term-Evolution (LTE).

Critical data leak analysis in educational environment

The rapid growth of computer technologies has resulted in a corresponding increase in the creation of new loopholes in systems which eventually might result in data leak. Data leakage is an incident in which sensitive information is released to any unauthorized party intentionally or unintentionally. There is a large set of software available which can be used to prevent data leak, however they fail to cover all the channels of leakage involved. Many institutions specifically educational institutions are in possession of large amounts of personal data. Due to the sheer size of these institutions, securing each and every system is overlooked. Educational institutions need to implement higher levels of security practices in their systems to stop any attempts of users trying to access critical data intentionally. If adequate measures are not taken, records belonging to the staff as well as students can be manipulated and used. We conduct a case study, and show the potential weaknesses and vulnerabilities that can exist in an educational environment which can be a path for data leak. The results have important implications on preventive measures that can be used by such institutions to avert data leak.