Biblio

The most widely used protocol for routing across the 6LoWPAN stack is the Routing Protocol for Low Power and Lossy (RPL) Network. However, the RPL lacks adequate security solutions, resulting in numerous internal and external security vulnerabilities. There is still much research work left to uncover RPL's shortcomings. As a result, we first implement the worst parent selection (WPS) attack in this paper. Second, we offer an intrusion detection system (IDS) to identify the WPS attack. The WPS attack modifies the victim node's objective function, causing it to choose the worst node as its preferred parent. Consequently, the network does not achieve optimal convergence, and nodes form the loop; a lower rank node selects a higher rank node as a parent, effectively isolating many nodes from the network. In addition, we propose DWA-IDS as an IDS for detecting WPS attacks. We use the Contiki-cooja simulator for simulation purposes. According to the simulation results, the WPS attack reduces system performance by increasing packet transmission time. The DWA-IDS simulation results show that our IDS detects all malicious nodes that launch the WPS attack. The true positive rate of the proposed DWA-IDS is more than 95%, and the detection rate is 100%. We also deliberate the theoretical proof for the false-positive case as our DWA-IDS do not have any false-positive case. The overhead of DWA-IDS is modest enough to be set up with low-power and memory-constrained devices.

The Internet of Things (IoT) as a demanding technology require the best features of information security for effective development of the IoT based smart city and technological activity. There are huge number of recent security threats searching for some loopholes which are ready to exploit any network. Against the back-drop of recent rapidly growing technological advancement of IoT, security-threats have become a critical challenge which demand responsive and continuous action. As privacy and security exhibit an ever-present flourishing issue, so loopholes detection and analysis are indispensable process in the network. This paper presents Block chain based security analysis of data generated from IoT devices to prevent malicious attacks and intrusion in the IoT network.

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.

In this paper, we propose a new method for optimizing the deployment of security solutions within an IoT network. Our approach uses dominating sets and centrality metrics to propose an IoT security framework where security functions are optimally deployed among devices. An example of such a solution is presented based on EndToEnd like encryption. The results reveal overall increased security within the network with minimal impact on the traffic.

Internet of Things (IoT) has an immense potential for a plethora of applications ranging from healthcare automation to defence networks and the power grid. The security of an IoT network is essentially paramount to the security of the underlying computing and communication infrastructure. However, due to constrained resources and limited computational capabilities, IoT networks are prone to various attacks. Thus, safeguarding the IoT network from adversarial attacks is of vital importance and can be realised through planning and deployment of effective security controls; one such control being an intrusion detection system. In this paper, we present a novel intrusion detection scheme for IoT networks that classifies traffic flow through the application of deep learning concepts. We adopt a newly published IoT dataset and generate generic features from the field information in packet level. We develop a feed-forward neural networks model for binary and multi-class classification including denial of service, distributed denial of service, reconnaissance and information theft attacks against IoT devices. Results obtained through the evaluation of the proposed scheme via the processed dataset illustrate a high classification accuracy.

The Internet of Things (IoT) is growing rapidly controlling and connecting thousands of devices every day. The increased number of interconnected devices increase the network traffic leading to energy and Quality of Service efficiency problems of the IoT network. Therefore, IoT platforms and networks are susceptible to failures and attacks that have significant economic and security consequences. In this regard, implementing effective secure IoT platforms and networks are valuable for both the industry and society. In this paper, we propose two frameworks that aim to verify a number of security policies related to runtime information of the network and dynamic flow routing paths, respectively. The underlying rationale is to allow the operator of an IoT network in order to have an overall control of the network and to define different policies based on the demands of the network and the use cases (e.g., achieving more secure or faster network).

The Internet of Things (IoT) is an emerging technology that plays a vital role in interconnecting various objects into a network to provide desired services within its resource constrained characteristics. In IoT, the Routing Protocol for Low power and Lossy network (RPL) is the standardized proactive routing protocol that achieves satisfying resource consumption, but it does not consider the node's routing behavior for forwarding data packets. The malicious intruders exploit these loopholes for launching various forms of routing attacks. Different security mechanisms have been introduced for detecting these attacks singly. However, the launch of multiple attacks such as Rank attack and Sybil attacks simultaneously in the IoT network is one of the devastating and destructive situations. This problem can be solved by establishing secure routing with trustworthy nodes. The trustworthiness of the nodes is determined using trust evaluation methods, where the parameters considered are based on the factors that influence in detecting the attacks. In this work, Providing Routing Security using the Technique of Collective Trust (PROTECT) mechanism is introduced, and it aims to provide a secure RPL routing by simultaneously detecting both Rank and Sybil attacks in the network. The advantage of the proposed scheme is highlighted by comparing its performance with the performance of the Sec-Trust protocol in terms of detection accuracy, energy consumption, and throughput.

Recently, as the age of the Internet of Things is approaching, there are more and more devices that communicate data with each other by incorporating sensors and communication functions in various objects. If the IoT is miniaturized, it can be regarded as a sensor having only the sensing ability and the low performance communication ability. Low-performance sensors are difficult to use high-quality communication, and wireless security used in expensive wireless communication devices cannot be applied. Therefore, this paper proposes authentication and key Agreement that can be applied in sensor networks using communication with speed less than 1 Kbps and has limited performances.

Internet of Things (IoT) is an emerging paradigm in information technology (IT) that integrates advancements in sensing, computing and communication to offer enhanced services in everyday life. IoTs are vulnerable to sybil attacks wherein an adversary fabricates fictitious identities or steals the identities of legitimate nodes. In this paper, we model sybil attacks in IoT and evaluate its impact on performance. We also develop a defense mechanism based on behavioural profiling of nodes. We develop an enhanced AODV (EAODV) protocol by using the behaviour approach to obtain the optimal routes. In EAODV, the routes are selected based on the trust value and hop count. Sybil nodes are identified and discarded based on the feedback from neighbouring nodes. Evaluation of our protocol in ns-2 simulator demonstrates the effectiveness of our approach in identifying and detecting sybil nodes in IoT network.

In the Internet of Things (IoT), smart devices are connected using various communication protocols, such as Wi-Fi, ZigBee. Some IoT devices have multiple built-in communication modules. If an IoT device equipped with multiple communication protocols is compromised by an attacker using one communication protocol (e.g., Wi-Fi), it can be exploited as an entry point to the IoT network. Another protocol (e.g., ZigBee) of this IoT device could be used to exploit vulnerabilities of other IoT devices using the same communication protocol. In order to find potential attacks caused by this kind of cross-protocol devices, we group IoT devices based on their communication protocols and construct a graphical security model for each group of devices using the same communication protocol. We combine the security models via the cross-protocol devices and compute hidden attack paths traversing different groups of devices. We use two use cases in the smart home scenario to demonstrate our approach and discuss some feasible countermeasures.