DoS and DDoS vulnerability of IoT: A review

Internet of Things (IoT) paradigm became particularly popular in the last couple of years in such a way that the devices are present in almost every home across the globe. Using cheap components one can connect any device to the internet and enable information collecting from the environment, making everyday life a lot easier. Even though it does bring multiple advantages to the table, at the same time it brings certain challenges and vulnerabilities that need to be addressed. In this paper we focus on Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks and we provide a review of the current architecture of Internet of Things which is prone to these

SECURITY& PRIVACY THREATS, ATTACKS AND COUNTERMEASURES IN INTERNET OF THINGS

The idea to connect everything to anything and at any point of time is what vaguely defines the concept of the Internet of Things (IoT). The IoT is not only about providing connectivity but also facilitating interaction among these connected things. Though the term IoT was introduced in 1999 but has drawn significant attention during the past few years, the pace at which new devices are being integrated into the system will profoundly impact the world in a good way but also poses some severe queries about security and privacy. IoT in its current form is susceptible to a multitudinous set of attacks. One of the most significant concerns of IoT is to provide security assurance for the data exchange because data is vulnerable to some attacks by the attackers at each layer of IoT. The IoT has a layered structure where each layer provides a service. The security needs vary from layer to layer as each layer serves a different purpose. This paper aims to analyze the various security and privacy threats related to IoT. Some attacks have been discussed along with some existing and proposed countermeasures

Systematic Review of Internet of Things Security

The Internet of Things has become a new paradigm of current communications technology that requires a deeper overview to map its application domains, advantages, and disadvantages. There have been a number of in-depth research efforts to study various aspects of IoT. However, to the best of our knowledge, there is no literature that have discussed specifically and deeply about the security and privacy aspects of IoT. To that end, this paper aims at providing a more comprehensive and systematic review of IoT security based on the survey result of the most recent literature over the past three years (2015 to 2017). We have classified IoT security research based on the research objectives, application domains, vulner-abilities/threats, countermeasures, platforms, proto-cols, and performance measurements. We have also provided some security challenges for further research

DEDA: An algorithm for early detection of topology attacks in the internet of things

The internet of things (IoT) is used in domestic, industrial as well as mission-critical systems including homes, transports, power plants, industrial manufacturing and health-care applications. Security of data generated by such systems and IoT systems itself is very critical in such applications. Early detection of any attack targeting IoT system is necessary to minimize the damage. This paper reviews security attack detection methods for IoT Infrastructure presented in the state-of-the-art. One of the major entry points for attacks in IoT system is topology exploitation. This paper proposes a distributed algorithm for early detection of such attacks with the help of predictive descriptor tables. This paper also presents feature selection from topology control packet fields. The performance of the proposed algorithm is evaluated using an extensive simulation carried out in OMNeT++. Performance parameter includes accuracy and time required for detection. Simulation results presented in this paper show that the proposed algorithm is effective in detecting attacks ahead in time

Machine Learning based Attacks Detection and Countermeasures in IoT

While the IoT offers important benefits and opportunities for users, the technology raises various security issues and threats. These threats may include spreading IoT botnets through IoT devices which are the common and most malicious security threat in the world of internet. Protecting the IoT devices against these threats and attacks requires efficient detection. While we need to take into consideration IoT devices memory capacity limitation and low power processors. In this paper, we will focus in proposing low power consumption Machine Learning (ML) techniques for detecting IoT botnet attacks using Random forest as ML-based detection method and describing IoT common attacks with its countermeasures. The experimental result of our proposed solution shows higher accuracy. From the results, we conclude that IoT botnet detection is possible; achieving a higher accuracy rate as an experimental result indicates an accuracy rate of over 99.99% where the true positive rate is 1.000 and the false-negative rate is 0.000

Sinkhole Detection in IOT using Elliptic Curve Digital Signature

A variety of smart applications, including homes, transportation, health, and robots in industries, are starting to gain interest due to the fast expansion of Internet of Things (IoT). Smart devices are made up of sensors and actuators that actively involved in monitoring, prediction, security, and information sharing in the IoT ecosystem. These state-of-the-art (SOTA) technologies enable people to monitor and manage their unified milieu in real-time. IoT devices are nevertheless regularly used in hostile situations, where attackers try to grab and penetrate them to take over the entire network. Due to the possibility of selective forwarding, sinkhole, blackhole, and wormhole attacks on IoT networks is a serious security risk. This research offers an effective method using a digital signature to detect and mitigate sinkhole attacks on IoT networks to resolve this problem. By doing a thorough security study of this suggested system, it shows how safe it is and how resistant it is to secure sinkhole attack detection. In this study, elliptic curve digital signature algorithm is used along with the node ranker to detect the sinkhole attack in IoT environment. According to the performance analysis and experimental findings compared to other research, the suggested system offers good detection accuracy and greatly lowers the overhead associated with computing, communication, and storage