A novel multi-fold security framework for cognitive radio wireless ad-hoc networks

Cognitive Radio (CR) Technology has emerged as a smart and intelligent technology to address the problem of spectrum scarcity and its under-utilization. CR nodes sense the environment for vacant channels, exchange control information, and agree upon free channels list (FCL) to use for data transmission and conclusion. CR technology is heavily dependent on the control channel to dialogue on the exchanged control information which is usually in the Industrial-Scientific-Medical (ISM) band. As the ISM band is publically available this makes the CR network more prone to security vulnerabilities and flaws. In this paper a novel multi-fold security framework for cognitive radio wireless ad-hoc networks has been proposed. Multiple security levels, such as, encryption of beacon frame and privately exchanging the FCL, and the dynamic and adaptive behaviour of the framework makes the proposed protocol more resilient and secure against the traditional security attacks when compared with existing protocols

Assessing the Socio-economic Impacts of Secure Texting and Anti-Jamming Technologies in Non-Cooperative Networks

Operating securely over 5G (and legacy) infrastructure is a challenge. In non-cooperative networks, malicious actors may try to decipher, block encrypted messages, or specifically jam wireless radio systems. Such activities can disrupt operations, from causing minor inconvenience, through to fully paralyzing the functionality of critical infrastructure. While technological mitigation measures do exist, there are very few methods capable of assessing the socio-economic impacts from different mitigation strategies. This leads to a lack of robust evidence to inform cost-benefit analysis, and thus support decision makers in industry and government. Consequently, this paper presents two open-source simulation models for assessing the socio-economic impacts of operating in untrusted non-cooperative networks. The first focuses on using multiple non-cooperative networks to transmit a message. The second model simulates a case where a message is converted into alternative plain language to avoid detection, separated into different portions and then transmitted over multiple non-cooperative networks. A probabilistic simulation of the two models is performed for a 15 km by 15 km spatial grid with 5 untrusted non-cooperative networks and intercepting agents. The results are used to estimate economic losses for private, commercial, government and military sectors. The highest probabilistic total losses for military applications include US$300, US$150, and US$75, incurred for a 1, 3 and 5 site multi-transmission approach, respectively, for non-cooperative networks when considering 1,000 texts being sent. These results form a framework for deterministic socio-economic impact analysis of using non-cooperative networks and secure texting as protection against radio network attacks. The simulation data and the open-source codebase is provided for reproducibility

Spectrum Sensing and Security Challenges and Solutions: Contemporary Affirmation of the Recent Literature

Cognitive radio (CR) has been recently proposed as a promising technology to improve spectrum utilization by enabling secondary access to unused licensed bands. A prerequisite to this secondary access is having no interference to the primary system. This requirement makes spectrum sensing a key function in cognitive radio systems. Among common spectrum sensing techniques, energy detection is an engaging method due to its simplicity and efficiency. However, the major disadvantage of energy detection is the hidden node problem, in which the sensing node cannot distinguish between an idle and a deeply faded or shadowed band. Cooperative spectrum sensing (CSS) which uses a distributed detection model has been considered to overcome that problem. On other dimension of this cooperative spectrum sensing, this is vulnerable to sensing data falsification attacks due to the distributed nature of cooperative spectrum sensing. As the goal of a sensing data falsification attack is to cause an incorrect decision on the presence/absence of a PU signal, malicious or compromised SUs may intentionally distort the measured RSSs and share them with other SUs. Then, the effect of erroneous sensing results propagates to the entire CRN. This type of attacks can be easily launched since the openness of programmable software defined radio (SDR) devices makes it easy for (malicious or compromised) SUs to access low layer protocol stacks, such as PHY and MAC. However, detecting such attacks is challenging due to the lack of coordination between PUs and SUs, and unpredictability in wireless channel signal propagation, thus calling for efficient mechanisms to protect CRNs. Here in this paper we attempt to perform contemporary affirmation of the recent literature of benchmarking strategies that enable the trusted and secure cooperative spectrum sensing among Cognitive Radios

Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved