Spectrum Sensing Security in Cognitive Radio Networks

This thesis explores the use of unsupervised machine learning for spectrum sensing in cognitive radio (CR) networks from a security perspective. CR is an enabling technology for dynamic spectrum access (DSA) because of a CR's ability to reconfigure itself in a smart way. CR can adapt and use unoccupied spectrum with the help of spectrum sensing and DSA. DSA is an efficient way to dynamically allocate white spaces (unutilized spectrum) to other CR users in order to tackle the spectrum scarcity problem and improve spectral efficiency. So far various techniques have been developed to efficiently detect and classify signals in a DSA environment. Neural network techniques, especially those using unsupervised learning have some key advantages over other methods mainly because of the fact that minimal preconfiguration is required to sense the spectrum. However, recent results have shown some possible security vulnerabilities, which can be exploited by adversarial users to gain unrestricted access to spectrum by fooling signal classifiers. It is very important to address these new classes of security threats and challenges in order to make CR a long-term commercially viable concept. This thesis identifies some key security vulnerabilities when unsupervised machine learning is used for spectrum sensing and also proposes mitigation techniques to counter the security threats. The simulation work demonstrates the ability of malicious user to manipulate signals in such a way to confuse signal classifier. The signal classifier is forced by the malicious user to draw incorrect decision boundaries by presenting signal features which are akin to a primary user. Hence, a malicious user is able to classify itself as a primary user and thus gains unrivaled access to the spectrum. First, performance of various classification algorithms are evaluated. K-means and weighted classification algorithms are selected because of their robustness against proposed attacks as compared to other classification algorithm. Second, connection attack, point cluster attack, and random noise attack are shown to have an adverse effect on classification algorithms. In the end, some mitigation techniques are proposed to counter the effect of these attacks

Coexistence Analysis between Radar and Cellular System in LoS Channel

Sharing spectrum with incumbents such as radar systems is an attractive solution for cellular operators in order to meet the ever growing bandwidth requirements and ease the spectrum crunch problem. In order to realize efficient spectrum sharing, interference mitigation techniques are required. In this letter we address techniques to mitigate MIMO radar interference at MIMO cellular base stations (BSs). We specifically look at the amount of power received at BSs when radar uses null space projection (NSP)-based interference mitigation method. NSP reduces the amount of projected power at targets that are in-close vicinity to BSs. We study this issue and show that this can be avoided if radar employs a larger transmit array. In addition, we compute the coherence time of channel between radar and BSs and show that the coherence time of channel is much larger than the pulse repetition interval of radars. Therefore, NSP-based interference mitigation techniques which depends on accurate channel state information (CSI) can be effective as the problem of CSI being outdated does not occur for most practical scenarios.Comment: Corrected some typos and reference

Neuro-cognitive virtual environment for children with autism (VECA).

Autism a neurological disorder which is often diagnosed during early childhood and can cause significant social, communication, and behavioral challenges over a lifetime. It is increasing day by day and people are inclining from clinical and psychological therapies to assistive technologies. We have developed an interactive virtual environment VECA that aims to enhance the cognitive skills and creativity in children with autism by playing games and interacting with the environment. The setup also incorporates the feedback of the child that whether he/she is comfortable with the environment or not. This solution is cost effective, with no side effects unlike traditional therapies, and can provide valuable insight to the behavior analysis of the autism patients

Security Threats to Signal Classifiers Using Self-Organizing Maps

Spectrum sensing is required for many cognitive radio applications, including spectral awareness, interoperability, and dynamic spectrum access. Previous work has demonstrated the ill effects of primary user emulation attacks, and pointed out specific vulnerabilities in spectrum sensing that uses featurebased classifiers. This paper looks specifically at the use of unsupervised learning in signal classifiers, and attacks against self-organizing maps. By temporarily manipulating their signals, attackers can cause other secondary users to permanently misclassify them as primary users, giving them complete access to the spectrum. In the paper we develop the theory behind manipulating the decision regions in a neural network using self-organizing maps. We then demonstrate through simulation the ability for an attacker to formulate the necessary input signals to execute the attack. Lastly we provide recommendations to mitigate the efficacy of this type of attack

Role of dams in the economic growth of Pakistan

Pakistan is located in a semiarid to arid region where rainfall is highly deficient and does not match the crop requirements. In most plain areas of the country it is less than 500 mm and is unevenly distributed over the year. As Pakistan is not an oil rich country, its economy depends on agriculture sector which accounts for about 23 % of the GDP and 42% of total employed labour force. It is also the largest source of foreign exchange earnings. Agriculture of country is mostly dependent on waters of Indus River System (IRS). IRS maintains World’s largest integrated irrigation network called Indus Basin Irrigation System (IBIS). IBIS is fed with waters derived from Indus and its five major tributaries. As a result of Indus Water Treaty with India in 1960, Indus Basin Project (IBP) works were constructed during the sixties and the seventies. Two mega multipurpose projects (Mangla and Tarbela dams), five barrages, one gated siphon and eight inter-river link canals were constructed to regulate and convey water of western rivers to irrigation canals taking off from eastern rivers. Pakistan Water and Power Development Authority (WAPDA) completed the construction of all sixteen IBP components within a decade. Two multipurpose dams, Mangla (Live Storage 6.6 billion cubic meter (BCM), Installed Capacity 1000 MW) and Tarbela (Live Storage 11.9 BCM, Installed Capacity 3478 MW) were built on Jhelum and Indus Rivers respectively. These multipurpose mega dams provide about 70% of total existing storage capacity and hydropower infrastructure (producing one fifth of the country’s electricity during 2007-08). These dams were constructed to regulate and supplement flows in irrigation network to sustain Pakistan’s agriculture. These dams are operated primarily according to irrigation requirements of the country while inexpensive hydroelectricity is produced as a byproduct. This paper highlights the role of the two large multipurpose dams built on IRS to regulate flows i.e. Mangla and Tarbela commissioned in 1967 and 1976 respectively, in the economic development of Pakistan Careful analysis of four decades of historic data obtained after the construction of these dams from the canal head diversions of IBIS, when compared with the historic figures, reveals large volumes of additional flows are available for irrigation during low flow season. The paper also analyzes these dams role in providing hydroelectricity that sustains the energy sector of Pakistan. Moreover impact on the economic growth of the country due to failure to construct the mega multipurpose dam since 1976 to date is explored