Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Mitigating Denial of Service Attacks in Fog-Based Wireless Sensor Networks Using Machine Learning Techniques

Wireless sensor networks are considered to be among the most significant and innovative technologies in the 21st century due to their wide range of industrial applications. Sensor nodes in these networks are susceptible to a variety of assaults due to their special qualities and method of deployment. In WSNs, denial of service attacks are common attacks in sensor networks. It is difficult to design a detection and prevention system that would effectively reduce the impact of these attacks on WSNs. In order to identify assaults on WSNs, this study suggests using two machine learning models: decision trees and XGBoost. The WSNs dataset was the subject of extensive tests to identify denial of service attacks. The experimental findings demonstrate that the XGBoost model, when applied to the entire dataset, has a higher true positive rate (98.3%) than the Decision tree approach (97.3%) and a lower false positive rate (1.7%) than the Decision tree technique (2.7%). Like this, with selected dataset assaults, the XGBoost approach has a higher true positive rate (99.01%) than the Decision tree technique (97.50%) and a lower false positive rate (0.99%) than the Decision tree technique (2.50%)

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection

Efficiency and Accuracy Enhancement of Intrusion Detection System Using Feature Selection and Cross-layer Mechanism

The dramatic increase in the number of connected devices and the significant growth of the network traffic data have led to many security vulnerabilities and cyber-attacks. Hence, developing new methods to secure the network infrastructure and protect data from malicious and unauthorized access becomes a vital aspect of communication network design. Intrusion Detection Systems (IDSs), as common widely used security techniques, are critical to detect network attacks and unauthorized network access and thus minimize further cyber-attack damages. However, there are a number of weaknesses that need to be addressed to make reliable IDS for real-world applications. One of the fundamental challenges is the large number of redundant and non-relevant data. Feature selection emerges as a necessary step in efficient IDS design to overcome high dimensionality problem and enhance the performance of IDS through the reduction of its complexity and the acceleration of the detection process. Moreover, detection algorithm has significant impact on the performance of IDS. Machine learning techniques are widely used in such systems which is studied in details in this dissertation. One of the most destructive activities in wireless networks such as MANET is packet dropping. The existence of the intrusive attackers in the network is not the only cause of packet loss. In fact, packet drop can occur because of faulty network. Hence, in order detect the packet dropping caused by a malicious activity of an attacker, information from various layers of the protocol is needed to detect malicious packet loss effectively. To this end, a novel cross-layer design for malicious packet loss detection in MANET is proposed using features from physical layer, network layer and MAC layer to make a better detection decision. Trust-based mechanism is adopted in this design and a packet loss free routing algorithm is presented accordingly

Building a reliable and secure management framework for software-defined networks

Title from PDF of title page viewed December 15, 2021Dissertation advisor: Sejun SongVitaIncludes bibliographical references (pages 101-109)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2021The Software-Defined Networking (SDN) technologies promise to enhance the performance and cost of managing both wired and wireless network infrastructures, functions, controls, and services (i.e., Internet of Things). However, centralized management in softwarization architecture poses new security, reliability, and scalability challenges. Significantly, the current OpenFlow Discovery Protocol (OFDP) in SDN induces substantial issues due to its gossipy, centralized, periodic, and tardy protocol. Furthermore, the problems are aggravated in the wireless and mobile SDN due to the dynamic topology churns and the lack of link-layer discovery methods. In this work, we tackle both security and reliability management issues in SDN. Specifically, we design and build a novel multitemporal cross-stratum discovery proto- col framework, which efficiently orchestrates different reliability monitoring mechanisms over SDN networks and synchronizes the control messages among various applications. It facilitates multiple discovery frequency timers for each target over different stratum instead of using a uniform discovery timer for the entire network. It supports many common reliability monitoring factors for registered applications by analyzing offline and online network architecture information such as network topologies, traffic flows, virtualization architectures, and protocols. The framework consists of traffic-aware discovery (TaDPole), and centrality-aware protocol (CAMLE) facilities. We implemented the framework on Ryu controller. Extensive Mininet experimental results validate that the framework significantly improves discovery message efficiency and makes the control traffic less bursty than OFDP with a uniform timer. It also reduces the network status discovery delay without increasing the control overhead. We then evaluated the security issues in SDN and proposed an SDN-based Wormhole Analysis using the Neighbor Similarity (SWANS) approach as a novel wormhole countermeasure in a Software-defined MANET. As SWANS analyses the similarity of neighbor counts at a centralized SDN controller, it apprehends wormholes not only without requiring any particular location information but also without causing significant communication and coordination overhead. SWANS also countermeasures various false-positive and false-negative scenarios generated by the Link Layer Discovery Protocol (LLDP) vulnerability. We performed extensive studies via both analysis and simulations. Our simulation results show that SWANS can detect wormhole attacks efficiently with low false-positive and false-negative rates.Introduction -- Background -- Literature review -- Traffic-aware discovery protocol for software-defined wireless and mobile networks -- Centrality-aware multitemporal discovery protocol for software-defined networks -- SDN-based wormhole analysis using the neighbor similarity for a Mobile Ad hoc Network (MANET) -- Conclusions and future wor

Internet of Things Adoption for Saudi Healthcare Services

Background: Recent studies in information systems have predicted that applications of the Internet of Things (IoT) innovations will revolutionise various sectors including healthcare. Besides the issues and opportunities of IoT based innovations, existing studies have shown limitations to advance the adoption of IoT-understanding and relevant interventions to benefit researchers and healthcare practitioners. Method: In this context, a systematic literature review study was conducted to re-position a qualitative, phenomenological investigation that could offer useful insights into the factors affecting IoT-adoption in a developing country’s healthcare service. In addition to it, five participants who worked in hospitals and clinics in Jazan, Saudi Arabia, took part in the semi-structured interviews developed based on the diffusion of innovation theory. Results: The study explored the relevant literature and evaluated how the outcome is used to identify the key delivers of IoT in healthcare. Conclusions: According to the findings, the capacity of the Saudi healthcare sector to accept and implement a new IT with IoT technologies is increasing and its integrations remains a debated issue

GAN-CAN: A Novel Attack to Behavior-Based Driver Authentication Systems

openFor many years, car keys have been the sole mean of authentication in vehicles. Whether the access control process is physical or wireless, entrusting the ownership of a vehicle to a single token is prone to stealing attempts. Modern vehicles equipped with the Controller Area Network (CAN) bus technology collects a wealth of sensor data in real-time, covering aspects such as the vehicle, environment, and driver. This data can be processed and analyzed to gain valuable insights and solutions for human behavior analysis. For this reason, many researchers started developing behavior-based authentication systems. Many Machine Learning (ML) and Deep Learning models (DL) have been explored for behavior-based driver authentication, but the emphasis on security has not been a primary focus in the design of these systems. By collecting data in a moving vehicle, DL models can recognize patterns in the data and identify drivers based on their driving behavior. This can be used as an anti-theft system, as a thief would exhibit a different driving style compared to the vehicle owner. However, the assumption that an attacker cannot replicate the legitimate driver behavior falls under certain conditions. In this thesis, we propose GAN-CAN, the first attack capable of fooling state-of-the-art behavior-based driver authentication systems in a vehicle. Based on the adversary's knowledge, we propose different GAN-CAN implementations. Our attack leverages the lack of security in the CAN bus to inject suitably designed time-series data to mimic the legitimate driver. Our malicious time series data is generated through the integration of a modified reinforcement learning technique with Generative Adversarial Networks (GANs) with adapted training process. Furthermore we conduct a thorough investigation into the safety implications of the injected values throughout the attack. This meticulous study is conducted to guarantee that the introduced values do not in any way undermine the safety of the vehicle and the individuals inside it. Also, we formalize a real-world implementation of a driver authentication system considering possible vulnerabilities and exploits. We tested GAN-CAN in an improved version of the most efficient driver behavior-based authentication model in the literature. We prove that our attack can fool it with an attack success rate of up to 99%. We show how an attacker, without prior knowledge of the authentication system, can steal a car by deploying GAN-CAN in an off-the-shelf system in under 22 minutes. Moreover, by considering the safety importance of the injected values, we demonstrate that GAN-CAN can successfully deceive the authentication system without compromising the overall safety of the vehicle. This highlights the urgent need to address the security vulnerabilities present in behavior-based driver authentication systems. In the end, we suggest some possible countermeasures to the GAN-CAN attack.For many years, car keys have been the sole mean of authentication in vehicles. Whether the access control process is physical or wireless, entrusting the ownership of a vehicle to a single token is prone to stealing attempts. Modern vehicles equipped with the Controller Area Network (CAN) bus technology collects a wealth of sensor data in real-time, covering aspects such as the vehicle, environment, and driver. This data can be processed and analyzed to gain valuable insights and solutions for human behavior analysis. For this reason, many researchers started developing behavior-based authentication systems. Many Machine Learning (ML) and Deep Learning models (DL) have been explored for behavior-based driver authentication, but the emphasis on security has not been a primary focus in the design of these systems. By collecting data in a moving vehicle, DL models can recognize patterns in the data and identify drivers based on their driving behavior. This can be used as an anti-theft system, as a thief would exhibit a different driving style compared to the vehicle owner. However, the assumption that an attacker cannot replicate the legitimate driver behavior falls under certain conditions. In this thesis, we propose GAN-CAN, the first attack capable of fooling state-of-the-art behavior-based driver authentication systems in a vehicle. Based on the adversary's knowledge, we propose different GAN-CAN implementations. Our attack leverages the lack of security in the CAN bus to inject suitably designed time-series data to mimic the legitimate driver. Our malicious time series data is generated through the integration of a modified reinforcement learning technique with Generative Adversarial Networks (GANs) with adapted training process. Furthermore we conduct a thorough investigation into the safety implications of the injected values throughout the attack. This meticulous study is conducted to guarantee that the introduced values do not in any way undermine the safety of the vehicle and the individuals inside it. Also, we formalize a real-world implementation of a driver authentication system considering possible vulnerabilities and exploits. We tested GAN-CAN in an improved version of the most efficient driver behavior-based authentication model in the literature. We prove that our attack can fool it with an attack success rate of up to 99%. We show how an attacker, without prior knowledge of the authentication system, can steal a car by deploying GAN-CAN in an off-the-shelf system in under 22 minutes. Moreover, by considering the safety importance of the injected values, we demonstrate that GAN-CAN can successfully deceive the authentication system without compromising the overall safety of the vehicle. This highlights the urgent need to address the security vulnerabilities present in behavior-based driver authentication systems. In the end, we suggest some possible countermeasures to the GAN-CAN attack

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)