Robust data protection and high efficiency for IoTs streams in the cloud

Remotely generated streaming of the Internet of Things (IoTs) data has become a vital category upon which many applications rely. Smart meters collect readings for household activities such as power and gas consumption every second - the readings are transmitted wirelessly through various channels and public hops to the operation centres. Due to the unusually large streams sizes, the operation centres are using cloud servers where various entities process the data on a real-time basis for billing and power management. It is possible that smart pipe projects (where oil pipes are continuously monitored using sensors) and collected streams are sent to the public cloud for real-time flawed detection. There are many other similar applications that can render the world a convenient place which result in climate change mitigation and transportation improvement to name a few. Despite the obvious advantages of these applications, some unique challenges arise posing some questions regarding a suitable balance between guaranteeing the streams security, such as privacy, authenticity and integrity, while not hindering the direct operations on those streams, while also handling data management issues, such as the volume of protected streams during transmission and storage. These challenges become more complicated when the streams reside on third-party cloud servers. In this thesis, a few novel techniques are introduced to address these problems. We begin by protecting the privacy and authenticity of transmitted readings without disrupting the direct operations. We propose two steganography techniques that rely on different mathematical security models. The results look promising - security: only the approved party who has the required security tokens can retrieve the hidden secret, and distortion effect with the difference between the original and protected readings that are almost at zero. This means the streams can be used in their protected form at intermediate hops or third party servers. We then improved the integrity of the transmitted protected streams which are prone to intentional or unintentional noise - we proposed a secure error detection and correction based stenographic technique. This allows legitimate recipients to (1) detect and recover any noise loss from the hidden sensitive information without privacy disclosure, and (2) remedy the received protected readings by using the corrected version of the secret hidden data. It is evident from the experiments that our technique has robust recovery capabilities (i.e. Root Mean Square (RMS) <0.01%, Bit Error Rate (BER) = 0 and PRD < 1%). To solve the issue of huge transmitted protected streams, two compression algorithms for lossless IoTs readings are introduced to ensure the volume of protected readings at intermediate hops is reduced without revealing the hidden secrets. The first uses Gaussian approximation function to represent IoTs streams in a few parameters regardless of the roughness in the signal. The second reduces the randomness of the IoTs streams into a smaller finite field by splitting to enhance repetition and avoiding the floating operations round errors issues. Under the same conditions, our both techniques were superior to existing models mathematically (i.e. the entropy was halved) and empirically (i.e. achieved ratio was 3.8:1 to 4.5:1). We were driven by the question ‘Can the size of multi-incoming compressed protected streams be re-reduced on the cloud without decompression?’ to overcome the issue of vast quantities of compressed and protected IoTs streams on the cloud. A novel lossless size reduction algorithm was introduced to prove the possibility of reducing the size of already compressed IoTs protected readings. This is successfully achieved by employing similarity measurements to classify the compressed streams into subsets in order to reduce the effect of uncorrelated compressed streams. The values of every subset was treated independently for further reduction. Both mathematical and empirical experiments proved the possibility of enhancing the entropy (i.e. almost reduced by 50%) and the resultant size reduction (i.e. up to 2:1)

DYNAMIC SMART GRID COMMUNICATION PARAMETERS BASED COGNITIVE RADIO NETWORK

The demand for more spectrums in a smart grid communication network is a significant challenge in originally scarce spectrum resources. Cognitive radio (CR) is a powerful technique for solving the spectrum scarcity problem by adapting the transmission parameters according to predefined objectives in an active wireless communication network. This paper presents a cognitive radio decision engine that dynamically selects optimal radio transmission parameters for wireless home area networks (HAN) of smart grid applications via the multi-objective differential evolution (MODE) optimization method. The proposed system helps to drive optimal communication parameters to realize power saving, maximum throughput and minimum bit error rate communication modes. A differential evolution algorithm is used to select the optimal transmission parameters for given communication modes based on a fitness function that combines multiple objectives based on appropriate weights. Simulation results highlight the superiority of the proposed system in terms of accuracy and convergence as compared with other evolution algorithms (genetic optimization, particle swarm optimization, and ant colony optimization) for different communication modes (power saving mode, high throughput mode, emergency communication mode, and balanced mode)

Resilient to shared spectrum noise scheme for protecting cognitive radio smart grid readings - BCH based steganographic approach

Cognitive Radio smart grids have recently attracted attention because of high efficiency and throughput performance. They transmit (1) periodically collected readings (e.g. monitoring) and (2) highly sensitive data (e.g. geometric location). However, robustness, efficiency and security of the transmitted data compose an unaddressed unique challenge due to CR shared spectrum possible noise. This paper proposes the first novel hybrid model that combines advanced steganographic algorithms with error detection and correction techniques (BCH syndrome codes) in the CR smart meter context. This will allow us to (a) detect and recover any loss from the hidden confidential information without privacy disclosure, and (b) remedy the received normal readings by using the corrected version of the secret hidden data. To randomize hiding and minimize the distortion, 3D wavelet is used to decompose normal readings into a set of coefficients. To strengthen the security, a key is utilized to generate a 3D randomly selected order used in the hiding process. To accurately measure the detection and recovery capabilities, random noise levels are applied to the transmitted readings. The recovered sensitive information and stego readings are extensively measured using BER, PRD and RMS. It is obvious from the experiments that our technique has robust recovery capabilities (i.e. BER = 0, PRD < 1% and RMS < 0.01%)

Teknik penyembunyian mesej dalam steganografi teks menggunakan pendekatan warna RGB dan penempatan rawak

Steganography is a technique that protects the confidentiality and integrity of data in a protective medium from suspicion of hidden data. The hiding of a message in a text medium can be performed on various text attributes such as type, style, size, and font color to generate a stego text. This study have identified two main problems that lead to the suspicion towards the stego text which is the obvious change of colors of the generated stego and the static representation of the secret message characters using sequential selection of hiding location. Therefore, the main objective of this study is to propose the use of specific value for each combination of Red, Green, Blue (RGB) color to reduce the generated stego text obvious color changes. This study also recommends a dynamic secret message representation method based on a randomly selected character location. A Homophonic Cipher Table was adapted as a method to generate the dynamic secret message characters. Besides, the Second Quotient Remainder Theorem was proposed to convert the secret message characters into a 3D representation by mapping (x,y,z) values to RGB color. The RGB color cube model values of RGB(0,0,0) to RGB(15,15,15) were used to format a selected cover text characters using the Pseudorandom Number Generator. The performance of stego text produced in this study was evaluated using three main measures namely capacity, imperceptibility, and robustness. The results revealed that the proposed method produces a better performance of secret message hiding by 41.31% increase in capacity and the Jaro Winkler's scale imperceptibility score of 1. The performance of stego text is proven to be robust as there is no difference compared to the cover text before and after the compression process. In conclusion, the proposed method has successfully reduced the generated stego text obviousness in the change of colors that lead to suspicion of existence of hidden message. Beside, this method also capable of producing dynamic secret messages using a single cover text