Showing items 1 - 8 of 8

A blockchain-based distributed peer-to-peer ecosystem for energy trading

- Islam, Mohammad

  • Authors: Islam, Mohammad
  • Date: 2024
  • Type: Text , Thesis , PhD
  • Full Text:
  • Description: Blockchain technologies are revolutionising peer-to-peer (P2P) distributed energy trading. These technologies can leverage microgrid decentralisation and immutable data storage to provide efficient and secure trading to benefit prosumers. A double auction mechanism is best suited for energy trading in a P2P microgrid. This mechanism requires a solvent cryptocurrency reserve for payment settlement. Double auctions give rise to unspent auction reservations (UARs). Existing mechanisms can settle further auctions with UARs but need improvements to do this without affecting trading efficiency. Keeping a cryptocurrency reserve solvent also requires adaptations to existing mechanisms. Auction settlements within a microgrid leave UARs, meaning that other microgrids must join for further auction settlements, and this leads to security vulnerabilities. It is important to develop an ecosystem that can enhance trading efficiency, ensure the solvency of the cryptocurrency reserve and provide security for multi-microgrid energy trading. In distributed energy trading, an auctioneer passes UARs to the next auctioneer as specified by the passing mechanism. Traditional energy trading systems use simple passing mechanisms and basic pricing mechanisms, but this adversely affects trading efficiency and buyers’ economic surplus. Traditional P2P energy trading systems use passing mechanisms that only partially consider the auction capacity of the next auctioneer. We propose a blockchain-based energy trading mechanism using a smart passing mechanism (SPM) that uses an unspent reservation profile (URP) to represent the auctioneers’ capability to pass UARs within a P2P microgrid. We further propose an intelligent passing mechanism (iPass) that incorporates price information into URPs to enhance trading efficiency. We applied three metrics to measure trading efficiency: convergence time, auction settlements and the economic surplus of buyers and sellers. We simulated our mechanisms in Hyperledger Fabric, a permissioned blockchain framework that managed the data storage and smart contracts. Experiments showed that our SPM reduces the convergence time, increases auction settlements and increases the economic surplus of buyers compared with existing mechanisms. Experiments showed that iPass is even more efficient than other passing mechanisms, including SPM, further reducing the convergence time, increasing auction settlements and increasing the economic surplus of buyers and sellers. Settling payments in blockchain-based P2P energy trading requires maintaining the solvency of the cryptocurrency reserve to ensure a stable medium of exchange and reduce price volatility. Stablecoins, as a form of cryptocurrency—the most suitable medium of exchange—are gaining attention from central banks. A consortium of central banks has recommended compliance with capital and liquidity standards for high-quality liquid assets (HQLA). Stablecoins, as a form of HQLA, require the adaptation of these standards for P2P energy trading. We propose a mechanism (NF90) to control the inflow of stablecoins in response to the liquidity coverage ratio (LCR) for reserve resilience and to maintain solvency. The Basel III Accord recommends 100% LCR. We measured the effectiveness of NF90 using LCR as a metric simulating the mechanism in Hyperledger Fabric to manage deceni tralisation, data storage and smart contracts. NF90 was the most effective inflow control mechanism. The use of iPass for a P2P microgrid leaves UARs. Traditional trading mechanisms settle further auctions with UARs within a microgrid, which affects the economic surplus of prosumers. Auction settlements with neighbouring microgrids increase prosumers’ economic surplus, but the usual pricing of double auction mechanisms reduces their economic surplus. Other pricing mechanisms are needed in a multi-microgrid paradigm. Settling auctions for microgrids requires common computational resources that are close to microgrids. Edge computing technologies suit this need, and blockchain technology leverages immutable data storage in cloud servers. However, communication with a cloud server through proprietary edge computing devices exposes the ecosystem to security vulnerabilities. It is important to control access by prosumers and forensic users. Immutable data storage and the retrieval of data are essential. Two challenges in information security are incorporating reliable access control for users and devices while granting access to confidential data for relevant users and maintaining data persistence. This research used a blockchain structure for data persistence. We propose a framework of novel protocols to authenticate users (prosumers and auctioneers) by the edge server and of the edge server by the cloud server. Our framework also provides access to forensic users using immutable blockchain-based data storage with endpoint authentication and a role-based user access control system. We simulated the framework using the Automated Validation of Internet Security Protocols and Applications and showed that it can deal effectively with several security issues.
  • Description: Doctor of Philosophy

A robust forgery detection method for copy-move and splicing attacks in images

- Islam, Mohammad, Karmakar, Gour, Kamruzzaman, Joarder, Murshed, Manzur

  • Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Electronics Vol. 9, no. 9 (2020), p. 1-22
  • Full Text:
  • Reviewed:
  • Description: Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy-move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors' physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.
  • Description: This research was funded by Research Priority Area (RPA) scholarship of Federation University Australia.

Comprehensive analysis of feature extraction techniques and runtime performance evaluation for phishing detection

- Nath, Subrata, Islam, Mohammad, Chowdhury, Abdullahi, Rashid, Mohammad, Islam, Maheen, Jabid, Taskeed, Naha, Ranesh

Detecting splicing and copy-move attacks in color images

- Islam, Mohammad, Karmakar, Gour, Kamruzzaman, Joarder, Murshed, Manzur, Kahandawa, Gayan, Parvin, Nahida

  • Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur , Kahandawa, Gayan , Parvin, Nahida
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-7
  • Full Text:
  • Reviewed:
  • Description: Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
  • Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018

Leveraging oversampling techniques in machine learning models for multi-class malware detection in smart home applications

- Chowdhury, Abdullahi, Islam, Mohammad, Kaisar, Shahriar, Khoda, Mahbub, Naha, Ranesh, Khoshkholghi, Mohammad, Aiash, Mahdi

Measuring trustworthiness of image data in the internet of things environment

- Islam, Mohammad

  • Authors: Islam, Mohammad
  • Date: 2021
  • Type: Text , Thesis , PhD
  • Full Text:
  • Description: Internet of Things (IoT) image sensors generate huge volumes of digital images every day. However, easy availability and usability of photo editing tools, the vulnerability in communication channels and malicious software have made forgery attacks on image sensor data effortless and thus expose IoT systems to cyberattacks. In IoT applications such as smart cities and surveillance systems, the smooth operation depends on sensors’ sharing data with other sensors of identical or different types. Therefore, a sensor must be able to rely on the data it receives from other sensors; in other words, data must be trustworthy. Sensors deployed in IoT applications are usually limited to low processing and battery power, which prohibits the use of complex cryptography and security mechanism and the adoption of universal security standards by IoT device manufacturers. Hence, estimating the trust of the image sensor data is a defensive solution as these data are used for critical decision-making processes. To our knowledge, only one published work has estimated the trustworthiness of digital images applied to forensic applications. However, that study’s method depends on machine learning prediction scores returned by existing forensic models, which limits its usage where underlying forensics models require different approaches (e.g., machine learning predictions, statistical methods, digital signature, perceptual image hash). Multi-type sensor data correlation and context awareness can improve the trust measurement, which is absent in that study’s model. To address these issues, novel techniques are introduced to accurately estimate the trustworthiness of IoT image sensor data with the aid of complementary non-imagery (numeric) data-generating sensors monitoring the same environment. The trust estimation models run in edge devices, relieving sensors from computationally intensive tasks. First, to detect local image forgery (splicing and copy-move attacks), an innovative image forgery detection method is proposed based on Discrete Cosine Transformation (DCT), Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. Using Support Vector Machine (SVM), the proposed method is extensively tested on four well-known publicly available greyscale and colour image forgery datasets and on an IoT-based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples. Second, a robust trust estimation framework for IoT image data is proposed, leveraging numeric data-generating sensors deployed in the same area of interest (AoI) in an indoor environment. As low-cost sensors allow many IoT applications to use multiple types of sensors to observe the same AoI, the complementary numeric data of one sensor can be exploited to measure the trust value of another image sensor’s data. A theoretical model is developed using Shannon’s entropy to derive the uncertainty associated with an observed event and Dempster-Shafer theory (DST) for decision fusion. The proposed model’s efficacy in estimating the trust score of image sensor data is analysed by observing a fire event using IoT image and temperature sensor data in an indoor residential setup under different scenarios. The proposed model produces highly accurate trust scores in all scenarios with authentic and forged image data. Finally, as the outdoor environment varies dynamically due to different natural factors (e.g., lighting condition variations in day and night, presence of different objects, smoke, fog, rain, shadow in the scene), a novel trust framework is proposed that is suitable for the outdoor environments with these contextual variations. A transfer learning approach is adopted to derive the decision about an observation from image sensor data, while also a statistical approach is used to derive the decision about the same observation from numeric data generated from other sensors deployed in the same AoI. These decisions are then fused using CertainLogic and compared with DST-based fusion. A testbed was set up using Raspberry Pi microprocessor, image sensor, temperature sensor, edge device, LoRa nodes, LoRaWAN gateway and servers to evaluate the proposed techniques. The results show that CertainLogic is more suitable for measuring the trustworthiness of image sensor data in an outdoor environment.
  • Description: Doctor of Philosophy

Measuring trustworthiness of IoT image sensor data using other sensors' complementary multimodal data

- Islam, Mohammad, Karmakar, Gour, Kamruzzaman, Joarder, Murshed, Manzur

  • Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 p. 775-780
  • Full Text: false
  • Reviewed:
  • Description: Trust of image sensor data is becoming increasingly important as the Internet of Things (IoT) applications grow from home appliances to surveillance. Up to our knowledge, there exists only one work in literature that estimates trustworthiness of digital images applied to forensic applications, based on a machine learning technique. The efficacy of this technique is heavily dependent on availability of an appropriate training set and adequate variation of IoT sensor data with noise, interference and environmental condition, but availability of such data cannot be assured always. Therefore, to overcome this limitation, a robust method capable of estimating trustworthy measure with high accuracy is needed. Lowering cost of sensors allow many IoT applications to use multiple types of sensors to observe the same event. In such cases, complementary multimodal data of one sensor can be exploited to measure trust level of another sensor data. In this paper, for the first time, we introduce a completely new approach to estimate the trustworthiness of an image sensor data using another sensor's numerical data. We develop a theoretical model using the Dempster-Shafer theory (DST) framework. The efficacy of the proposed model in estimating trust level of an image sensor data is analyzed by observing a fire event using IoT image and temperature sensor data in a residential setup under different scenarios. The proposed model produces highly accurate trust level in all scenarios with authentic and forged image data. © 2019 IEEE.
  • Description: E1

Passive detection of splicing and copy-move attacks in image forgery

- Islam, Mohammad, Kamruzzaman, Joarder, Karmakar, Gour, Murshed, Manzur, Kahandawa, Gayan

  • Authors: Islam, Mohammad , Kamruzzaman, Joarder , Karmakar, Gour , Murshed, Manzur , Kahandawa, Gayan
  • Date: 2018
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 25th International Conference on Neural Information Processing, ICONIP 2018; Siem Reap, Cambodia; 13th-16th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11304 LNCS, p. 555-567
  • Full Text:
  • Reviewed:
  • Description: Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.
  • 1