arXiv subjects

Jawad Ahmad

Publications and source records attributed to Jawad Ahmad.

34 records · Page 2Linked to original sources

arXiv2023

Distributed Twins in Edge Computing: Blockchain and IOTA

Blockchain (BC) and Information for Operational and Tactical Analysis (IOTA) are distributed ledgers that record a huge number of transactions in multiple places at the same time using decentralized databases. Both BC and IOTA facilitate Internet-of-Things (IoT) by overcoming the issues related to traditional centralized systems, such as privacy, security, resources cost, performance, and transparency. Still, IoT faces the potential challenges of real-time processing, resource management, and storage services. Edge computing (EC) has been introduced to tackle the underlying challenges of IoT by providing real-time processing, resource management, and storage services nearer to IoT devices on the network's edge. To make EC more efficient and effective, solutions using BC and IOTA have been devoted to this area. However, BC and IOTA came with their pitfalls. This survey outlines the pitfalls of BC and IOTA in EC and provides research directions to be investigated further.

cs.DC↗

arXiv2023

Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio

Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates the use of Wi-Fi channel state information (CSI) as a novel method of ambient sensing that can be employed as a contactless means of recognizing human activity in indoor environments. These methods avoid additional costly hardware required for vision-based systems, which are privacy-intrusive, by (re)using Wi-Fi CSI for various safety and security applications. During an experiment utilizing universal software-defined radio (USRP) to collect CSI samples, it was observed that a subject engaged in six distinct activities, which included no activity, standing, sitting, and leaning forward, across different areas of the room. Additionally, more CSI samples were collected when the subject walked in two different directions. This study presents a Wi-Fi CSI-based HAR system that assesses and contrasts deep learning approaches, namely convolutional neural network (CNN), long short-term memory (LSTM), and hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental results indicate that LSTM surpasses current models and achieves an average accuracy of 95.3% in multi-activity classification when compared to CNN and hybrid techniques. In the future, research needs to study the significance of resilience in diverse and dynamic environments to identify the activity of multiple users.

cs.LG↗

arXiv2022

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N $\times$ M $\times$ 3 to P $\times$ Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N $\times$ M $\times$ 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. encryption.

cs.CR↗

arXiv2022

A Novel Chaos-based Light-weight Image Encryption Scheme for Multi-modal Hearing Aids

Multimodal hearing aids (HAs) aim to deliver more intelligible audio in noisy environments by contextually sensing and processing data in the form of not only audio but also visual information (e.g. lip reading). Machine learning techniques can play a pivotal role for the contextually processing of multimodal data. However, since the computational power of HA devices is low, therefore this data must be processed either on the edge or cloud which, in turn, poses privacy concerns for sensitive user data. Existing literature proposes several techniques for data encryption but their computational complexity is a major bottleneck to meet strict latency requirements for development of future multi-modal hearing aids. To overcome this problem, this paper proposes a novel real-time audio/visual data encryption scheme based on chaos-based encryption using the Tangent-Delay Ellipse Reflecting Cavity-Map System (TD-ERCS) map and Non-linear Chaotic (NCA) Algorithm. The results achieved against different security parameters, including Correlation Coefficient, Unified Averaged Changed Intensity (UACI), Key Sensitivity Analysis, Number of Changing Pixel Rate (NPCR), Mean-Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Entropy test, and Chi-test, indicate that the newly proposed scheme is more lightweight due to its lower execution time as compared to existing schemes and more secure due to increased key-space against modern brute-force attacks.

cs.CR↗

arXiv2022

Ransomware: Analysing the Impact on Windows Active Directory Domain Services

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services

cs.CR↗

arXiv2021

Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers

Threat information sharing is considered as one of the proactive defensive approaches for enhancing the overall security of trusted partners. Trusted partner organizations can provide access to past and current cybersecurity threats for reducing the risk of a potential cyberattack - the requirements for threat information sharing range from simplistic sharing of documents to threat intelligence sharing. Therefore, the storage and sharing of highly sensitive threat information raises considerable concerns regarding constructing a secure, trusted threat information exchange infrastructure. Establishing a trusted ecosystem for threat sharing will promote the validity, security, anonymity, scalability, latency efficiency, and traceability of the stored information that protects it from unauthorized disclosure. This paper proposes a system that ensures the security principles mentioned above by utilizing a distributed ledger technology that provides secure decentralized operations through smart contracts and provides a privacy-preserving ecosystem for threat information storage and sharing regarding the MITRE ATT\&CK framework.

cs.CR↗

arXiv2021

Evolution of Target Localization in Wireless Sensor Network (WSN): A Review

Wireless Sensor Network holds a pivotal position and gained a lot of attention from researchers in recent years. Sensor nodes have been used in vast applications such as environment monitoring, security purpose applications, and target tracking. This latter comprises of detection and monitoring of the target movement. In this paper, we explore in detail well-known target tracking techniques. The existing techniques are evaluated using metrics such as network topology, target recovery, energy efficiency, and security. We also discuss some of the challenges that affect the performance of tracking schemes. Furthermore, a thorough analysis is performed on existing techniques and future directions are explored.

cs.NI↗

arXiv2021

GuideMe: A Mobile Application based on Global Positioning System and Object Recognition Towards a Smart Tourist Guide

Finding information about tourist places to visit is a challenging problem that people face while visiting different countries. This problem is accentuated when people are coming from different countries, speak different languages, and are from all segments of society. In this context, visitors and pilgrims face important problems to find the appropriate doaas when visiting holy places. In this paper, we propose a mobile application that helps the user find the appropriate doaas for a given holy place in an easy and intuitive manner. Three different options are developed to achieve this goal: 1) manual search, 2) GPS location to identify the holy places and therefore their corresponding doaas, and 3) deep learning (DL) based method to determine the holy place by analyzing an image taken by the visitor. Experiments show good performance of the proposed mobile application in providing the appropriate doaas for visited holy places.

cs.CV↗

arXiv2021

Intrusion Detection using Machine Learning Techniques: An Experimental Comparison

Due to an exponential increase in the number of cyber-attacks, the need for improved Intrusion Detection Systems (IDS) is apparent than ever. In this regard, Machine Learning (ML) techniques are playing a pivotal role in the early classification of the attacks in case of intrusion detection within the system. However, due to a large number of algorithms available, the selection of the right method is a challenging task. To resolve this issue, this paper analyses some of the current state-of-the-art intrusion detection methods and discusses their pros and cons. Further, a review of different ML methods is carried out with four methods showing to be the most suitable one for classifying attacks. Several algorithms are selected and investigated to evaluate the performance of IDS. These IDS classifies binary and multiclass attacks in terms of detecting whether or not the traffic has been considered as benign or an attack. The experimental results demonstrate that binary classification has greater consistency in their accuracy results which ranged from 0.9938 to 0.9977, while multiclass ranges from 0.9294 to 0.9983. However, it has been also observed that multiclass provides the best results with the algorithm k-Nearest neighbor giving an accuracy score of 0.9983 while the binary classification highest score is 0.9977 from Random Forest. The experimental results demonstrate that multiclass classification produces better performance in terms of intrusion detection by specifically differentiating between the attacks and allowing a more targeted response to an attack.

cs.CR↗

arXiv2021

Microservices in IoT Security: Current Solutions, Research Challenges, and Future Directions

In recent years, the Internet of Things (IoT) technology has led to the emergence of multiple smart applications in different vital sectors including healthcare, education, agriculture, energy management, etc. IoT aims to interconnect several intelligent devices over the Internet such as sensors, monitoring systems, and smart appliances to control, store, exchange, and analyze collected data. The main issue in IoT environments is that they can present potential vulnerabilities to be illegally accessed by malicious users, which threatens the safety and privacy of gathered data. To face this problem, several recent works have been conducted using microservices-based architecture to minimize the security threats and attacks related to IoT data. By employing microservices, these works offer extensible, reusable, and reconfigurable security features. In this paper, we aim to provide a survey about microservices-based approaches for securing IoT applications. This survey will help practitioners understand ongoing challenges and explore new and promising research opportunities in the IoT security field. To the best of our knowledge, this paper constitutes the first survey that investigates the use of microservices technology for securing IoT applications.

cs.CR↗

arXiv2021

Voting Classifier-based Intrusion Detection for IoT Networks

Internet of Things (IoT) is transforming human lives by paving the way for the management of physical devices on the edge. These interconnected IoT objects share data for remote accessibility and can be vulnerable to open attacks and illegal access. Intrusion detection methods are commonly used for the detection of such kinds of attacks but with these methods, the performance/accuracy is not optimal. This work introduces a novel intrusion detection approach based on an ensemble-based voting classifier that combines multiple traditional classifiers as a base learner and gives the vote to the predictions of the traditional classifier in order to get the final prediction. To test the effectiveness of the proposed approach, experiments are performed on a set of seven different IoT devices and tested for binary attack classification and multi-class attack classification. The results illustrate prominent accuracies on Global Positioning System (GPS) sensors and weather sensors to 96% and 97% and for other machine learning algorithms to 85% and 87%, respectively. Furthermore, comparison with other traditional machine learning methods validates the superiority of the proposed algorithm.

cs.CR↗

arXiv2021

A Novel CNN-LSTM-based Approach to Predict Urban Expansion

Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveilling crops, coastal changes, flood risk assessment, and urban sprawl. This paper addresses the challenge of using time-series satellite images to predict urban expansion. Building upon previous work, we propose a novel two-step approach based on semantic image segmentation in order to predict urban expansion. The first step aims to extract information about urban regions at different time scales and prepare them for use in the training step. The second step combines Convolutional Neural Networks (CNN) with Long Short Term Memory (LSTM) methods in order to learn temporal features and thus predict urban expansion. In this paper, experimental results are conducted using several multi-date satellite images representing the three largest cities in Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. We empirically evaluated our proposed technique, and examined its results by comparing them with state-of-the-art approaches. Following this evaluation, we determined that our results reveal improved performance for the new-coupled CNN-LSTM approach, particularly in terms of assessments based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy.

cs.CV↗

arXiv2021

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

cs.CR↗

arXiv2020

Deep Learning Techniques for Future Intelligent Cross-Media Retrieval

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods.

cs.IR↗

arXiv2020

An authentication protocol based on chaos and zero knowledge proof

Port Knocking is a method for authenticating clients through a closed stance firewall, and authorising their requested actions, enabling severs to offer services to authenticated clients, without opening ports on the firewall. Advances in port knocking have resulted in an increase in complexity in design, preventing port knocking solutions from realising their potential. This paper proposes a novel port knocking solution, named Crucible, which is a secure method of authentication, with high usability and features of stealth, allowing servers and services to remain hidden and protected. Crucible is a stateless solution, only requiring the client memorise a command, the server's IP and a chosen password. The solution is forwarded as a method for protecting servers against attacks ranging from port scans, to zero-day exploitation. To act as a random oracle for both client and server, cryptographic hashes were generated through chaotic systems.

cs.CR↗

arXiv2018

Real-Time Lightweight Chaotic Encryption for 5G IoT Enabled Lip-Reading Driven Secure Hearing-Aid

Existing audio-only hearing-aids are known to perform poorly in noisy situations where overwhelming noise is present. Next-generation audio-visual (lip-reading driven) hearing-aids stand as a major enabler to realise more intelligible audio. However, high data rate, low latency, low computational complexity, and privacy are some of the major bottlenecks to the successful deployment of such advanced hearing aids. To address these challenges, we envision an integration of 5G Cloud-Radio Access Network, Internet of Things (IoT), and strong privacy algorithms to fully benefit from the possibilities these technologies have to offer. The envisioned 5G IoT enabled secure audio-visual (AV) hearing-aid transmits the encrypted compressed AV information and receives encrypted enhanced reconstructed speech in real-time which fully addresses cybersecurity attacks such as location privacy and eavesdropping. For security implementation, a real-time lightweight AV encryption is utilized. For speech enhancement, the received AV information in the cloud is used to filter noisy audio using both deep learning and analytical acoustic modelling (filtering based approach). To offload the computational complexity and real-time optimization issues, the framework runs deep learning and big data optimization processes in the background on the cloud. Specifically, in this work, three key contributions are reported: (1) 5G IoT enabled secure audio-visual hearing-aid framework that aims to achieve a round-trip latency up to 5ms with 100 Mbps datarate (2) Real-time lightweight audio-visual encryption (3) Lip-reading driven deep learning approach for speech enhancement in the cloud. The critical analysis in terms of both speech enhancement and AV encryption demonstrate the potential of the envisioned technology in acquiring high-quality speech reconstruction and secure mobile AV hearing aid communication.

cs.CR↗