ASPG

The impact of AI-based cyber security on the banking and financial sectors

Haya saleh alrafi , Shailendra Mishra

BD and AI are now transforming the banking and finance industry at a very fast pace, which is leading to change in the banking and finance institutions. This change is making them better, customer-oriented and financially rewarding organizations. Big data and AI have been useful in the banking and financial institutions to assess and manage the risks. Through the analysis of big amounts of unstructured data in real time, AI algorithms are capable of identifying risks. This makes it easy to put preventive measures in place to avert the risks. In addition, big data and AI have come a long way in solving the problem of fraud in banking and finance. This paper showed how big data and AI improve risk management, Cyber threat, and fraud in banking and finance by using data analysis and data pattern identification in real-time. That is why our work emphasizes the importance of implementing secure privacy and explaining the AI algorithm to eliminate ethical and Cyber security issues. Using analytical approaches, AI can identify the transactions with the help of comparison with the previous data and the behavioral characteristics related to the fraud. This approach to fraud prevention has been effective in reducing losses while at the same time improving the customer’s confidence in the company. On the other hand, there are disadvantages of big data and AI such as privacy, security, and ethical issues. Measures that can be used to safeguard customer information have to be employed in order to effectively safeguard the consumer data. Furthermore, transparency and accountability of the AI algorithms are crucial in order to avoid unfair decisions.

Vol. 14 Issue. 1 PP. 08-19, (2024)

Anomaly-Based Intrusion Detection Systems Using Machine Learning

Alsamir Alqahtani , Hanan AlShaher

With the increased use of the Internet, unauthorized access has increased, allowing malicious users to hack networks and carry out malicious activities. One of the essential modern approaches in today's cybersecurity efforts is the limitation of access by suspect users. In this study, the approach toward real-time intrusion detection was to consider behavioral patterns of past users on the network. We classified the users as two categories: intervention and non-intervention, and employed the machine learning techniques Artificial Neural Networks [ANN], Support Vector Machines [SVM], and Decision Trees [DT]. The Decision Trees model was chosen as it had a mature capability concerning complex pattern recognition and an enhancement capability of the intrusion detection systems. The efficiency of these algorithms is examined via the key performance metrics: confusion matrix, F1-score, and Area Under the Curve [AUC]. Decision Tree, which came up as the best model for both the training and testing phases, produced an outstanding F1-score of 99.96% and AUC score of 99.93% in the testing phase. SVM and ANN gave good results; the F1 scores of SVM and ANN in the testing phase were 92.76% and 93.33%, while the AUC was 90.57% and 94.78%, respectively. This research will enlighten us on the influence of machine learning on the scope of intrusion detection, fostering more development efforts toward more responsive and dynamic intrusion detection systems. The comparative evaluation of these models will help in providing vital information for the further enhancement of cybersecurity strategies, ensuring better defenses against these ever-evolving cyber threats.

Vol. 14 Issue. 1 PP. 20-33, (2024)

Securing the Digital Commerce Spectrum and Cyber Security Strategies for Web, E-commerce, M-commerce, and E-mail Security

Rohit Pachlor , R. Mohanraj , K. Sharada , Savya Sachi , K. Neelima , Punyala Ramadevi

Secure protection of sensitive data and financial transactions is of the utmost importance in the dynamic world of online trade. In this study, we present a full-stack security architecture that uses five separate algorithms: ECF, Transaction Anomaly Detection, Adaptive Threat Intelligence, Behavioral Biometric Authentication, and Dynamic Encryption Protocol. By creating encryption keys on the fly while the user logs in, the DEP method lays a solid groundwork for safe data transfer. Behavioral biometric authentication (BBA) uses DEP output to verify users based on their distinct behavior, which is an extra layer of security. By combining both current and past threat information, the ATI algorithm is able to constantly adjust security protocols, providing a preventative shield against new dangers. TAD is an expert at detecting anomalies in online purchases, which helps keep financial transactions honest. When ECF and DEP work together, they filter email content, making communication more secure. Flowcharts help to illustrate the interactions between various algorithms, which helps to understand their operations in detail. Every algorithm's importance is brought to light by an ablation study, which shows how each one contributes and how they all work together to affect the overall security posture. The suggested security framework outperforms the state-of-the-art in terms of efficacy, adaptability, and usability, according to performance evaluations conducted using a number of metrics. These findings can help decision-makers build a strong security plan that is specific to the challenges of online shopping. To conclude, the suggested framework is an integrated and complementary strategy that will strengthen online trade in the face of several cyber dangers while simultaneously protecting the confidentiality, authenticity, and availability of all associated communications and transactions.

Vol. 14 Issue. 1 PP. 34-49, (2024)

Secure Medical Records Through Big Data Analytics and Blockchain

Hamad Almani , Shailendra Mishra , Aditi Singh

As healthcare shifts to digital platforms, the healthcare sector is suffering from multiple security vulnerabilities that make it vulnerable to various types of cyberattacks. Therefore, robust security solutions need to be implemented to resolve these vulnerabilities. In this context, blockchain technology has emerged as a promising solution in several sectors, including the healthcare sector. This study harnesses blockchain technology to improve medical record management. By integrating blockchain, we address issues like data breaches and inefficient data sharing. The proposed study ensures a seamless health record exchange that is secure, transparent, and beneficial to both patients and healthcare providers. The goal of this study is to empower patients to be more in control of their data while streamlining processes and enhancing security for healthcare institutions. Medical records are increasingly secure, interoperable, and accessible when blockchain technology and big data are used. According to the study, healthcare workers recognize the importance of protecting medical records through blockchain technology and big data, which can improve security, interoperability, and accessibility. This minimizes concerns related to data manipulation while providing a more cost-effective and efficient method of managing medical records. Medical records management is made more cost-effective and efficient by reducing concerns related to data manipulation.

Vol. 14 Issue. 1 PP. 50-63, (2024)

ML-based Intrusion Detection for Drone IoT Security

Abdullah Al-Fuwaiers , Shailendra Mishra

The integration of drones into various industries brings about cybersecurity challenges due to their reliance on internet connectivity. To address this, we propose a comprehensive cybersecurity architecture leveraging machine learning (ML) algorithms and Internet of Things (IoT) technologies within the Internet of Drones (IoD) framework. Our architecture employs IoT-enabled sensors strategically placed across the drone ecosystem to collect and analyze data on system behaviors, communication patterns, and environmental variables. This data is then processed by a centralized platform equipped with sophisticated ML algorithms for pattern identification and anomaly detection. A key feature is the dynamic learning mechanism, enabling real-time intrusion detection by adapting to evolving threats. By combining IoT and ML, the system proactively defends against cyberattacks by distinguishing between typical and abnormal activity. Emphasis is placed on data integrity and confidentiality through secure communication protocols and cryptographic algorithms. Extensive simulations and tests validate the framework's effectiveness in various IoD scenarios, demonstrating its ability to swiftly identify intrusions and informing future enhancements. This comprehensive study meticulously examines the pressing cybersecurity concerns within the burgeoning drone industry. It proposes a robust architectural framework designed to enhance security for drone-enabled applications in our increasingly interconnected world. By harnessing the synergies between Internet of Things (IoT) and Machine Learning (ML) technologies, this innovative approach aims to fortify the integrity and reliability of drone systems.

Vol. 14 Issue. 1 PP. 64-78, (2024)

Security Validation in OpenStack: A Comprehensive Evaluation

Mohammed Saffran , Shailendra Mishra

The study delves into the security architecture of OpenStack, an open-source cloud platform that is increasingly prevalent in modern computing environments. Its primary goal is to rigorously assess and confirm hypotheses about OpenStack's security infrastructure while identifying vulnerabilities and potential threats using a comprehensive security evaluation framework. The study utilizes a multifaceted security assessment methodology to analyze both private and public cloud deployments of OpenStack. This methodology involves various techniques, including vulnerability scanning, penetration testing, and analysis of security policies and configurations. Benchmarking against industry standards and previous studies further strengthens the analytical framework, ensuring a thorough exploration of various dimensions of OpenStack security. The assessment revealed that OpenStack has a robust security posture, with vulnerabilities detected in only 2% of cases across both private and public cloud deployments. The study also found a resilience rate of 95% against common security challenges. The comprehensive analysis covered various dimensions of OpenStack security, providing valuable insights into the platform's security resilience and vulnerabilities, thereby significantly contributing to the body of knowledge in cloud security research. The research underscores the importance of implementing robust security protocols in OpenStack environments to ensure the reliability of cloud infrastructure. Regular security updates and adherence to best practices can strengthen the security posture of OpenStack deployments. The insights from this study can inform the development of guidelines and policies aimed at enhancing security practices in cloud computing environments. Overall, the study evaluates the security framework of OpenStack and emphasizes the significance of implementing robust security measures to ensure the dependability of cloud infrastructure, guiding the creation of recommendations and superior practices for strengthening security in cloud computing environments.

Vol. 14 Issue. 1 PP. 79-95, (2024)

Investigating the Efficacy of Deep Reinforcement Learning Models in Detecting and Mitigating Cyber-attacks: a Novel Approach

S. Phani Praveen , Anuradha Chokka , Pappula Sarala , Rajeswari Nakka , Suresh Babu Chandolu , V. Esther Jyothi

Ordinary defence components like rule-based firewalls and mark based detection are not staying aware of the always expanding intricacy and frequency of cyber security dangers. The reason for this work is to explore the way that deep reinforcement learning (DRL), a subfield of artificial intelligence famous for its viability in handling testing decision-production situations, may be utilized to improve cyber security conventions. To mimic and balance threatening cyber-attacks, we present a system that utilizes deep reinforcement learning (DRL). We propose a specialist based model that can learn and adjust ceaselessly in powerful network security situations. In light of the present status of the network and the rewards it gets for its decisions, the specialist concludes what the best game-plans are. Specifically, we utilize the policy gradient (PG)- based double deep Q-network (DDQN) model and trial on three different datasets: NSL-KDD, CIC-IDS, and AWID. Our review demonstrates the way that DRL can really further develop the detection after-effects of cyber-attacks. Utilizing the policy gradient DDQN model on different datasets, we find prominent upgrades in cyber security conventions. Specific boundary modifications upgrade the viability of our philosophy much more, displaying empowering results on different datasets. This exploration features the potential of deep reinforcement learning (DRL) as a successful instrument in the field of cyber security. Our examination progresses detection techniques and gives a versatile arrangement that can be applied to an assortment of cyber security worries by giving areas of strength for a to demonstrating and relieving cyber dangers.

Vol. 14 Issue. 1 PP. 96-113, (2024)

Concealed Chosen Plaintext Attack on Multiple S-boxes Based Image Encryption

Ahmed Rabea , Mohamed G. Abdelfattah , Abeer T. Khalil , Ali E. Takieldeen

Chosen plaintext attacks (CPA) pose a significant security risk to encryption algorithms. However, it can be difficult to perform such an attack without direct access to the encryption process. This paper introduces a new cryptoanalysis method that uses hidden CPA to analyze image encryption schemes based on substitution boxes (S-boxes) Unlike traditional CPA methods, the proposed algorithm does not require that they can directly into the encryption process. Instead, a hidden attack vector is embedded in the natural host image to reduce the risk of attack detection. By asking the owner of the encryption algorithm to encrypt this encryption image and provide a cipher image, the input vector can be compared with its encrypted counterpart This can have an effective S-box and break encryption the algorithm, which does not interact directly with the encryption process. Experimental results demonstrate that the proposed method can completely recover cipher images in cascading S-box encryption schemes, regardless of the number of S-boxes used. Additionally, it conceals the CPA vector within the host image imperceptibly, achieving a high PSNR of 49.47 dB, indicating minimal visual distortion. Furthermore, our CPA significantly outperforms existing techniques in speed, recovering a  grayscale image in just 1.2 seconds. This method provides a simple yet effective cryptanalysis tool to evaluate the security of such image encryption schemes against CPAs.

Vol. 14 Issue. 1 PP. 114-124, (2024)

Fortifying Connected Vehicles Based Cybersecurity Measures for Secure Over-the-Air Software Updates

Shashikant Patil , Senthil Kumar A. , Saket Mishra , N. Gobi , Intekhab Alam , Romil Jain

The emergence of connected vehicles has transformed the automotive sector by enhancing the vehicle’s functionality, efficiency, and safety. The performance and security of these vehicles significantly rely on the deployment of the over-the-air software update. However, the execution of OTA comes with many challenges, especially with regard to security vulnerabilities and risks. The current paper delves into the complexities of the secure OTA software update for connected vehicles addressing the most critical issues; authentication; encryption and integrity verification, and risk management. Through advanced cryptographic methodologies, stringent authentication processes, and secure communication channels, automotive manufacturers and other service providers can guarantee the integrity and confidentiality of the updates, and consumers’ data from malicious attack. Moreover, the paper explores the regulatory and other standards-related matters that control the use of OTA in the automotive sector. An understanding of the secure OTA update mechanisms aids the stakeholders in establishing a resilient connection in connected vehicles boosting consumer trust and the future of the automobiles industry.

Vol. 14 Issue. 1 PP. 125-140, (2024)

Design of Novel Cryptographic Model Using Zero-Knowledge Proof Structure for Cyber Security Applications

S. Anthoniraj , Rahul Mishra , Shweta Loonkar , Trapty Agarwal , Gunveen Ahluwalia , Amandeep Gill

Privacy and security in the current modern, digital communication and data transfer-oriented world has become imperative. Most commonly used encryption methods often involve exposing sensitive information, which might be an open gate for potential vulnerabilities. This paper aims to explore the topic of applying ZKPs in cybersecurity in a comprehensive manner. For this purpose, Proposed work will provide an exhaustive description of the basic concepts of Zero-Knowledge Proofs , which refer to both the interactive and non-interactive forms of the product. Additionally, the study will focus on presenting various cryptographic protocols and algorithms utilizing Zero-Knowledge Proofs , such as zk-SNARKs and zk-STARKs . In addition to theoretical studies, Proposed work analyze the practical implementation details of Zero-Knowledge Proofs implementations , cryptographic libraries, programming languages, and frameworks commonly used to create ZKP-based applications . Zero-knowledge proofs enable groundbreaking approaches to address cybersecurity problems with an emphasis on user privacy and data confidentiality. On average, cryptographic operations experienced delays of approximately 10 milliseconds which was not intrusive for real-time systems. The system’s throughout remained at a steady average of 100 Mbps all times, so it performed well at processing data despite cryptographic overhead. The packet delivery ratio was constantly high at 98%, implying that most data packets were delivered consistently even over encrypted communication paths.

Vol. 14 Issue. 1 PP. , (2024)

Enhancing Energy Efficiency in Heterogeneous Cyber Security Networks Using Deep Q-Networks Data Routing

Gowrishankar J. , Bhargavi Gaurav Deshpande , Dhiraj Singh , Awakash Mishra , Zeeshan Ahmad Lone , Bharat Bhushan

Since heterogeneous wireless sensor networks consist of sensor nodes of varying capacity and energy-constrained, effective routing techniques are essential to ensure the proper functioning of the systems. Most traditional routing techniques fail to dynamically adjust to varying network conditions, leading to ineffective use of energy and poor performance. Therefore, deep Q-Networks implementation of reinforcement learning provides a beneficial approach to the problem due to adaptive routing decisions depending on the environmental signals and systems’ performance. Therefore, the suggested approach integrates Deep Q-Network into the data routing framework for different Wireless Sensor Networks to improve energy-efficiency and ensure data delivery. The DQN agent is designed to pick routing functions that maximize total rewards which depend on energy consumption, packet delivery, and network stability. Hence, the decentralized learning allows each sensor node to develop its routing policy based on the local environment under the interactions with their neighbors. Therefore, the approach promotes the ability to adapt and learn, crucial for changing network conditions. Thus, extensive simulation was conducted to assess the applicability of the DQN-based routing for different WSNs, proving the significant reducing of energy consumption compared to traditional routing approaches with an average of 25% regardless of the network formation and traffic conditions . This approach also demonstrates lower packet loss of 15%, revealing enhanced data transfer reliability . In particular, the existing on demand routing protocols, only forward the request that arrives first from each route discovery process. The attacker exploits this property of the operation of route discovery. The network lifetime was extended by 30% showing growing energy efficiency for a long-term run. In general, the integration of Deep Q-Networks into data routing provides an excellent opportunity to improve energy-efficiency in different types of wireless sensor networks. Hence, the proposed approach effectively optimizes the routing solutions in real-time, using adaptive lenience, also showing enhancing data delivery, and improving the systems’ lifetime. Hence, the presented results prove the capability of reinforcement learning methods to address the growing challenges of WSNs and leave space for further research in autonomous WSN improvement.

Vol. 14 Issue. 1 PP. 160-178, (2024)

Cyber Security Protection in Roadside Unit Based on Cross-Layer Adaptive Graph Neural Networks (Gnns) in Vanet

Raj Kumar , Sakshi Pandey , Asha KS , Rakesh Kumar Yadav , Abhinav Mishra , Sunil Sharma

The proposed systems can improve cyber security in VANET applications by enabling efficient detection of complex attacks on the RSU component. The subsequent sections discuss the systems that are applied and support the suggestions for improving the VANET trustworthiness. VANETs and show that the utilization of Cross-Layer Adaptive GNNs can improve cyber security and LEARNING in VANET-based RSUs. As a result, the suggested system can provide robust ways for detecting cyber-attacks by: modeling the network architecture using graphs while combining information regarding several protocol layers to detect complicated interactions between the network entities and find the abnormal activities. the nature of the GNN enables it to update in real-time by adapting to the evolving attack patterns and the shifting network conditions, making them sturdy and flexible defense ways for cyber security. The proposed network e systems can efficiently detect multiple cyber threats and focus on reducing the number of false positives while maintaining low computation costs. Therefore, chances are that incorporating the Cross-layer adaptive GNNs into the RSUs can improve cyber security in VANETs, enhancing the robustness and reliability of prospective smart transportation systems.

Vol. 14 Issue. 1 PP. 179-196, (2024)

NCBI Medical Data Encryption with Lossless DNA Compression

Anfal Emad Lafta , Sahar Adil Kadhum

The health information data includes reports on the patient’s condition, including addresses, names, tests, treatments, diagnoses, and medical history. It is sensitive information for patients, and all means of protection must be provided to prevent third parties from manipulation or fraudulent use. It has been discovered that DNA is now a reliable and efficient biological media for securing data. Data encryption is made possible by DNA's bimolecular computing powers. In this paper proposed a new strategy of safeguard the transfer of sensitive data over an unsecured network using cryptography with non-liner function, and DNA lossless compression to enhance security. The work gains best results in compression processes, as percentages range 75%. for character compression, the different rate ranges between 91% to 94%, and the compression rate ranges from 35% to 37%. the retrieving data with an accuracy rate up to 100% without any data loss, as well as excellent percentages within the Compression Ratio, Compression Factor, Error Rate, Accuracy measures.

Vol. 14 Issue. 1 PP. 197-206, (2024)

Novel Slumped SRAM Configuration using QCA Leveraging Differential Voltage Sensing for Enhanced Stability and Efficiency

N. Naga Saranya , V. Jean Shilpa , K. Jayakumar , P. Senthil , M. Arun

This paper presents a novel Slumped Static Random-Access Memory (SRAM) configuration utilizing Quantum-dot Cellular Automata (QCA) technology, aimed at achieving enhanced stability and efficiency. Traditional CMOS-based SRAM designs face significant challenges related to power consumption and scalability as technology nodes shrink. QCA, with its potential for ultra-low power dissipation and high-density integration, emerges as a promising alternative. Our proposed SRAM configuration leverages a unique differential voltage sensing mechanism to bolster the stability of the memory cells, particularly under conditions of variability and noise. Through detailed simulations and comparative analysis, we demonstrate that the Slumped SRAM configuration not only improves static noise margin (SNM) but also reduces power consumption and enhances overall read/write speed. The results indicate a substantial improvement in stability and operational efficiency, positioning this design as a viable solution for future high-performance, low-power memory applications. Through detailed simulations and comparative analysis, we demonstrate that the Slumped SRAM configuration achieves a static noise margin (SNM) improvement of 35% over conventional CMOS-based SRAM designs. Additionally, the proposed design reduces power consumption by 40% and enhances read/write speed by 25%. These results indicate a substantial improvement in stability and operational efficiency, positioning this design as a viable solution for future high-performance, low-power memory applications.

Vol. 14 Issue. 1 PP. 207-217, (2024)

Prediction of Traffic Congestion in Vehicular Ad-Hoc Networks Employing Extreme Deep Learning Machines (Edrlm)

R. Logesh Babu , Jagannadha Naidu K. , V. Jeya Ramya , Regan D.

Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using real-world VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices.

Vol. 14 Issue. 1 PP. 218-226, (2024)

A Novel Secured Deep Learning Model for COVID Detection Using Chest X-Rays

Chhaya Gupta , Vasima Khan , Ramya Srikanteswara , Nasib Singh Gill , Preeti Gulia , Sindhu Menon

Automatic detection of a medical disease is a need of the hour as it helps doctors diagnose diseases and provide fast medical reports. COVID-19 is a deadly disease for which an automated detection system may be helpful. This study proposes a unique hybrid deep learning model, COVIDet, based on CNN and the speeded-up robust features (SURF) extraction approach to diagnose COVID-19 using chest x-ray images. SURF is utilized in this work to extract features, and the output is then transferred to a 25-layer CNN for detection using the extracted features. This investigation employed 4623 COVID-19 positive X-ray pictures or 8055 total. The suggested hybrid model also contrasts with the study's VGG19, Resnet50, Inception, Xception, and traditional CNN models. The proposed model had a 98.01% accuracy, a 97.03% F1-score, a 98.65% sensitivity, a 99% precision, and a 95.65% specificity. The proposed model can be further improved when more datasets are available and can help doctors to diagnose patients quickly and efficiently. Using chest X-ray pictures, a secured web application is also developed to identify COVID-19. The user sends the application a chest X-ray image, and in return, it determines whether an individual is COVID-19 positive or not, cutting down on testing time. In Covid times, when people are standing in long queues and waiting for their turns, this application would greatly help. The application uses the pre-trained COVIDet model in the backend.

Vol. 14 Issue. 1 PP. 227-244, (2024)

Journal of Cybersecurity and Information Management

Journal DOI

Journal Menu