11 research outputs found
An improved RSA cryptosystem based on thread and CRT / Saheed Yakub Kayode and Gbolagade Kazeem Alagbe
Get PDFThis paper proposes an efficient approach to improve Rivest Shamir Adleman (RSA) algorithm using parallel technique. RSA public-key cryptosystem has been the most popular and interesting security technique for majority of applications such as internet protocols, secure internet acces and electronic commerce. The main bottleneck of RSA algorithm is that they are slower compared to symmetric cryptography key alternatives simply because of their foundation in modular arithmetic. Hence, how to make a more efficient and faster implementation of Rivest Shamir Adleman algorithm (RSA) is a great concern to researchers in the field of cryptography. In this paper, we propose a parallel implementation technique using Chinese Remainder Theorem and thread on encryption and decryption operation in RSA when files are to be encrypted and decrypted. Also, in our method, the key size is extended from 1024 bits to 2048 bits in length to provide a good level of security, since 1024 bits key size is no more appropraiate for protecting data. We use a parallel technique that divides RSA power process into seperate threads and employs the use of Chinese Remainder Theorem (CRT) to decrease the time required for both encryption and decryption operation. Java programming language is used to implement the algorithm. Experimental results indicate that as the thread level increases, the encryption and decryption time, which is the most time consuming operation, decreases which shows an improved speed of the RSA cryptosystem.The proposed implementation has a great potential to effectively deal with the RSA algorithm slow speed
An improved RSA cryptosystem based on thread and CRT
No full textThis paper proposes an efficient approach to improve Rivest Shamir Adleman (RSA) algorithm using
parallel technique. RSA public-key cryptosystem has been the most popular and interesting security
technique for majority of applications such as internet protocols, secure internet acces and electronic
commerce. The main bottleneck of RSA algorithm is that they are slower compared to symmetric
cryptography key alternatives simply because of their foundation in modular arithmetic. Hence, how
to make a more efficient and faster implementation of Rivest Shamir Adleman algorithm (RSA) is a
great concern to researchers in the field of cryptography. In this paper, we propose a parallel
implementation technique using Chinese Remainder Theorem and thread on encryption and
decryption operation in RSA when files are to be encrypted and decrypted. Also, in our method, the
key size is extended from 1024 bits to 2048 bits in length to provide a good level of security, since
1024 bits key size is no more appropraiate for protecting data. We use a parallel technique that divides
RSA power process into seperate threads and employs the use of Chinese Remainder Theorem (CRT)
to decrease the time required for both encryption and decryption operation. Java programming
language is used to implement the algorithm. Experimental results indicate that as the thread level
increases, the encryption and decryption time, which is the most time consuming operation, decreases
which shows an improved speed of the RSA cryptosystem.The proposed implementation has a great
potential to effectively deal with the RSA algorithm slow speed
A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the Internet of Things
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XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles
Get PDFJournal Article.Department of Information Systems, North-West University, Mahikeng,The Internet of Vehicles (IoV) is a network of interconnected vehicles that use modern communication technologies to communicate with each other and the surrounding infrastructure. The IoV is a novel network that experiences a continuous emergence and evolution of various forms of attacks. This research addresses the growing challenge of detecting zero-day botnet attacks within the IoV, a complex network that enhances realtime communication among vehicles and surrounding infrastructure to improve traffic management, safety, and driving experiences. Traditional machine learning-based intrusion detection systems (IDS) for IoV face two key limitations: the requirement for large labeled datasets and the “black box” issue, where the reasoning behind model decisions is not transparent, reducing user and stakeholder confidence. To solve these problems, this research proposes an eXplainable Artificial Intelligence (XAI) Ensemble Transfer Learning (TL) model specifically for detecting zero-day attacks in IoV. The proposed model integrates deep Shapley Additive Explanations (SHAP), providing transparency and making decisions understandable to cybersecurity professionals. Additionally, the model employs hybrid bidirectional long-short-term memory with autoencoders (BiLAE) to reduce the dimensionality of IoV network traffic, improving computational efficiency. It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. Experimental results showed that the model performed with an accuracy of 100 %, precision of 100 %, recall of 100 %, F1-score of 100 %, and Matthew Correlation Coefficient of 100 % in binary-class situations for internal vehicular (CAN) networks and achieved 99.88 % accuracy and similarly high metrics in multi-class scenarios for external vehicular networks(N-BaIoT). Compared to state-of-the-art techniques, the model proved to be more effective in detecting zero-day botnet attacks, reducing reliance on large datasets. Unlike traditional black-box models, the XAI component of the ensemble model offers insight into the decision-making process. It allows network administrators and security experts to understand how specific patterns in the data contribute to detection, making the system more transparent. The solution is highly adaptable and scalable for real-time application, designed to operate efficiently even on IoV gateway electronic control units with limited computational power
Feature Selection with IG-R for Improving Performance of Intrusion Detection System
Get PDFAs the popularity of the internet computer continued to grow and become an indispensable in human life, the security of computer network has become an important issue in computer security field. The Intrusion Detection System (IDS) is a system used in computer security for network security. The feature selection stage of IDS is considered to be the most critical stage in IDS. This stage is very costly both in efforts and time. However, many machine learning approaches have been presented to improve this stage in order to improve the performance of an IDS. However, these approaches did not give desirable results with respect to the detection accuracy in the IDS. A novel technique is proposed in this paper combining the Information Gain and Ranker (IG+R) method as the feature selection strategy with Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as the classifiers. The performance of these IG+R-NB, IG+R-SVM, and IG+R-KNN was evaluated on NSLKDD dataset. The experimental results of our proposed method gave high accuracy and low false alarm rate. The results obtained was compared and benchmarked with existing works. The results of this paper outperformed the existing approaches in terms of the detection accuracy
Diabetic retinopathy detection using principal component analysis multi-label feature extraction and classification
No full textDiabetic Retinopathy (DR) is the most common cause of eyesight loss that affects millions of people worldwide. Although there are recognized screening procedures for detecting the condition, such as fluorescein angiography and optical coherence tomography, the majority of patients are unaware and fail to have such tests at the proper time. Prompt identification of the condition is critical in avoiding vision loss, which occurs when Diabetes Mellitus (DM) is left untreated for an extended length of time. Several Machine Learning (ML) and Deep Learning (DL) algorithms have been used on DR datasets for disease prediction and classification, however, the majority of them have ignored the element of data pre-processing and dimensionality reduction, which are known as a major gap resulting in biased findings. In the first line of this research, data preprocessing was performed on the color Fundus Photographs (CFPs). Subsequently, we performed feature extraction with Principal Component Analysis (PCA). A Deep Learning Multi-Label Feature Extraction and Classification (ML-FEC) model based on pre-trained Convolutional Neural Network (CNN) architecture was proposed. Then, transfer learning was applied to train a subset of the images using three state-of-the-art CNN architectures, namely, ResNet50, ResNet152, and SqueezeNet1 with parameter-tuning to identify and classify the lesions. The experimental findings revealed an accuracy of 93.67% with a hamming loss of 0.0603 for ResNet 50, an accuracy of 91.94%, and Hamming Loss of 0.0805 for Squeezenet1 and an accuracy of 94.40% with Hamming loss of 0.0560 was achieved by ResNet 152 which demonstrates the suitability of the model for implementation in daily clinical practice and to support large scale DR screening programs
An Effective Intrusion Detection in Mobile Ad-hoc Network Using Deep Belief Networks and Long Short-Term Memory
Get PDFA Mobile Ad-hoc Network (MANET) is a self-organizing collection of mobile devices communicating in a distributed fashion across numerous hops. MANETs are an appealing technology for many applications, including rescue operations, environmental monitoring, tactical operations, and so on, because they let people communicate without the usage of permanent infrastructure. This flexibility, however, creates additional security vulnerabilities. Because of its benefits and expanding demand, MANETs have attracted a lot of interest from the scientific community. They do, however, seem to be more vulnerable to numerous attacks that wreak havoc on their performance than any network. Traditional cryptography techniques cannot entirely defend MANETs in terms of fresh attacks and vulnerabilities due to the distributed architecture of MANETs; however, these issues can be overcome by using machine learning approaches-based intrusion detection systems (IDS). IDS, typically screening system processes and identifying intrusions, are commonly employed to supplement existing security methods because preventative techniques are never enough. Because MANETs are continually evolving, their highly limited nodes, and the lack of central observation stations, intrusion detection is a complex and tough process. Conventional IDSs are difficult to apply to them. Existing methodologies must be updated for MANETs or new approaches must be created. This paper aims to present a novel concept founded on deep belief networks (DBN) and long shortterm memory (LSTM) for MANET attack detection. The experimental analysis was performed on the probe, root to local, user to root, and denial of service (DoS) attacks. In the first phase of this paper, particle swarm optimization was used for feature selection, and subsequently, the DBN and LSTM were used for the classification of attacks in the MANET. The experimental results gave an accuracy reaching 99.46%, a sensitivity of 99.52%, and a recall of 99.52% for DBN and LSTM accuracy reaching 99.75%, a sensitivity of 99.79%, and a recall of 99.79%
A novel hybrid ensemble learning for anomaly detection in industrial sensor networks and SCADA systems for smart city infrastructures
No full textCritical Infrastructures (CIs) use Supervisory Control and Data Acquisition (SCADA) systems for monitoring and remote control. Sensor networks are being integrated into all areas of the infrastructures of smart cities. The sensor network data stream contains information that can be utilized to model and control the activity of these infrastructures. However, SCADA systems are constantly exposed to a variety of diverse intrusions, making detection with traditional intrusion detection systems (IDS) extremely difficult. Due to their unique specifications, conventional security solutions, like antivirus and firewall software, are unsuitable for properly securing SCADA systems. In addition, anomaly detection in industrial sensor networks (ISNs) should occur in real time. Therefore, effectively identifying cyberattacks in major SCADA systems is unquestionably essential for enhancing their resilience, ensuring safe operations, and avoiding expensive maintenance. We developed a novel hybrid ensemble model approach to address these issues. This paper's primary objective is to detect hostile intrusions that have already circumvented firewalls and typical IDS. In this paper, we propose a hybrid Ensemble Learning Model (ELM) for intrusion detection in SCADA systems with ISNs utilizing a tangible data gathered from a gas pipeline system given by Mississippi State University (MSU), the water system, and the high-dimensional University of New South Wales-NB 2015 (UNSW-NB15) data that reflects a typical attack in the Internet of Things (IoT) environment. The unity normalization method was adopted for data preprocessing, and the Principal Component Analysis (PCA) was utilized for feature extraction of the high-dimensional datasets. Grey Wolf Optimizer (GWO) was used for optimizing the bagging, stacking, Adaboost, and an ensemble of classifiers Naive Bayes and Support Vector Machine with a majority voting technique. Then, we utilized the proposed approach founded on the bijective soft-set approach for efficient ELM selection. The experiment was conducted in two phases: Initially, without PCA + GWO for feature extraction and selection on the ELM, and subsequently, with PCA + GWO for feature extraction and selection on the ELM. PCA + GWO on the ensemble of classifiers NB + SVM provided an accuracy of 99%, precision of 100%, recall of 100%, and detection rate of 99.90%, outpacing the ensemble of classifiers without PCA feature extraction and GWO optimization approaches
A machine learning-based intrusion detection for detecting internet of things network attacks
Get PDFThe Internet of Things (IoT) refers to the collection of all those devices that could connect to the Internet to collect and share data. The introduction of varied devices continues to grow tremendously, posing new privacy and security risks—the proliferation of Internet connections and the advent of new technologies such as the IoT. Various and sophisticated intrusions are driving the IoT paradigm into computer networks. Companies are increasing their investment in research to improve the detection of these attacks. By comparing the highest rates of accuracy, institutions are picking intelligent procedures for testing and verification. The adoption of IoT in the different sectors, including health, has also continued to increase in recent times. Where the IoT applications became well known for technology researchers and developers. Unfortunately, the striking challenge of IoT is the privacy and security issues resulting from the energy limitations and scalability of IoT devices. Therefore, how to improve the security and privacy challenges of IoT remains an important problem in the computer security field. This paper proposes a machine learning-based intrusion detection system (ML-IDS) for detecting IoT network attacks. The primary objective of this research focuses on applying ML-supervised algorithm-based IDS for IoT. In the first stage of this research methodology, feature scaling was done using the Minimum-maximum (min–max) concept of normalization on the UNSW-NB15 dataset to limit information leakage on the test data. This dataset is a mixture of contemporary attacks and normal activities of network traffic grouped into nine different attack types. In the next stage, dimensionality reduction was performed with Principal Component Analysis (PCA). Lastly, six proposed machine learning models were used for the analysis. The experimental results of our findings were evaluated in terms of validation dataset, accuracy, the area under the curve, recall, F1, precision, kappa, and Mathew correlation coefficient (MCC). The findings were also benchmarked with the existing works, and our results were competitive with an accuracy of 99.9% and MCC of 99.97%
Feature selection in intrusion detection systems: a new hybrid fusion of Bat algorithm and Residue Number System
Get PDFThis research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed in the past for attribute selection and IDS performance improvement. However, these methods have often fallen short in terms of detection accuracy, detection rate, precision, and F-score. To tackle these issues, the paper presents a novel hybrid feature selection approach combining the Bat metaheuristic algorithm with the Residue Number System (RNS). Initially, the Bat algorithm is utilized to partition training data and eliminate irrelevant attributes. Recognizing the Bat algorithm\u27s slower training and testing times, RNS is incorporated to enhance processing speed. Additionally, principal component analysis (PCA) is employed for feature extraction. In a second phase, RNS is excluded for feature selection, allowing the Bat algorithm to perform this task while PCA handles feature extraction. Subsequently, classification is conducted using naive bayes, and k-Nearest Neighbors. Experimental results demonstrate the remarkable effectiveness of combining RNS with the Bat algorithm, achieving outstanding detection rates, accuracy, and F-scores. Notably, the fusion approach doubles processing speed. The findings are further validated through benchmarking against existing intrusion detection methods, establishing their competitiveness
