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Network intrusion detection systems (NIDS) are essential to mitigate the increasing cyber security risks since they protect computer networks from a wide range of attacks. Class imbalances in the datasets frequently impede the effectiveness of these systems, leading to incorrect classification of minority classes. In addition, improper feature selection may lead to a degradation of results and higher computing time. This research combines the Jaya optimization technique with the Synthetic Minority Oversampling Technique—Edited Nearest Neighbors (SMOTE-ENN) method to address the problems of feature dimensionality and class imbalance, offering an efficient approach to identifying network intrusions. The Jaya optimization algorithm selects the most suitable features after the preprocessing stage. We test the proposed method on two widely used IDS datasets, namely UNSW-NB15 and NSL-KDD. We perform the classification task using five machine-learning approaches: decision trees (DT), random forests (RF), bagging, J48, and extra-tree classifiers for a comprehensive analysis. The Extra Tree Classifier emerged as the best performer, achieving exceptional accuracy values of 99.94% and 99.13% for NSL-KDD and UNSW-NB15, respectively. In both datasets, our proposed model outperforms the existing solutions proposed in the literature. The proposed methodology effectively addresses the problems of feature dimensionality and class imbalance, offering an efficient approach to identifying network intrusions.

Grover’s algorithm harnesses the power of quantum computing to swiftly locate specific elements in an unstructured database, outperforming classical computers in tasks like database searching. This algorithm capitalizes on the unique ability of qubits to be in both 0 and 1 states simultaneously (superposition), allowing it to scan the entire search space at once. It then boosts the probability of the target element, making it more prominent. While the foundational concepts of Grover’s algorithm are well-documented, practical implementation using quantum operators, especially for large search spaces, remains less explored beyond basic examples with a small number of qubits. Existing general synthesis techniques often involve numerous operators or are time-consuming. Our proposed methods specifically address the amplitude-amplification component of Grover’s algorithm for any size of search space. These methods detail the required types and quantities of qubits and operators, emphasizing minimal usage and efficient assembly. Developed and evaluated in Python, our methods consistently identified target elements with over 95% accuracy and achieved configurations comparably compact as those in the existing literature but at a faster pace. We anticipate that these methods facilitate practical implementations of Grover’s algorithm across various domains.

The Controller Area Network (CAN) is a major protocol for in-vehicle network communications. This protocol is simple and efficient for message transmission and the smooth functioning of an in-vehicle system. On the other hand, the weaknesses of this protocol, such as the ID-based arbitration method for message transmission and lack of authentication mechanism, make it vulnerable to various security attacks, including DoS attacks, Fuzzy attacks, impersonation attacks, and replay attacks. Since there is no authentication mechanism for transmitted messages, we need a way to distinguish between normal and attack messages. An intrusion detection system (IDS) is an option for this problem because it can raise alarms when there are flaws in the system. IDS is very efficient for intrusion detection where messages with the same IDs are transmitted periodically. The deviation from the normal pattern of message transmission will force the IDS system to trigger alarms. Most studies on the CAN bus IDS system were based on a supervised learning approach. On the other hand, the lack of labeled datasets and a huge amount of training time make it inefficient for new attack patterns. This paper proposes a transfer learning-based IDS system for in-vehicle network intrusion detection. The extraction of quality features using transfer learning (TL) and appropriate fine-tuning methodology is used in the proposed model. This approach can use the available intrusion attack dataset to detect new attacks. The experimental results indicated that the proposed deep hybrid transfer learning (TL) model detects new threats with a high accuracy of approximately 99.9% when compared to state-of-the-art methods, while also lowering training and testing time by more than 30%.

Programming for quantum computers is complicated and time-consuming, because quantum operations are counterintuitive and their combined effects are difficult to understand. Existing tools allow automatic synthesis of quantum programs, which releases the burden of handwriting. However, many existing systems arrange predetermined operators in successive manner to gradually reduce the gap with requirements; these methods are quick but often produce lengthy programs, and they are difficult to adopt for new operators. Other systems depend on stochastic or heuristic search; they identify near-optimal programs for certain cases, but it is not easy to tune the algorithms for a wide range of cases. We propose a system that produces compact programs for most cases and easily evolves with new operators. The system automatically learns the roles of available operators by composing various possible programs. Based on the knowledge, it selects a subset of operators most appropriate for requirements and uses them to compose a program. The learning is geared toward concise programs; thus, the system tends to produce programs with the fewest operators possible. We implemented the system and evaluated it by synthesizing over 400 programs. In comparison with a state-of-the-art system, the proposed system produced programs with approximately 40-times fewer operators at the cost of increased synthesis time from seconds to minutes. We also observed that the system successfully adopted new operators by learning their differences from existing operators and utilizing them in right places. We believe that the system provides a basis of utilizing machine learning for quantum program synthesis.

The trustworthiness of nodes in Vehicular Ad-Hoc Networks (VANETs) is essential for disseminating truthful event messages. False messages may cause vehicles to behave in unintended ways, creating an unreliable transportation system. The efficiency and reliability of the transportation system can be obtained through trustworthy vehicular nodes providing correct event messages. In a VANET, the consensus issue can be resolved by employing blockchain. Even if we employ blockchain in a VANET, the trustworthiness of each message recorded needs to be verified separately since the blockchain itself does not guarantee the trust level of each event message. For instance, when there are multiple conflicting messages associated with a single accident on the road, a vote based on majority opinion can be considered one option for making a decision regarding the accident. In this work, we design the VANET event message clustering algorithm (VEMCA) to resolve the conflicting message problem. Furthermore, we develop a simulator for the VANET environment that demonstrates how the clustering algorithm can be used for event message validation. Experimental results show that our algorithm outperforms state-of-the-art clustering algorithms in terms of accuracy, precision, recall, f1-score, and computational time.

Program synthesis writes programs on behalf of humans, increasing software development productivity. Existing systems select a proper sequence of commands by assembling them in various ways and analyzing their meanings, which is accurate and fast when a detailed specification is given for each available command, including the syntax and semantics. However, preparing this specification is burdensome and must be repeated to add or modify available commands. We propose a synthesis system that requires moderate groundwork to achieve sufficient accuracy and speed. The proposed system receives the syntax of the available commands and learns their meanings independently by writing programs and observing their input-output behavior. Using the learned knowledge, the system selects a likely sequence of commands and gradually revises them to converge to a target program. We validated the system by synthesizing 1,000 integer-manipulation programs out of 245,410 possible programs. The system synthesized most of the programs within 1,000 revisions, which is 80% faster than the state-of-the-art system based on machine-learned input-output patterns. In addition, greater than 10% of the programs were synthesized within several revisions. We believe that the proposed system provides a basis for synthesis systems based on learning input-output behavior.

Despite the rapid expansion in the insurance industry, many issues remain unresolved and may require immediate action. As the insurance sector continues to evolve with the development of new technologies, it faces more challenges, especially related to data security and fraud. The fraud-prevention data and tactics presently used by insurance firms are outdated and ineffective. Additionally, insurance firms have traditionally handled the settlement of all consumer claims through lengthy manual processes. These manual processes need to be changed to provide opportunities for insurance businesses to grow. In the case of vehicles, the information obtained from an automobile data recorder can be used as evidence. Data from automated vehicles are critical because they can help the police, law enforcement agencies, and insurance companies to reconstruct the events leading up to a collision. Insurance companies require the forensic analysis of accident videos, which is a time-consuming process and involves a large amount of storage. Due to hardware limitations and associated costs, the current standalone (and often dedicated) computing infrastructures used for this purpose are quite limited. Previous research focused on simple video analysis tasks within cloud computing and blockchain technology. The requirements for a large-scale auto-insurance system are quite high and need more thorough investigation. In this paper, a review of the contribution of recent approaches to storing accidental data in cloud computing using blockchain is provided. We focused on the latest cloud and blockchain studies related to auto-insurance along with the related issues and challenges. Some useful solutions and recommendations are provided to address the identified issues and challenges in the cloud-based and blockchain-based auto-insurance sector.

In the Vehicular Ad-hoc NETworks (VANET), the collection and dissemination of life-threatening traffic event information by vehicles are of utmost importance. However, traditional VANETs face several security issues. We propose a new type of blockchain to resolve critical message dissemination issues in the VANET. We create a local blockchain for real-world event message exchange among vehicles within the boundary of a country, which is a new type of blockchain suitable for the VANET. We present a public blockchain that stores the node trustworthiness and message trustworthiness in a distributed ledger that is appropriate for secure message dissemination.

The next generation of vehicles will be autonomous, connected, electric, and intelligent with distinct requirements such as high mobility, low latency, real-time applications, seamless connectivity, and security. Blockchain can provide a good solution to the issue of secure message dissemination or secure information sharing in vehicular networks with a weak trust relationship among the nodes. In this paper, we investigate the design of a regional blockchain for VANETs, where the blockchain is shared among nodes in a geographically bounded area. We investigate how to design the regional blockchain while achieving a low 51% attack success probability. We derive a condition that guarantees a low 51% attack success probability in terms of the numbers of good nodes and malicious nodes, the message delivery time, and the puzzle computation time. The condition can provide a useful guideline for selection of several control parameters guaranteeing the stable operation of the blockchain. We run several simulations to show the validity of the condition and investigate the effects of various parameters on the 51% attack success probability. Our analysis and simulation results show that maintaining a low message delivery time for good nodes is very important in protecting the stability of the blockchain system.

An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident’s location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.

In recent years, the cloud computing model has gained increasing attention and popularity in the field of information technology. For this reason, people are migrating their applications to public, private, or hybrid cloud environments. Many cloud vendors offer similar features with varying costs, so an appropriate choice will be the key to guaranteeing comparatively low operational costs for an organization. The motivation for this work is the necessity to select an appropriate cloud storage provider offering for the migration of applications with less cost and high performance. However, the selection of a suitable cloud storage provider is a complex problem that entails various technical and organizational aspects. In this research, a dynamic Decision Support System (DSS) for selection of an appropriate cloud storage provider is proposed. A web-based application is implemented using PHP and MySQL to facilitate decision makers. The proposed mechanism has been optimized in a way that enables the system to address static database issues for which a user might not acquire the best solution. It focuses on comparing and ranking cloud storage providers by using two modules: scraping and parsing. The evaluation of the proposed system is carried out with appropriate test cases and compared with existing tools and frameworks.

Security techniques like cryptography and authentication can fail to protect a network once a node is compromised. Hence, trust establishment continuously monitors and evaluates node behavior to detect malicious and compromised nodes. However, just like other security schemes, trust establishment is also vulnerable to attack. Moreover, malicious nodes might misbehave intelligently to trick trust establishment schemes. Unfortunately, attack-resistance and robustness issues with trust establishment schemes have not received much attention from the research community. Considering the vulnerability of trust establishment to different attacks and the unique features of sensor nodes in wireless sensor networks, we propose a lightweight and robust trust establishment scheme. The proposed trust scheme is lightweight thanks to a simple trust estimation method. The comprehensiveness and flexibility of the proposed trust estimation scheme make it robust against different types of attack and misbehavior. Performance evaluation under different types of misbehavior and on-off attacks shows that the detection rate of the proposed trust mechanism is higher and more stable compared to other trust mechanisms.

The use of unmanned aerial vehicles of varying types and sizes has increased drastically in recent years. Finding the exact geometric location of these unmanned aerial vehicles is important for several reasons. In this work, we investigate a new approach to localizing unmanned aerial vehicles using multiple image sensors, such as cameras connected through sensor networks, so that sensitive areas can be protected. In the proposed approach, image sensors deployed in the ground measure the azimuth angle and angle of elevation of the target vehicle and send that information to a collector node, so it can estimate the location of the target vehicle based on the collected samples. Two estimation methods are proposed to solve the localization problem. The first scheme localizes the projection of the target vehicle on the horizontal plane and estimates the altitude later. The second scheme estimates the location of the target vehicle directly in three-dimensional space. The performance of the proposed schemes is evaluated through simulation.

Trust establishment is an important tool to improve cooperation and enhance security in wireless sensor networks. The core of trust establishment is trust estimation. If a trust estimation method is not robust against attack and misbehavior, the trust values produced will be meaningless, and system performance will be degraded. We present a novel trust estimation method that is robust against on-off attacks and persistent malicious behavior. Moreover, in order to aggregate recommendations securely, we propose using a modified one-step M-estimator scheme. The novelty of the proposed scheme arises from combining past misbehavior with current status in a comprehensive way. Specifically, we introduce an aggregated misbehavior component in trust estimation, which assists in detecting an on-off attack and persistent malicious behavior. In order to determine the current status of the node, we employ previous trust values and current measured misbehavior components. These components are combined to obtain a robust trust value. Theoretical analyses and evaluation results show that our scheme performs better than other trust schemes in terms of detecting an on-off attack and persistent misbehavior.

A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized.

In cognitive radio sensor networks, the routing methods including multiple relays have been extensively studied to achieve higher throughput and lower end-to-end delay. As one of innovative approaches, the opportunistic routing scheme was proposed. In this paper, the effectiveness of the opportunistic transmission in terms of reliability and delay of transmission is verified with an analytical way. For the analysis, we establish the probabilistic model with respect to distance and the number of relay nodes under the Rayleigh fading channels including path loss effects. Under this model, we develop a generic Markov chain model to obtain the analytical results and verify the effectiveness of the statistical analysis. The results show that an opportunistic transmission approach is better than traditional multihop transmissions in terms of successful data delivery with fewer transmissions. Consequently, it can provide an energy efficient transmission mechanism for cognitive radio sensor networks.

This paper proposes a cross-layer-based routing protocol for mobile ad hoc cognitive radio networks. The routing layer receives the spectrum-availability-related information from the medium access control layer and selects the next hop node on the route according to the spectrum information. This paper examines the expectation of channel switching in a range of scenarios and proposes a novel route selection method to mitigate the frequent channel switching. The proposed protocol distributes the routing overheads among secondary users in the network and prolongs the network lifetime. An extensive simulation was performed to evaluate the proposed approach, and the results showed that the proposed protocol extends the network lifetime and maintains a higher data delivery ratio.

A practical spare bandwidth sharing scheme required for recovering the network service from network faults is presented. A smaller amount of spare bandwidth is required by the proposed spare bandwidth sharing scheme than the conventional scheme.