Interrelationship between Body Weight and Body Size Parameters in Chinchilla and New Zealand White Rabbit Genotypes in Abeokuta, Nigeria

Traits of economic importance in rabbit when measured provide attributes that may be used as selection criteria by livestock breeders. This study was carried out to determine the interrelationship between body weight and body size parameters and to develop linear equations for predicting body weight using some body size parameters in two rabbit genotypes. Chinchilla and New Zealand White rabbit genotypes were selected for this study, with each breed having 20 does and 4 bucks making a total of 48 rabbits (precisely breeders). The animals were intensively managed and body weight, body length, ear length, heart girth, head length and tail length were measured on weekly basis. Body size parameters were correlated and regressed with body weight of each genotype and results were compared. At the end of the twelve-week study period, body weights were 2.074±0.280 kg and 2.130±0.240 kg for Chinchilla and New Zealand White rabbit genotypes, respectively. Body size parameters considered were positively correlated with body weight for both genotypes. Ear length, heart girth and head length only were significantly (P < 0.01) correlated with body weight for Chinchilla, while body size parameters were found to be significantly (P < 0.01) correlated with body weight for New Zealand White rabbit. Linear equations were developed for predicting the body weight from these body size parameters in each genotype. We observed that heart girth proved to be the best predictor for body weight in New Zealand White and Chinchilla rabbit genotypes, having a coefficient of determination (r²) of 0.529 and 0.547, respectively. Keywords: Body dimensions, Breeds of rabbit, Bucks, Does, Prediction, Selection criteri

Survey of Automotive Controller Area Network Intrusion Detection Systems

Novel attacks continue to appear against in-vehicle networks due to the increasing complexity of heterogeneous software and hardware components used in vehicles. These new components introduce challenges when developing efficient and adaptable security mechanisms. Several intrusion detection systems (IDS) have been proposed to identify and protect in-vehicle networks against malicious activities. We describe the state-of-the-art intrusion detection methods for securing automotive networks, with special focus on the Controller Area Network (CAN). We provide a description of vulnerabilities, highlight threat models, identify known attack vectors present in CAN, and discuss the advantages and disadvantages of suggested solutions

Automotive Intrusion Detection Based on Constant CAN Message Frequencies Across Vehicle Driving Modes

The modern automobile relies on numerous electronic control units communicating over the de facto standard of the controller area network (CAN) bus. This communication network was not developed with cybersecurity in mind. Many methods based on constant time intervals between messages have been proposed to address this lack of security issue with the CAN bus. However, these existing methods may struggle to handle variable time intervals between messages during transitions of vehicle driving modes. This paper proposes a simple and cost-effective method to ensure the security of the CAN bus that is based on constant message frequencies across vehicle driving modes. This proposed method does not require any modifications on the existing CAN bus and it is designed with the intent for efficient execution in platforms with very limited computational resources. Test results with the proposed method against two different vehicles and a frequency domain analysis are also presented in the paper

Anomaly Detection Approach Using Adaptive Cumulative Sum Algorithm for Controller Area Network

The modern vehicle has transformed from a purely mechanical system to a system that embeds several electronic devices. These devices communicate through the in-vehicle network for enhanced safety and comfort but are vulnerable to cyber-physical risks and attacks. A well-known technique of detecting these attacks and unusual events is by using intrusion detection systems. Anomalies in the network occur at unknown points and produce abrupt changes in the statistical features of the message stream. In this paper, we propose an anomaly-based intrusion detection approach using the cumulative sum (CUSUM) change-point detection algorithm to detect data injection attacks on the controller area network (CAN) bus. We leverage the parameters required for the change-point algorithm to reduce false alarm rate and detection delay. Using real dataset generated from a car in normal operation, we evaluate our detection approach on three different kinds of attack scenarios

Instructions to authors for case reporting are limited:A review of a core journal list

BACKGROUND: Case reports are frequently published in the health care literature, however advice on preparing such reports using the "instructions to authors" pages of journals is alleged to be limited. However, to our knowledge, this has not been formally evaluated. As roles of case reports may vary according to the case and the clinical specialities, one might expect the advice to authors to vary according to journal clinical grouping. METHODS: We surveyed the current advice available to authors of case reports from 'instructions to authors' pages of a core collection of 249 journals ('Hague' list). These were examined and compared for advice or recommendation on writing case reports. Of these, 163 (65%) published case reports and provided instructions on this publication type. Data were extracted on items of style and content of case reports, using a piloted data extraction form. RESULTS: Journals that published case reports were grouped into medical (n = 81, 50%), surgical (n = 38, 23%) and generic or multidisciplinary (n = 44, 27%) categories. There was a difference among the medical, surgical and generic or multidisciplinary journals in the maximum number of words and pages allowed but no difference in the number of figures, tables, references, authors, abstract or synopsis, indexing or key words and consent. Additionally, there was no statistically significant difference among the three different categories of journals regarding the content of the case reports. CONCLUSIONS: Of the journals reviewed, we found that 'instructions to authors' pages provided limited and varied information for preparing a case report. There is a need for consensus, and more consistent guidance for authors of case report

Reverse Engineering Controller Area Network Messages using Unsupervised Machine Learning

The smart city landscape is rife with opportunities for mobility and economic optimization, but also presents many security concerns spanning the range of components and systems in the smart ecosystem. One key enabler for this ecosystem is smart transportation and transit, which is foundationally built upon connected vehicles. Ensuring vehicular security, while necessary to guarantee passenger and pedestrian safety, is itself challenging due to the broad attack surfaces of modern automotive systems. A single car contains dozens to hundreds of small embedded computing devices known as electronic control units (ECUs) executing 100s of millions of lines of code; the inherent complexity of this tightly-integrated cyber-physical system (CPS) is one of the key problems that frustrate effective security. We describe an approach to help reduce the complexity of security analyses by leveraging unsupervised machine learning to learn clusters of messages passed between ECUs that correlate with changes in the CPS state of a vehicle as it moves throughout the world. Our approach can help to improve the security of vehicles in a smart city, and can leverage smart city infrastructure to further enrich and refine the quality of the machine learning output