Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS) to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS) and black hole attacks on vehicular ad hoc networks (VANETs). The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection

The EPATH trial

Observational studies suggested a link between bone disease and left ventricular (LV) dysfunction that may be pronounced in hyperparathyroid conditions. We therefore aimed to test the hypothesis that circulating markers of bone turnover correlate with LV function in a cohort of patients with primary hyperparathyroidism (pHPT). Cross-sectional data of 155 subjects with pHPT were analyzed who participated in the “Eplerenone in Primary Hyperparathyroidism” (EPATH) Trial. Multivariate linear regression analyses with LV ejection fraction (LVEF, systolic function) or peak early transmitral filling velocity (e’, diastolic function) as dependent variables and N-terminal propeptide of procollagen type 1 (P1NP), osteocalcin (OC), bone- specific alkaline phosphatase (BALP), or beta-crosslaps (CTX) as the respective independent variable were performed. Analyses were additionally adjusted for plasma parathyroid hormone, plasma calcium, age, sex, HbA1c, body mass index, mean 24-hours systolic blood pressure, smoking status, estimated glomerular filtration rate, antihypertensive treatment, osteoporosis treatment, 25-hydroxy vitamin D and N-terminal pro-brain B-type natriuretic peptide. Independent relationships were observed between P1NP and LVEF (adjusted β-coefficient = 0.201, P = 0.035) and e’ (β = 0.188, P = 0.042), respectively. OC (β = 0.192, P = 0.039) and BALP (β = 0.198, P = 0.030) were each independently related with e’. CTX showed no correlations with LVEF or e’. In conclusion, high bone formation markers were independently and paradoxically related with better LV diastolic and, partly, better systolic function, in the setting of pHPT. Potentially cardio-protective properties of stimulated bone formation in the context of hyperparathyroidism should be explored in future studies

Stau-catalyzed 6^6Li Production in Big-Bang Nucleosynthesis

If the gravitino mass is in the region from a few GeV to a few 10's GeV, the scalar lepton X such as stau is most likely the next lightest supersymmetry particle. The negatively charged and long-lived X^- may form a Coulomb bound state (A X) with a nucleus A and may affect the big-bang nucleosynthesis through catalyzed fusion process. We calculate a production cross section of Li6 from the catalyzed fusion (He4 X^-) + d \to Li6 + X^- by solving the Schr\"{o}dinger equation exactly for three-body system of He4, d, and X. We utilize the state-of-the-art coupled-channel method, which is known to be very accurate to describe other three-body systems in nuclear and atomic reactions. The importance of the use of appropriate nuclear potential and the exact treatment of the quantum tunneling in the fusion process are emphasized. We find that the astrophysical S-factor at the Gamow peak corresponding to T=10 keV is 0.038 MeV barn. This leads to the Li6 abundance from the catalyzed process as Li6|_{CBBN}\simeq 4.3\times 10^{-11} (D/2.8\times 10^{-5}) ([n_{X^-}/s]/10^{-16}) in the limit of long lifetime of X. Particle physics implication of this result is also discussed.Comment: 16 pages, 7 figure

An intrusion detection system against malicious attacks on the communication network of driverless cars

Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology and the open wireless medium makes them more vulnerable to attacks. It is important to design new approaches and mechanisms to rise the security these networks and protect them from attacks. In this paper, we design an intrusion detection mechanism for the VANETs using Artificial Neural Networks (ANNs) to detect Denial of Service (DoS) attacks. The main role of IDS is to detect the attack using a data generated from the network behavior such as a trace file. The IDSs use the features extracted from the trace file as auditable data. In this paper, we propose anomaly and misuse detection to detect the malicious attack

An Intrusion Detection System against Black Hole Attacks on the Communication Network of Self-Driving Cars

The emergence of self-driving and semi self-driving vehicles which form vehicular ad hoc networks (VANETs) has attracted much interest in recent years. However, VANETs have some characteristics that make them more vulnerable to potential attacks when compared to other networks such as wired networks. The characteristics of VANETs are: an open medium, no traditional security infrastructure, high mobility and dynamic topology. In this paper, we build an intelligent intrusion detection system (IDS) for VANETs that uses a Proportional Overlapping Scores (POS) method to reduce the number of features that are extracted from the trace file of VANET behavior and used for classification. These are relevant features that describe the normal or abnormal behavior of vehicles. The IDS uses Artificial Neural Networks (ANNs) and fuzzified data to detect black hole attacks. The IDSs use the features extracted from the trace file as auditable data to detect the attack. In this paper, we propose hybrid detection (misuse and anomaly) to detect black holes

On the detection of grey hole and rushing attacks in self-driving vehicular networks

Vehicular ad hoc networks play an important role in the success of a new class of vehicles, i.e. self-driving and semi self-driving vehicles. These networks provide safety and comfort to passengers, drivers and vehicles themselves. These vehicles depend heavily on external communication to predicate the surrounding environment through the exchange of cooperative awareness messages (CAMs) and control data. VANETs are exposed to many types of attacks such as black hole, grey hole and rushing attacks. In this paper, we present an intelligent Intrusion Detection System (IDS) which relies on anomaly detection to protect external communications from grey hole and rushing attacks. Many researchers agree that grey hole attacks in VANETs are a substantial challenge due to them having their distinct types of behaviour: normal and abnormal. These attacks try to prevent transmission between vehicles and roadside units and have a direct and negative impact on the wide acceptance of this new class of vehicles. The proposed IDS is based on features that have been extracted from a trace file generated in a network simulator. In our paper, we used a feed-forward neural network and a support vector machine for the design of the intelligent IDS. The proposed system uses only significant features extracted from the trace file. Our research, concludes that a reduction in the number of features leads to a higher detection rate and a decrease in false alarms

Prediction of DoS attacks in external communication for self-driving vehicles using a fuzzy petri net model

In this paper we propose a security system to protect external communications for self-driving and semi self-driving cars. The proposed system can detect malicious vehicles in an urban mobility scenario. The anomaly detection system is based on fuzzy petri nets (FPN) to detect packet dropping attacks in vehicular ad hoc networks. The experimental results show the proposed FPN-IDS can successfully detect DoS attacks in external communication of self-driving vehicles

Non-skeletal health effects of vitamin D supplementation: a systematic review on findings from meta-analyses summarizing trial data

Background A large number of observational studies have reported harmful effects of low 25-hydroxyvitamin D (25OHD) levels on non-skeletal outcomes. We performed a systematic quantitative review on characteristics of randomized clinical trials (RCTs) included in meta-analyses (MAs) on non-skeletal effects of vitamin D supplementation. Methods and findings We identified systematic reviews (SR) reporting summary data in terms of MAs of RCTs on selected non-skeletal outcomes. For each outcome, we summarized the results from available SRs and scrutinized included RCTs for a number of predefined characteristics. We identified 54 SRs including data from 210 RCTs. Most MAs as well as the individual RCTs reported null-findings on risk of cardiovascular diseases, type 2 diabetes, weight-loss, and malignant diseases. Beneficial effects of vitamin D supplementation was reported in 1 of 4 MAs on depression, 2 of 9 MAs on blood pressure, 3 of 7 MAs on respiratory tract infections, and 8 of 12 MAs on mortality. Most RCTs have primarily been performed to determine skeletal outcomes, whereas non-skeletal effects have been assessed as secondary outcomes. Only one-third of the RCTs had low level of 25OHD as a criterion for inclusion and a mean baseline 25OHD level below 50 nmol/L was only present in less than half of the analyses. Conclusions Published RCTs have mostly been performed in populations without low 25OHD levels. The fact that most MAs on results from RCTs did not show a beneficial effect does not disprove the hypothesis suggested by observational findings on adverse health outcomes of low 25OHD levels