Local Detection Mechanisms Research Articles

A misbehavior detection framework for cooperative intelligent transport systems

With changing times, the need for security increases in all fields, whether we talk about cloud networks or vehicular networks. In every place, it has its importance, but in vehicular networks where the lives of human beings are involved, security becomes the topmost priority. Therefore, this article aims to shed light on Misbehavior Detection Framework (MDF) used in the Cooperative Intelligent Transport Systems community. Here, MDF keeps an eye on malicious entities on the roads. It is done by regularly evaluating two main checks: consistency and local plausibility. These checks are done by Intelligent Transport System Stations. All the messages received through Vehicle-to-Everything are scrutinized through this model. After that, all the messages are evaluated by local detection mechanisms to decide the holistic message’s plausibility. This article mainly focuses on the logic behind the proposed Misbehavior Detection Framework providing more security, evaluating various Machine Learning-based models to ensure one best out of all based on quality and computation latency of all models along with the results of various parameters, such as Recall, Precision, F1 Score, Accuracy, Bookmaker Informedness, Markedness, Mathews Correlation Coefficient, Kappa, and achieved the best results.

A novel multimodal multiobjective memetic algorithm with a local detection mechanism and a clustering-based selection strategy

A novel multimodal multiobjective memetic algorithm with a local detection mechanism and a clustering-based selection strategy

Anomaly Detection Based Secure In-Network Aggregation for Wireless Sensor Networks

Secure in-network aggregation in wireless sensor networks (WSNs) is a necessary and challenging task. In this paper, we first propose integration of system monitoring modules and intrusion detection modules in the context of WSNs. We propose an extended Kalman filter (EKF) based mechanism to detect false injected data. Specifically, by monitoring behaviors of its neighbors and using EKF to predict their future states (actual in-network aggregated values), each node aims at setting up a normal range of the neighbors' future transmitted aggregated values. This task is challenging because of potential high packet loss rate, harsh environment, and sensing uncertainty. We illustrate how to use EKF to address this challenge to create effective local detection mechanisms. Using different aggregation functions (average, sum, max, and min), we present how to obtain a theoretical threshold. We further apply an algorithm of combining cumulative summation and generalized likelihood ratio to increase detection sensitivity. To overcome the limitations of local detection mechanisms, we illustrate how our proposed local detection approaches work together with the system monitoring module to differentiate between malicious events and emergency events. We conduct experiments and simulations to evaluate local detection mechanisms under different aggregation functions.

Spatial summation of face information

Do all parts of the face contribute equally to face detection or are some parts more detectable than others? To evaluate this issue, we studied detection of the presence of normalized frontal-face images within aperture windows of varying extent. We performed a face summation study using two-alternative forced-choice psychophysics. The face stimuli were scaled to equal eye-to-chin distance, centered on the bridge of the nose, and windowed by fourth-power Gaussian envelopes of various sizes. The faces were intermixed with control stimuli consisting inverted faces to test for configuration effects, split-half inverted faces to perturb the symmetry, and phase-scrambled versions of the faces with equal Fourier energy. Face detectability improved rapidly at first, then at a progressively shallower rate for larger window sizes, in a similar fashion for the three face-based stimulus types. The spectrally equated noise stimuli were less detectable than the face stimuli for all except the smallest apertures. The results were fit with a model incorporating global face-specific and local nonspecific spatial integration mechanisms. Detection of the noise images was consistent with local detection mechanisms accessed through a wide-field attention mechanism. The data for face detection implied detection mechanisms that integrated linearly up to some small size, integrated more slowly up to an intermediate size, and failed to gain any improvement for information beyond some larger size. This performance supports the concept of a specialized face configuration mechanism operating at detection threshold, similar in extent among the observers.