Trustee: A Trust Management System for Fog-enabled Cyber Physical Systems

In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, badmouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasures the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e. CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system

Depthwise Separable Convolutional Neural Networks for Pedestrian Attribute Recognition

Video surveillance is ubiquitous. In addition to understanding various scene objects, extracting human visual attributes from the scene has attracted tremendous traction over the past many years. This is a challenging problem even for human observers. This is a multi-label problem, i.e., a subject in a scene can have multiple attributes that we are hoping to recognize, such as shoes types, clothing type, wearing some accessory, or carrying some object or not, etc. Solutions have been presented over the years and many researchers have employed convolutional neural networks (CNNs). In this work, we propose using Depthwise Separable Convolution Neural Network (DS-CNN) to solve the pedestrian attribute recognition problem. The network employs depthwise separable convolution layers (DSCL), instead of the regular 2D convolution layers. DS-CNN performs extremely well, especially with smaller datasets. In addition, with a compact network, DS-CNN reduces the number of trainable parameters while making learning efficient. We evaluated our method on two benchmark pedestrian datasets and results show improvements over the state of the art

A Lightweight Attribute-based Security Scheme for Fog-Enabled Cyber Physical Systems

In this paper, a lightweight attribute-based security scheme based on elliptic curve cryptography (ECC) is proposed for fog-enabled cyber physical systems (Fog-CPS). A novel aspect of the proposed scheme is that the communication between Fog-CPS entities is secure even when the certification authority (CA) is compromised. This is achieved by dividing the attributes into two sets, namely, secret and shared, and subsequently generating two key pairs, referred to as the partial and final key pairs, for each entity of the Fog-CPS system. Unlike existing attribute-based encryption (ABE) and identity-based encryption schemes, in the proposed scheme, each entity calculates the final public key of the communicating CPS devices without the need of generating and transmitting digital certificates. Moreover, the proposed security scheme considers an efficient and secure key pair update approach in which the calculation overhead is limited to one group element. To show the effectiveness of the proposed scheme, we have calculated and compared the memory and processing complexity with other bilinear and elliptic curve schemes. We have also implemented our scheme in a Raspberry Pi (3B+ model) for CPS simulations. The proposed scheme guarantees the confidentiality, integrity, privacy, and authenticity in Fog-CPS systems

Privacy Preserving Attribute Based Encryption for Multiple Cloud Collaborative Environment

In a Multiple Cloud Collaborative Environment (MCCE), cloud users and cloud providers interact with each other via a brokering service to request and provision cloud services. The brokering service considers several pieces of data to broker the best deal between users and providers which can subsequently risks the privacy and security of MCCE. In this paper, we propose a Privacy Preserving Attribute-Based Encryption(PPABE) scheme which protects MCCE from a compromised broker. The proposed encryption scheme preserves the privacy by employing data access policy over sets of attributes. The identifying attributes are anonymoized using pseudonyms. The data access policy is further anonymized so as it remain unknown to unauthorized parties. The PP-ABE achieves unlinkability between different data items which flows through the collaborative cloud environment and preserves the privacy of cloud users and cloud providers

A multi-branch separable convolution neural network for pedestrian attribute recognition

© 2020 The Authors Computer science; Computer Vision; Image processing; Deep learning; Pedestrian attribute recognitio

A Secure Integrated Framework for Fog-Assisted Internet of Things Systems

Fog-Assisted Internet of Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this paper proposes a framework featuring attribute-based access control and trust-based behavioural monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities’ performance using a trust model based on a set of QoS and network communication features. Subsequently, trust is embedded as an attribute within SC’s access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely DoS, DDoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework’s design principles. The framework is evaluated on a Raspberry Pi 3 Model B to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices

Crowd Modeling using Temporal Association Rules

Understanding crowd behavior has attracted tremendous attention from researchers over the years. In this work, we propose an unsupervised approach for crowd scene modeling and anomaly detection using association rules mining. Using object tracklets, we identify events occurring in the scene, demonstrated by the paths or routes objects take while traversing the scene. Allen\u27s interval-based temporal logic is used to extract frequent temporal patterns from the scene. Temporal association rules are generated from these frequent temporal patterns. Our goal is to understand the scene grammar, which is encoded in both the spatial and spatio-temporal patterns. We perform anomaly detection and test the method on a well-known public data

Reduced loss of NH3 by coating urea with biodegradable polymers, palm stearin and selected micronutrients

In agricultural lands, the loss of NH3 from surface-applied urea and micronutrient deficiencies are the two most common problems, which can be solved by using coated urea with micronutrients and biodegradable natural materials. These coatings can improve the nutrient status in the soil and simultaneously reduce nitrogen loss from urea. To control ammonia loss and urea’s hydrolysis process, two laboratory studies were conducted to compare the effects of using coated urea with that of using only urea. Both studies consisted of consecutive incubation experiments that were conducted on the same Typic Paleudult soil (Serdang Series). Eight treatments (labeled as Urea, UPS1, UPS2, UPS3, UAG1%, UAG2%, UG1% and UG2%) in study 1 and six treatments (labeled U, UPSCu, UAGCu, UGCu, UCu, and UCuZn) in study 2 were prepared and used to determine the effects of various concentrations of natural materials and the inhibitory effects of micronutrients on both ammonia loss and the hydrolysis process. The NH3 loss was measured by forced draft techniques; the soil’s exchangeable ammonium, available nitrate and urea-N were determined by using standard procedures. The outcomes of the study did not show any significant difference among various concentrations ofnatural material. Coated urea treatments significantly reduced ammonia loss by 30 to 40% in study 1 and by 40 to 67% in study 2 in comparison to urea alone. The same observation was made with respect to urea hydrolysis. All of the coated urea treatments significantly slowed down the hydrolysis process in comparison to urea. The outcomes of the study may improve urea fertilizer by reducing the loss of NH3 volatilization.Key words: Biodegradable polymers, coated urea, CuSO4, NH3 volatilization loss, urease inhibitor, urea