Livestock: A Reliable Source of Income Generation and Rehabilitation of Environment at Tharparkar

This paper attempts to identify the farming and growth rate of livestock and demographic conditions helping in its growth and focus is specially to examine: (i) to know the trend of growth of performance of livestock farming; (ii) to promote fencing of farmland and conservation of rangeland for fodder (iii) to find the new topics for further research. Hypothesis given bellow are tested in the light of above objectives: (i). it is hypothysed that livestock farming is reliable source of income generation; (ii). it is also hypothysed that reforming of farmland and rangeland will provide abundant fodder and will prove sustainable source of income generation and rehabilitation of environment. Two alternatives hypothesis are also set: (i). livestock farming is not reliable source of income generation, if properly managed too. (ii) reforming of farmland and rangeland will not provide abundant fodder and will prove sustainable source of income generation and rehabilitation of environment. The study reveals that the important component of agriculture sector is livestock and is an insurance against harvest failures and a source of easily cashable investment capital. It has more than 22 percentage of share of whole province’s livestock. Agriculture dependent families are 81 percent and 92 percent families have opinion that livestock is the only first level sustainable source of livelihood in Tharparkar and needs more attention of researchers to evaluate it.Livestock, Trends, Comparison, Tharparkar, Growth rate, Rehabilitation, Reforming

Why do patients with limb ischaemia present late to a vascular surgeon? A prospective cohort study from the developing world

OBJECTIVE: To look into the factors responsible for delay in presentation of Iimb ischemia patients to a vascular surgeon. METHODS: The prospective cohort study was conducted at the Aga Khan University Hospital, Karachi, from October 01, 2016, to August 10, 2018. Patients coming with delayed presentation of both acute and chronic limb ischemia were included. All the patients were assessed by qualified vascular surgeons. SPSS 23 was used for data analysis. RESULTS: Of the 55 patients, 33(60%) had acute and 22(40%) had chronic limb ischaemia. Mean age of acute cases was 44±23.72 years and it was 60±12.49 years for chronic cases. Overall, the commonest reason behind delay was non-referral by primary physician which was the case with 11(33.3%) patients in the acute group, and 13(59%) in the chronic group. The limb loss in the acute group was 20(60%) and 8(36%) in the chronic group.. CONCLUSION: Delayed presentation of patients with limb ischaemia is mainly due to non-referral. A robust campaign needs to be launched to reduce the rate of limb loss

Security and privacy for IoT and fog computing paradigm

In the past decade, the revolution in miniaturization (microprocessors, batteries, cameras etc.) and manufacturing of new type of sensors resulted in a new regime of applications based on smart objects called IoT. Majority of such applications or services are to ease human life and/or to setup efficient processes in automated environments. However, this convenience is coming up with new challenges related to data security and human privacy. The objects in IoT are resource constrained devices and cannot implement a fool-proof security framework. These end devices work like eyes and ears to interact with the physical world and collect data for analytics to make expedient decisions. The storage and analysis of the collected data is done remotely using cloud computing. The transfer of data from IoT to the computing clouds can introduce privacy issues and network delays. Some applications need a real-time decision and cannot tolerate the delays and jitters in the network. Here, edge computing or fog computing plays its role to settle down the mentioned issues by providing cloud-like facilities near the end devices. In this paper, we discuss IoT, fog computing, the relationship between IoT and fog computing, their security issues and solutions by different researchers. We summarize attack surface related to each layer of this paradigm which will help to propose new security solutions to escalate it acceptability among end users. We also propose a risk-based trust management model for smart healthcare environment to cope with security and privacy-related issues in this highly un-predictable heterogeneous ecosystem

Energy consumption analysis of reputation-based trust management schemes of wireless sensor networks

Energy consumption is one of the most important parame-ters for evaluation of a scheme proposed for WSNs because of their resource constraint nature. Comprehensive compar-ative analysis of proposed reputation-based trust manage-ment schemes of WSNs from this perspective is currently not available in the literature. In this paper, we have filled this gap by first proposing Generic Communication Proto-col (GCP) that is used to exchange trust values. Based on this proposed GCP protocol, we have presented a theo-retical energy consumption analysis and evaluation of three state-of-the-art reputation-based trust management schemes of WSNs

Intrusion-aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks

Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.Comment: 19 pages, 7 figure

A hybrid dual-mode trust management scheme for vehicular networks

Vehicular ad-hoc networks allow vehicles to exchange messages pertaining to safety and road efficiency. Building trust between nodes can, therefore, protect vehicular ad-hoc networks from malicious nodes and eliminate fake messages. Although there are several trust models already exist, many schemes suffer from varied limitations. For example, many schemes rely on information provided by other peers or central authorities, for example, roadside units and reputation management centers to ensure message reliability and build nodes’ reputation. Also, none of the proposed schemes operate in different environments, for example, urban and rural. To overcome these limitations, we propose a novel trust management scheme for self-organized vehicular ad-hoc networks. The scheme is based on a crediting technique and does not rely on other peers or central authorities which distinguishes it as an economical solution. Moreover, it is hybrid, in the sense it is data-based and entity-based which makes it capable of revoking malicious nodes and discarding fake messages. Furthermore, it operates in a dual-mode (urban and rural). The simulation has been performed utilizing Veins, an open-source framework along with OMNeT++, a network simulator, and SUMO, a traffic simulator. The scheme has been tested with two trust models (urban and rural). The simulation results prove the performance and security efficacy of the proposed scheme

TQ-Model: A New Evaluation Model for Knowledge-Based Authentication Schemes

Many user authentication schemes are developed to resolve security issues of traditional textual password scheme. However, only Android unlock scheme gets wide acceptance among users in the domain of smartphones. Although Android unlock scheme has many security issues, it is widely used due to usability advantages. Different models and frameworks are developed for evaluating the performance of user authentication schemes. However, most of the existing frameworks provide ambiguous process of evaluation, and their results do not reflect how much an authentication scheme is strong or weak with respect to traditional textual password scheme. In this research paper, an evaluation model called textual passwords-based quantification model (TQ-Model) is proposed for knowledge-based authentication schemes. In the TQ-Model, evaluation is done on the basis of different features, which are related to security, usability and memorability. An evaluator needs to assign a score to each of the feature based on some criteria defined in the model. From the evaluation result, the performance difference between a knowledge-based authentication scheme and textual password scheme can be measured. Furthermore, evaluation results of Android unlock scheme, picture gesture authentication scheme and Passface scheme are presented in the paper using the TQ-Model

A DDoS attack mitigation framework for IoT networks using fog computing

The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks

Inconsistency Detection Method for Access Control Policies

Abstract-In enterprise environments, the task of assigning access control rights to subjects for resources is not trivial. Because of their complexity, distribution and size, access control policies can contain anomalies such as inconsistencies, which can result in security vulnerabilities. A set of access control policies is inconsistent when, for specific situations different incompatible policies can apply. Many researchers have tried to address the problem of inconsistency using methods based on formal logic. However, this approach is difficult to implement and inefficient for large policy sets. Therefore, in this paper, we propose a simple, efficient and practical solution for detecting inconsistencies in access control policies with the help of a modified C4.5 data classification algorithm

An anomaly mitigation framework for IoT using fog computing

The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates