The design of cryptographic mechanisms in automotive systems has been a major focus over the last ten years as the increase of cyber attacks against in-vehicle networks. The integration of these protocols into CAN bus networks is an efficient solution for leaving security level, but features of CAN bus make the performance requirements within cryptographic schemes very challenging. In the literature most of academic researches focused on designing security mechanisms for the CAN bus. Yet, very few research proposals are interested in analyzing performances requirements by using cryptographic protocols. In this paper, we investigate effects of implementing cryptographic approaches on performance by proposing an analysis methodology for implementing cryptographic approach in CAN bus communication and measuring real-time performances. Next, we propose our system which presents a tool for determining the impact of implementing of cryptographic solutions. On the other hand we have proposed an intrusion detection system using the same platform. Our tool allows the implementation of any security strategy as well as the real-time performance analysis of CAN network.
The propagation of EM waves in soil is defined by permittivity and permeability which are in turn affected by the soil parameters such as soil moisture and texture. Therefore, a suitable Dielectric Model like MBSDM is required for the channel characterization of WUSN. Effect of soil parameters and environmental conditions on signal propagation is modelled using One Ray Model and Superposition Model. The simulation of these stages is done in MATLAB for UG-UG, UG-AG and AG-UG scenarios. The system is further implemented on the ZYNQ ZC-702 hardware platform. The BER for same inter-node distance is found to be increasing in the order BPSK, BFSK, 4-PAM, QPSK and 16-QAM modulation is analysed.
Low Power Wireless Personal Area Networks (LoWPAN) based on IEEE 802.15.4 are characterized by low throughput, short communication range, nodes with limited processing power and memory. Therefore there is a need for efficient algorithms which can process and transmit IPv6 packets with available resources and can as well maintain the required QoS parameters. Towards this effort this paper presents a compression scheme to improve data throughput and thereby enhance the QoS. Further this paper also presents dynamic bandwidth allocation based on priority queue data structure which also contributes to improvement of QoS parameters.
A future mobile trend is that traffic generated by smartphones will dominate even more than it does today. Recently, smartphone traffic is expected to increase by 10 times and total mobile traffic for all devices by 8 times and more than 90 percent of mobile data traffic will come from smartphones. For this reasons mobile users will need to be actively pushed onto the 5th generation mobile (5G), builds upon today's 4G mobile network technology, which promises to offer a higher connection speeds with lower latency, or time delays. In 5G, whatever the technology used, a user association technique is needed to determine whether a user is associated with a particular base station (BS) before data transmission starts. User association plays an indispensable role in enhancing the load balancing, the spectrum efficiency, and the energy efficiency of networks. The challenge here, is to make the appropriate association that achieve the minimum required data rate for each user with acceptable complexity. In this paper, the pragmatic user association is formulated as an optimization problem, which is resolved by Nash Bargaining Solution (NBS). Simulation results show that the proposed algorithm can enable network operators to support fair resource allocation and ensure that users can be served equitably by both macro cell and pico cell. Also this paper provide an algorithm with low-complexity and reaches to near-optimal solution with a high performance guarantee
Firewalls as an approved and highly deployed security mechanism have an important role in setting up reliable security policies to ensure the protection of private and critical systems and infrastructures. While a firewall is considered as an essential node in Information Systems (IS) security and represents the backbone of security solutions, its effectiveness is highly dependent on the efficiency of its configuration and the reliability and coherence of its filtering policy. Enhancing the efficiency of access control solutions via improving the quality and capacity of firewalls attracted several researchers which led to several generations of firewall technologies. In this context, we introduce the novel concept of FW-TR firewall that integrates a trust-risk assessment approach in firewall solutions. Evaluating and involving the trust-risk associated to the filtering rules and policy in a firewall solution helps primary in: (i) strengthening the quality of the firewall filtering service; (ii) discovering firewall misconfigurations; (iii) analyzing firewall rules for anomalies detection; and (iv) changing the firewall behavior facing critical and malicious scenarios. The current paper defines a framework for organizing thinking about incorporating policies and rules trust-risk values in firewall filtering solutions that constitute what we called FW-TR: the new generation of firewalls.
Traffic classification is an essential tool for network management and security. Traditional techniques such as port-based and payload analysis are ineffective as major Internet applications use dynamic port numbers and encryption. Recent studies have used statistical properties of flows to classify traffic with high accuracy, minimising the overhead limitations associated with other schemes such as deep packet inspection (DPI). Classification accuracy of statistical flow-based approaches, however, depends on the discrimination ability of the traffic features used. To this effect, the present paper customised the popular tcptrace utility to generate classification features based on traffic burstiness and periods of inactivity (idle time) for everyday Internet usage. An attempt was made to train a C5.0 decision tree classifier using the proposed features for eleven different Internet applications, generated by ten users. Overall, the newly proposed features reported a significant level of accuracy (~98%) in classifying the respective applications.
The problem of inﬂuence maximization aims at identifying the inﬂuential nodes allowing to reach the objectives of viral marketing on social networks. The previous researches concern mainly the development of a heuristic or efﬁcient algorithms on a static social network. When a network changes, the results of various algorithms such as the detection of inﬂuential nodes must be updated. In this article, we offer a new interesting approach to resolve the problem of the inﬂuential nodes detection in evolution social networks. This approach can be considered to be an extension to our previous algorithm SND, which combines the structural and semantic aspects to identify the inﬂuential nodes in static social networks. Experimental results prove the effectiveness of SNDUPDATE in the detection of the most inﬂuential nodes which maximize the spread of inﬂuence in dynamic social networks.
Protocols for wireless sensor networks are generally designed following the layered protocol stack in which layers are independent. Uncorrelated decisions coming from diﬀerent layers may eﬀect certain metrics such as the latency of communications and/or the energy consumption. Cross-layer approaches overcome this problem by exploiting the dependencies between the layers. In this paper, we propose LEMAR-WSN (Latency and Energy MAC-aware Routing for Wireless Sensor Networks), a new cross-layer routing approach using information of the TDMA schedule and exploiting the information of the energy consumed by each node in order to optimize the latency of communications and the energy consumption when relaying information to the sink in a wireless sensor network. Simulations show that our approach improves the average latency of communications up to 20% and the average energy consumption up to 5% compared to a similar existing approach.
The operation of a sensor network raises a lot of problems and those at several levels algorithmic, location, deployment, data collection, coverage and reduction of energy consumption, its batteries, to optimize the service life network. This last point has attracted significant attention from researchers. This article reports a new method of energy optimization of a sensor node by minimizing the transmission frequencies of measured data, to the base station. The node (mote) has several sensors measuring different environmental values, our work is to categorize the data captured in predefined classes numbered beforehand that we have named "confidence intervals", so each captured value is stored in a class and only its number is sent to the base station if and only if a change of class from the previous value is observed. The results show that the collection of data by confidence interval surprisingly reduces the energy of the sensors of the motes.ENCRYPTION MODEL USING PYTHON FOR SMART METER DATA PRIVACY
The implementation of smart grids and advanced metering infrastructure have lately been on the rise in different parts of the world for several good reasons. The smart grid is a cost-efficient, energy saving system for power generation and delivery while providing excellent power reliability and quality. As the smart grid involves data transmitted over a communication network, cybersecurity of the whole system is a serious issue. A 128-bit Advanced Encryption Scheme (AES) implemented on Smart meters resulted in encrypted energy usage data that is secure and only recoverable by its intended recipient.
Android smart phone is one of the fast growing mobile phones and because of these it the one of the most preferred target of malware developer. Malware apps can penetrate the device and gain privileges in which it can perform malicious activities such reading user contact, misusing of private information such as sending SMS and can harm user by exploiting the users private data which is stored in the device. This study is about implementation of detecting malwares on android applications, which would be the basis of all future development regarding malware detection. The Main reason why the researchers came up with this study is that majority of the smartphone users worldwide are not aware of the permissions as the basis of all malicious activities that could possibly operate in an android system and may steal personal and private information. Android operating system is an open system in which users are allowed to install application from any unsafe sites. However permission mechanism of and android system is not enough to guarantee the invulnerability of the application that can harm the user. In this paper, we propose a permission scoring-based analysis that will scrutinized the installed permission and allows user to increase the efficiency of Android permission to inform user about the risk of the installed Android apps. In this paper, we propose a framework that would classify the level of sensitivity of the permission access by the application. The framework uses a formula that will calculate the sensitivity level of the permission and to determine if the installed application is malicious or not. Our result show that, in a collection of 26 malicious application, the framework is able to correctly determine the application's behavior consistently and efficiently.
Conventional Data Mining (DM) algorithms treated data simply as numbers ignoring the semantic relationships among them. Consequently, recent researches claimed that ontology is the best option to represent the domain knowledge for data mining use because of its structural format. Additionally, it is reported that ontology can facilitate different steps in the Bayesian Network (BN) construction task. To this end, this paper investigates the advantages of consolidating the Gene Ontology (GO) and the Hierarchical Bayesian Network (HBN) classifier in a flexible framework, which preserves the advantages of both, ontology and Bayesian theory. The proposed Semantically Aware Hierarchical Bayesian Network (SAHBN) is tested using data set in the biomedical domain. DNA repair genes are classified as either ageing-related or non-ageing-related based on their GO biological process terms. Furthermore, the performance of SAHBN was compared against eight conventional classification algorithms. Overall, SAHBN has outperformed existing algorithms in eight experiments out of eleven.
A new inductive SIW band pass filter designed and optimized in this article. A third order Kaband SIW band pass filter centered in 33.8 GHz with 750 MHz bandwidth, is conceived, modeled and simulated. Good agreement between the simulated HFSS and simulated results by CST is observed.
Security inside connected vehicles has gained substantial public attention in recent years. Closing existing security gaps of in-vehicle networks will be an enabling feature for connecting autonomously interacting systems in a more secure way. Economic requirements paired with growing ecological awareness demand well performing and low resource consuming solutions for security features in lowend devices. We introduce a novel approach for deriving a secret key using an efficient lightweight ciphering technique. The proposed method counteracts a known security issue in automotive device communication. By evaluating the method on a typical low-end automotive platform we could demonstrate the realistic applicability of the solution. Authenticated encryption could be achieved with 10% of the platform's resources in terms of program code size and RAM usage, reflecting the lightweight nature of the approach. This approach allows automotive communication systems to be enhanced by enabling authentication and confidentiality between the communicating devices, while at the same time meeting the high demands that automotive device suppliers are confronted with.
The classification by inductive learning finds its originality in the fact that humans often use it to resolve and to handle very complex situations in their daily lives. However, the induction in humans is often approximate rather than exact. Indeed, the human brain is able to handle imprecise, vague, uncertain and incomplete information. Also, the human brain is able to learn and to operate in a context where uncertainty management is indispensable. In this paper, we propose a Boolean model of fuzzy reasoning for indexing the monitoring sub-plans, based on characteristics of the classification by inductive learning. Several competing motivations have led us to define a Boolean model for CBR knowledge base systems. Indeed, we have not only desired experiment with a new approach to indexing of cases by fuzzy decision tree, but we also wanted to improve modelling of the vague and uncertain of the natural language concepts, optimize response time and the storage complexity.
In the following paper, applications developed under the DevOps methodology will be presented using Microsoft tools. This will allow a contribution to the academic community with different applications that will optimize the work of Virtual Companies through the online development of the different activities in which these companies are involved.
The usage of Augmented Reality (AR) in industrial and modern manufacturing are more and more growing since the fourth industrial revolution. Using AR boost the digitization of the industry production lines, gain time and money and improve maintenance tasks as well as the human-machine interaction. This paper is a literature review of the use of AR in industries including the use cases in different type of application such as: design, simulation, maintenance, remote assistance, human-robot interaction and robot programming.
Routing Protocol for Low Power and Lossy Networks (RPL) is one of the most utilized routing protocols. It designed to adapt with thousands of nodes in energy-constrained networks. It is a proactive distance vector protocol which has two major components objective function and trickle algorithm. Our work focus on the trickle timer algorithm, it is used to control, maintain and follow the control messages over the network. Short listen problem is the main blot in trickle algorithm. Several studies focused on enlarging the listen period. However, as it was suffering from node starvation when the period is short, it suffers from time and energy wasting when the period is enlarged. Notice that the time and power consumption are sensitive factors in Low Power and Lossy Networks. In this paper, we propose a randomized dynamic trickle algorithm, it contributes in the improvement of trickle and solving the above-mentioned problems by controlling the t variable in a dynamic randomly way, where t is the border line between listening and transmitting period. The performance of the proposed algorithm is validated through extensive simulation experiments under different scenarios and operation conditions using Cooja 2.7 simulator. Simulation results compared with the standard trickle timer algorithm based on convergence time, packet delivery ratio (PDR) and power consumption performance metrics. The results of the simulations denote a high improvement in term of convergence time, power consumption and packet delivery ratio.
MANET (Mobile Ad-hoc Network) is simply a set of mobile hosts connected wirelessly with no centralized management, with each node acts as a packet sender, packet receiver, and a router at the same time. The nature of this network means that the dynamic topology and the absence of a centralized management cause several security issues and attacks that occur in this kind of networks, such as the black hole attack, the wormhole attack and the impersonation and repudiation attack. In this survey, we are going to introduce the Black Hole attack security issues and some of the detection techniques used to detect the black hole attack. In this kind of attack (black hole attack) the intruders manipulate the network normal behavior, by introducing themselves as the node with the shortest path to the destination and use this way to do malicious behavior over the network.
The virtual Automatic Identification System (AIS) generation management system is a system for analyzing waterway risk by generating virtual AIS data which contains location information of a specific area. The system uses the data as an input data of the IALA Waterway Risk Assessment (IWRAP).
Applications for intelligent transportation utilize the Global Positioning Systems (GPS) signals on the Vehicular Adhoc Networks (VANETs) to improve road safety, traffic management and transportation system efficiency. However, Vehicular Nodes (VNs) using those applications may face the deterioration or complete loss of GPS signals due to many reasons such as changes in node velocity and/or positioning, dense foliage, compact high buildings and/or distance between vehicular nodes. Although several solutions have been developed to solve this issue through internal communication with surrounding vehicle nodes (i.e., beacons), the VNs remain to suffer the poor positioning accuracy or errors in localization estimation. In this paper, an Extended Self-Correcting Localization (ESCL-VNET) algorithm is developed to enhance the positioning accuracy and improve the localization estimation of a given VN over the distributed VANET. The technique integrates the use of Received Signal Strength Indication (RSSI) technique to enable the VNs to estimate their locations and the use of Signal to Interference Noise Ratio (SINR) values obtained through the Dedicated Short-Range Communications (DSRC) messaging by the other VNs to weight the localizations using the Weighted Centroid Localization (WCL) process. A simulation program was developed to conduct performance evaluation of the ESCL-VNET algorithm and to assess the given results. The simulation shows that the new ESCL-VNET algorithm can generate a more accurate position estimation or localization than in the case of the standard SCL-VNET. The ESCL-VNET algorithm may contribute to the development of better and more efficient localization applications as part of robust Intelligent Transportation System (ITS).
Single-Board Computers (SBC) refer to pocket-sized computers built on a single circuit board. A number of studies have explored the use of these highly popular devices in a variety of domains, including military, agriculture, healthcare, and more. However, no attempt was made to signify possible security risks that misuse of these devices may bring to organizations. In this study, we perform a series of experiments to validate the possibility of using SBCs as an espionage gadget. We show how an attacker can turn a Raspberry Pi device to an attacking gadget and benefit from short-term physical access to attach the gadget to the network in order to access unauthorized data or perform other malicious activities. We then provide experimental results of placing such tools in two real-world networks. Given the small size of SBCs, traditional physical security measures deployed in organizations may not be sufficient to detect and restrict the entrance of SBCs to their premises. Therefore, we reiterate possible directions for network administrators to deploy defensive mechanisms for detecting and preventing such attacks.
Cyber Physical Systems (CPS), like IoT and industrial control systems, are typically vulnerable to cyber threats due to a lack of cyber security measures and hard change management. Security monitoring is aimed at improving the situational awareness and the resilience to cyber attacks. Solutions tailored to CPS are required for greater effectiveness. This work proposes a monitoring framework that leverages the knowledge of the system to monitor in order to specify, check, and predict known critical conditions. This approach is particularly suitable to CPS, as they are designed for a precise purpose, well documented, and predictable to a good extent. The framework uses a formal logical language to specify quantitative critical conditions and an optimisation SMT-based engine that checks observable aspects from network traffic and logs. The framework computes a quantitative measure of the criticality of the current CPS system: checking how criticality changes in time enables to predict whether the system is approaching to a critical condition or reaching back a licit state. An important novelty of the approach is the capability of expressing conditions on the time of the observations and of dealing with unobservable variables. This work presents the formal framework, a prototype, a testbed, and first experimental results that validate the feasibility of the approach.
This paper presents an optical image encryption system based completely on amplitude modulation, phase modulation in the discrete Fourier transform and modified chaotic logistic map. Amplitude modulation and phase modulation are accomplished by the use of spatial light modulator (SLM). SLMs are normally used to control incident light in amplitude-best, phase-best or the mixture (amplitude-phase). The random amplitude modulation based on chaotic Baker map is carried out in time domain, while the random phase modulation is accomplished in the frequency domain. In this paper, we proposed a technique to regulate and enhance protection in chaotic logistic map method leading to increased variety of key space of the logistic map. This causes our encryption system to become extra sturdy against brute pressure. An exhaustive analysis of the proposed encryption system is undergone and shows positive results in encryption metrics when compared to several different photo encryption techniques. The analysis demonstrates the highly valued security and immunity to noise of the photograph encryption. The proposed modified logistic map with amplitude and phase modulation is suitable for real-time application.
Network anomaly detection, as an important means of network supervision, is of great significance for ensuring the reliable operation of the network.This paper proposes a network anomaly detectionalgorithm based on improved MSPCA.The main idea is to control the scale of the PCA filter by introducing the energy contribution efficiency ECE,and then filtering wavelet coefficient matrix with Bayesian PCA.The algorithm overcomes the shortcomings of the traditional MSPCA algorithm, such as high time complexity and difficulty in parameter selection.It is possible to more effectively separate abnormal data in the network.The experimental results show that compared with other detection algorithms, the improved MSPCA detection algorithm has achieved good detection results.
Network coding is a technique for maximizing the use of available bandwidth capacity. We are interested in applying network coding to multimedia content distribution. This is desirable because many popular network applications for content distribution consume high bandwidth and international bandwidth; both are scarce in countries such as New Zealand. Existing work has addressed the use of network coding for content distribution, however work on network coding and security does not consider the trade-off between quality of service and security for multimedia. Network coding is vulnerable to a pollution attack or a packet modification attack. It has detrimental effect particularly on network coding because of specific characteristic of network coding that allows nodes to modify received packets at any time. Many pollution attack defence mechanisms use computationally expensive techniques leading to higher communication cost. Therefore, the focus of this work is on developing protocols to address both open problems and validate the protocols using a combination of formal and simulation techniques. More importantly, our novel contribution is reduction of complexity of algorithms appropriate for streaming content distribution with network coding.
For 3D microstructures fabricated by two-photon polymerization an effective approach of machine learning for detection and classification in their optical microscopic images is state and demonstrated in this paper. It is based on Faster R-CNN, Multi-label classification (MLC) and Residual learning framework Algorithms for reliable, automated detection and accurate classification of Two Photo Polymerization (TPP) microstructures. From finding and detecting the microstructures from a different location in the microscope slide, matching different shapes of the microstructures classify them among their categories is fully automated. The results are compared with manual examination and SEM images of the microstructures for the accuracy test.Some modifications of ordinary optical Microscope so as to make it automated and by applying Deep learning and Image processing algorithms we can successfully detect, label and classify 3D microstructures, designing the neural network model for each phase and by training them using the datasets that we have made, the dataset is a set different images from different angles and their annotation we can achieve High accuracy. The accurate microstructure detection technique in the combination of image processing and computer vision help to simulate the values of each pixel and classify the Microstructures.