Smart vehicle parking is a collaborative effort of technology and human innovation where the efforts are to be minimized to save time and efforts. In smart cities it is one of the common challenges to introduce smart parking to increase parking efficiency and combat numerous issues like identification of free parking slot and real-time dynamic updation on traffic to save fuel and energy. In this work, a new cloud-based smart parking architecture is proposed that can help in predicting the available free parking slots in smart cities. Initially, the methodology collects the car count at any near by parking using Internet of Things (IoT) and Cloud-based approach. Later, the approach uses the Kernel Least Mean Square algorithm to make heuristic predictions about future vacancy using auto-regression. The proposed approach thus utilizes the online learning or model training. To validate the efficacy of the proposed work, the testing is done on the real-time dataset. The extensive numerical investigation is performed on parking lots of four international airports of a smart city in actual deployment scenarios. The experimentation has revealed superior performance of the method in terms of vacancy prediction.

The Internet of Medical Things are susceptible to Man-in-the-Middle (MitM) attack, which can identify healthcare emergency of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this article, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the raise of alarms by the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from received signal strength indication. Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%.

The use of Wireless Body Area Networks (WBANs) in healthcare for pervasive monitoring enhances the lives of patients and allows them to fulfill their daily life activities while being monitored. Various non-invasive sensors are placed on the skin to monitor several physiological attributes, and the measured data are transmitted wirelessly to a centralized processing unit to detect changes in the health of the monitored patient. However, the transferred data are vulnerable to various sources of interference, sensor faults, measurement faults, injection and alteration by malicious attackers, etc. In this article, we propose a change point detection model based on a Markov chain for centralized anomaly detection in WBANs. The model is derived from the Root Mean Square Error (RMSE) between the forecasted and measured values for whole attributes. The RMSE transforms the monitored attributes into a univariate times series which is divided into overlapping sliding window. The joint probability of the sequence of RMSE values in each sliding window is calculated to decide whether a change has occurred or not. When an effective change is detected over k consecutive windows, the number of deviated attributes is used to distinguish faulty measurements from a health emergency. We apply our proposed approach on real physiological data from the Physionet database and compare it with existing approaches. Our experimental results prove the effectiveness of our proposed approach, as it achieves high detection accuracy with a low false alarm rate (5.2%).

Rapidly increasing capacities, decreasing costs, and improvements in computational power, storage, and communication technologies have led to the development of many applications that carry increasingly large amounts of traffic on the global networking infrastructure. Smart devices lead to emerging technologies and play a vital role in rapid evolution. Smart devices have become a primary 24/7 need in today’s information technology world and include a wide range of supporting processing-intensive applications. Extensive use of many applications on smart devices results in increasing complexity of mobile software applications and consumption of resources at a massive level, including smart device battery power, processor, and RAM, and hinders their normal operation. Appropriate resource utilization and energy efficiency are fundamental considerations for smart devices because limited resources are sporadic and make it more difficult for users to complete their tasks. In this study we propose the model of mobile energy augmentation using cloud computing (MEACC), a new framework to address the challenges of massive power consumption and inefficient resource utilization in smart devices. MEACC efficiently filters the applications to be executed on a smart device or offloaded to the cloud. Moreover, MEACC efficiently calculates the total execution cost on both the mobile and cloud sides including communication costs for any application to be offloaded. In addition, resources are monitored before making the decision to offload the application. MEACC is a promising model for load balancing and power consumption reduction in emerging mobile computing environments.

In this paper, we introduce a translation tool that translates any sequential C++ source code into parallel source code written in C++ and OpenACC programming model. The tool generates different types of dependency graphs: class, method, loop, and block-of-statements graphs. The class and method dependency graphs are created for the sequential code, and the loop and block-of-statements dependency graphs are created for each method. From the class dependency graph, the dependency analyser identifies the independent classes, where the objects of the independent classes can run in parallel, and from the method dependency graph, the method analyser detects the methods that can run in parallel. For each block, the analyser detects the statements that run in parallel, where there is no data dependency between statements, and the loop analyser detects the type of parallelism in the loop-block: parallel statements, pipeline, or data partitions.

This paper presents a lightweight approach for the early detection of nocturnal epileptic seizures through analysis of inertial data and muscle contractions. Our approach uses an overlapping sliding window to derive the variance of data acquired by the MPU 9,250 motion tracking device and single channel surface ElectroMyoGram (sEMG). The Exponentially Weighted Moving Average (EWMA) is used to forecast the current value of the data variance. When the Kullback-Leibler divergence between the forecasted and measured variances deviates from past values, a signal is transmitted to the base station to set the current counter in an alarm window. If the filling ratio of the alarm window is greater than a predefined threshold, an alarm is triggered by the base station. The proposed approach is intended to improve the performance of existing detection systems based on data analysis from Accelerometer. The MPU 9,250 is 9-axis motion tracking and used to detect motor seizures, and it contains a 3-axis Accelerometer, Gyroscope, and Magnetometer. The sEMG is used to detect silent seizures without jerky movements. Our experimental results on a real dataset from an epileptic patient show that our proposed approach is able to increase detection accuracy and reduce the low false alarm rate. Comparison with a Probability Density Function (PDF) further demonstrates the detection efficiency of our approach.

Injection attacks remain yet one of the major challenges in non-relational data stores or NoSQL databases. Indeed, such databases are intended to store big data and are classified into four categories; Key-values Stores, Wide Column Stores, Document Stores, and Graph Databases. The different vulnerabilities of these NoSQL databases have attracted many researchers to attempt solving or mitigating this problem. Unfortunately, extensive experiments have revealed that all proposed approaches and techniques are away from the expectations. This is due mainly to their focusing only either on some parts of the problem or on a specific NoSQL engine. In this paper, we propose an open tool architecture which can take into consideration any NoSQL engines belonging to the four data stores categories whatever the programming language used. The proposed tool architecture is able to detect first vulnerable statements in the static mode on the developer side. Second, it detects also automatically injection attacks during run-time on the server side thanks to the added instrumenting statements during the first control (static mode). The easy expansion and adaptation of the proposed tool to any NoSQL engine and/or any kind of attacks and/or programming languages makes it very attractive compared the existing ones. Indeed nowadays, we observe the emergence of new kinds of attacks once a new security approach or framework or technique is proposed.

Intrusion Detection and/or Prevention Systems (IDPSs) are now a crucial defensive measure to defend against attacks intended to breach the security and operation of enterprise information systems. The IDPS configuration can, however, have a negative impact on network performance in terms of end-to-end delay and packet loss. This paper proposes an analytical queuing model based on the embedded Markov chain which analyzes the performance of the IDPS and evaluates its impact on performance. Through extensive simulations, we validate the proposed model and the numerical equations that estimate various performance metrics. Our results show that this model can be leveraged to assess and set up an effective configuration for the IDPS, achieving simultaneously the trade-off between security enforcement levels on one side and network Quality of Service (QoS) requirements on the other.

Protection and performance are the major requirements for any Intrusion Detection and/or Prevention System (IDPS). Existing IDPSs do not seem to provide a satisfactory method of achieving these two conflicting goals. Intrusion Detection Systems (IDSs) fulfill the network performance requirement but exhibit poor protection under successive attacks. On the other hand, Intrusion Prevention Systems (IPSs) can protect the network by dropping the malicious packets that match any attacking pattern; however, this can have a negative impact on network performance in terms of delay as the attacking patterns increase. This results in a tradeoff between security enforcement levels on one hand and the performance and usability of an enterprise information system on the other. This paper aims to study the impact of security enforcement levels on the performance and usability of an enterprise information system. We propose a rule mode selection optimization technique that aims to determine an appropriate IDPS configuration set in order to maximize the security enforcement levels while avoiding any unnecessary network performance degradation. Simulation was conducted to validate our proposed technique. The results demonstrate that it is desirable to strike a balance between system security and network performance.

Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defense against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Although many IDPS systems have been proposed, their appropriate configuration and control for effective attacks detection/prevention and efficient resources consumption has always been challenging. The evaluation of the IDPS performance for any given security configuration is a crucial step for improving real-time capability. This paper aims to analyze the impact of security enforcement levels on the performance and usability of an enterprise information system. We develop a new analytical model to investigate the relationship between the IDPS performance and the rules mode selection. In particular, we analyze the IDPS rule-checking process along with its consequent action (i.e., alert or drop) on the resulting security of the network, and on the average service time per event. Simulation was conducted to validate our performance analysis study. Our results show that applying different sets of rules categories and configuration parameters impacts average service time and affects system security. The results demonstrate that it is desirable to strike a balance between system security and network performance.

Intrusion detection and/or prevention systems (IDPS) represent an important line of defense against the variety of attacks that can compromise the security and well functioning of an enterprise information system. IDPSes can be network or host-based and can collaborate in order to provide better detections of malicious traffic. Although several IDPS systems have been proposed, their appropriate configuration and control for effective detection and prevention of attacks has always been far from trivial. Another concern is related to the slowing down of system performance when maximum security is applied, hence the need to trade off between security enforcement levels and the performance and usability of an enterprise information system. In this paper we motivate the need for and present a policy-based framework for the configuration and control of the security enforcement mechanisms of an enterprise information system. The approach is based on dynamic adaptation of security measures based on the assessment of system vulnerability and threat prediction and provides several levels of attack containment. As an application, we have implemented a dynamic policy-based adaptation mechanism between the Snort signature-based IDPS and the light weight anomaly-based FireCollaborator IDS. Experiments conducted over the DARPA 2000 and 1999 intrusion detection evaluation datasets show the viability of our framework.

Intrusion Detection Systems (IDSs) are designed to monitor user and/or network activity and generate alerts whenever abnormal activities are detected. The number of these alerts can be very large; making the task of security analysts difficult to manage. Furthermore, IDS alert management techniques, such as clustering and correlation, suffer from involving unrelated alerts in their processes and consequently provide imprecise results. In this paper, we propose a fuzzy-logic based technique for scoring and prioritizing alerts generated by an IDS(1). In addition, we present an alert rescoring technique that leads to a further reduction of the number of alerts. The approach is validated using the 2000 DARPA intrusion detection scenario specific datasets and comparative results between the Snort IDS alert scoring and our scoring and prioritization scheme are presented.