Biblio

The paper introduces and develops the new concept of current-mode multifunctional circuit, a computational structure that is able to implement, using the same functional core, a multitude of circuit functions: amplifying, squaring, square-rooting, multiplying, exponentiation or generation of any continuous mathematical function. As a single core computes a large number of circuit functions, the original approach of analog signal processing from the perspective of multifunctional structures presents the important advantages of a much smaller power consumption and design costs per implemented function comparing with classical designs. The current-mode operation, associated with the original concrete implementation of the proposed structure increase the accuracy of computed functions and the frequency behaviour of the designed circuit. Additionally, the temperature-caused errors are almost removed by specific design techniques. It will be also shown a new method for third-order approximating the exponential function using an original approximation function. A generalization of this method will represent the functional basis for realizing an improved accuracy function synthesizer circuit with a simple implementation in CMOS technology. The proposed circuits are compatible with low-power low voltage operations.

Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing machine learning methods such as Innovative Decision Tree (DT) with Support Vector Machine (SVM). By comparing the Decision Tree (N=20) and the Support Vector Machine algorithm (N=20) two classes of machine learning classifiers were used to determine the accuracy. The decision Tree (99.19%) has the highest accuracy than the SVM (98.5615%) and the independent T-test was carried out (=.507) and shows that it is statistically insignificant (p\textgreater0.05) with a confidence value of 95%. by comparing Innovative Decision Tree and Support Vector Machine. The Decision Tree is more productive than the Support Vector Machine for recognizing intruders with substantially checked, according to the significant analysis.

Improving the accuracy of intruders in innovative Intrusion detection by comparing Machine Learning classifiers such as Random Forest (RF) with Support Vector Machine (SVM). Two groups of supervised Machine Learning algorithms acquire perfection by looking at the Random Forest calculation (N=20) with the Support Vector Machine calculation (N=20)G power value is 0.8. Random Forest (99.3198%) has the highest accuracy than the SVM (9S.56l5%) and the independent T-test was carried out (=0.507) and shows that it is statistically insignificant (p \textgreater0.05) with a confidence value of 95% by comparing RF and SVM. Conclusion: The comparative examination displays that the Random Forest is more productive than the Support Vector Machine for identifying the intruders are significantly tested.

We present a collected overview on how to assess both the accuracy and reliability levels and relate them to the required effort, for different digital methods of synchronizing clocks. The presented process is intended for end users who require time synchronization but are not certain about how to judge at least one of the aspects. It can not only be used on existing technologies but should also be transferable to many future approaches. We further relate this approach to several examples. We discuss in detail the approach of medium-range White Rabbit connections over dedicated fibers, a method that occupies an extreme corner in the evaluation, where the effort is exceedingly high, but also yields excellent accuracy and significant reliability.

In the era of Internet of Things (IoT), the connection links are established from devices easily, which is vulnerable to insecure attacks from intruders, hence intrusion detection system in IoT is the need of an hour. One of the important thing for any organization is securing the confidential information and data from outside attacks as well as unauthorized access. There are many attempts made by the researchers to develop the strong intrusion detection system having high accuracy. These systems suffer from many disadvantages like unacceptable accuracy rates including high False Positive Rate (FPR) and high False Negative Rate (FNR), more execution time and failure rate. More of these system models are developed by using traditional machine learning techniques, which have performance limitations in terms of accuracy and timeliness both. These limitations can be overcome by using the deep learning techniques. Deep learning techniques have the capability to generate highly accurate results and are fault tolerant. Here, the intrusion detection model for IoT is designed by using the Taylor-Spider Monkey optimization (Taylor-SMO) which will be developed to train the Deep belief neural network (DBN) towards achieving an accurate intrusion detection model. The deep learning accuracy gets increased with increasing number of training data samples and testing data samples. The optimization based algorithm for training DBN helps to reduce the FPR and FNR in intrusion detection. The system will be implemented by using the NSL KDD dataset. Also, this model will be trained by using the samples from this dataset, before which feature extraction will be applied and only relevant set of attributes will be selected for model development. This approach can lead to better and satisfactory results in intrusion detection.

Recently, most of the processes are being computerized, due to the development of information and communication technology. In proportion to this, cyber-attacks are also increasing, and state-sponsored cyber-attacks are becoming a great threat to the country. These attacks are often composed of stages and proceed step-by-step, so for defense, it is necessary to predict the next action and perform appropriate mitigation. To this end, the paper proposes a mitigation-based performance evaluation method. We developed the new true positive which can have a value between 0 and 1 according to the mitigation. The experiment result and case studies show that the proposed method can effectively measure forecasting results under cyber security defense system.

The security and privacy concerns along with the amount of data that is required to be processed on regular basis has pushed processing to the edge of the computing systems. Deploying advanced Neural Networks (NN), such as deep neural networks (DNNs) and spiking neural networks (SNNs), that offer state-of-the-art results on resource-constrained edge devices is challenging due to the stringent memory and power/energy constraints. Moreover, these systems are required to maintain correct functionality under diverse security and reliability threats. This paper first discusses existing approaches to address energy efficiency, reliability, and security issues at different system layers, i.e., hardware (HW) and software (SW). Afterward, we discuss how to further improve the performance (latency) and the energy efficiency of Edge AI systems through HW/SW-level optimizations, such as pruning, quantization, and approximation. To address reliability threats (like permanent and transient faults), we highlight cost-effective mitigation techniques, like fault-aware training and mapping. Moreover, we briefly discuss effective detection and protection techniques to address security threats (like model and data corruption). Towards the end, we discuss how these techniques can be combined in an integrated cross-layer framework for realizing robust and energy-efficient Edge AI systems.

Vehicular Ad Hoc Networks (VANETs) have the potential to improve road safety and reduce traffic congestion by enhancing sharing of messages about road conditions. Communication in VANETs depends upon a Public Key Infrastructure (PKI) that checks for message confidentiality, integrity, and authentication. One challenge that the PKI infrastructure does not eliminate is the possibility of malicious vehicles mounting a Distributed Denial of Service (DDoS) attack. We present a scheme that combines statistical modeling and machine learning techniques to detect and prevent blackhole attacks in a VANET environment.Simulation results demonstrate that on average, our model produces an Area Under The Curve (ROC) and Receiver Operating Characteristics (AUC) score of 96.78% which is much higher than a no skill ROC AUC score and only 3.22% away from an ideal ROC AUC score. Considering all the performance metrics, we show that the Support Vector Machine (SVM) and Gradient Boosting classifier are more accurate and perform consistently better under various circumstances. Both have an accuracy of over 98%, F1-scores of over 95%, and ROC AUC scores of over 97%. Our scheme is robust and accurate as evidenced by its ability to identify and prevent blackhole attacks. Moreover, the scheme is scalable in that addition of vehicles to the network does not compromise its accuracy and robustness.

Nowadays, Denial of Service (DOS) is a significant cyberattack that can happen on the Internet. This attack can be taken place with more than one attacker that in this case called Distributed Denial of Service (DDOS). The attackers endeavour to make the resources (server & bandwidth) unavailable to legitimate traffic by overwhelming resources with malicious traffic. An appropriate security module is needed to discriminate the malicious flows with high accuracy to prevent the failure resulting from a DDOS attack. In this paper, a DDoS attack discriminator will be designed for Software Defined Network (SDN) architecture so that it can be deployed in the POX controller. The simulation results present that the proposed model can achieve an accuracy of about 99.4%which shows an outstanding percentage of improvement compared with Decision Tree (DT), K-Nearest Neighbour (KNN), Support Vector Machine (SVM) approaches.

Ransomware attack is posting a serious threat against Android devices and stored data that could be locked or/and encrypted by such attack. Existing solutions attempt to detect and prevent such attack by studying different features and applying various analysis mechanisms including static, dynamic or both. In this paper, recent ransomware detection solutions were investigated and compared. Moreover, a deep analysis of android permissions was conducted to identify significant android permissions that can discriminate ransomware with high accuracy before harming users' devices. Consequently, based on the outcome of this analysis, a permissions-based ransomware detection system is proposed. Different classifiers were tested to build the prediction model of this detection system. After the evaluation of the ransomware detection service, the results revealed high detection rate that reached 96.9%. Additionally, the newly permission-based android dataset constructed in this research will be made available to researchers and developers for future work.

The Internet of Things (IoT) is a system paradigm that recently introduced, which includes different smart devices and applications, especially, in smart cities, e.g.; manufacturing, homes, and offices. To improve their awareness capabilities, it is attractive to add more sensors to their framework. In this paper, we propose adding a new sensor as a wearable sensor connected wirelessly with a neural network located on the base station (WSNB). WSNB enables the added sensor to refine their labels through active learning. The new sensors achieve an average accuracy of 93.81%, which is 4.5% higher than the existing method, removing human support and increasing the life cycle for the sensors by using neural network approach in the base station.

Insider threat makes great damage to the security of information system, traditional security methods are extremely difficult to work. Insider attack identification plays an important role in insider threat detection. Monitoring user's abnormal behavior is an effective method to detect impersonation, this method is applied to insider threat identification, to built user's behavior attribute information database based on weights changeable feedback tree augmented Bayes network, but data is massive, using the dimensionality reduction based on rough set, to establish the process information model of user's behavior attribute. Using the minimum risk Bayes decision can effectively identify the real identity of the user when user's behavior departs from the characteristic model.

Differential privacy offers a formal framework for reasoning about privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analyses results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages aimed at helping a data analyst in programming differentially private analyses. However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses. To overcome this limitation, in this work we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy and their trade-offs. The distinguishing feature of DPella is a novel component which statically tracks the accuracy of different data analyses. In order to make tighter accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can figure out the best manner to calibrate privacy to meet the accuracy requirements.

Partitional Clustering Algorithm (PCA) on the Hadoop Distributed File System is to perform big data securities using the Perturbation Technique is the main idea of the proposed work. There are numerous clustering methods available that are used to categorize the information from the big data. PCA discovers the cluster based on the initial partition of the data. In this approach, it is possible to develop a security safeguarding of data that is impoverished to allow the calculations and communication. The performances were analyzed on Health Care database under the studies of various parameters like precision, accuracy, and F-score measure. The outcome of the results is to demonstrate that this method is used to decrease the complication in preserving privacy and better accuracy than that of the existing techniques.

Most traditional recommendation algorithms only consider the binary relationship between users and projects, these can basically be converted into score prediction problems. But most of these algorithms ignore the users's interests, potential work factors or the other social factors of the recommending products. In this paper, based on the existing trustworthyness model and similarity measure, we puts forward the concept of trust similarity and design a joint interest-content recommendation framework to suggest users which videos to watch in the online video site. In this framework, we first analyze the user's viewing history records, tags and establish the user's interest characteristic vector. Then, based on the updated vector, users should be clustered by sparse subspace clust algorithm, which can improve the efficiency of the algorithm. We certainly improve the calculation of similarity to help users find better neighbors. Finally we conduct experiments using real traces from Tencent Weibo and Youku to verify our method and evaluate its performance. The results demonstrate the effectiveness of our approach and show that our approach can substantially improve the recommendation accuracy.

What does it mean to trust, or not trust, an augmented reality system? Froma computer security point of view, trust in augmented reality represents a real threat to real people. The fact that augmented reality allows the programmer to tinker with the user's senses creates many opportunities for malfeasance. It might be natural to think that if we warn users to be careful it will lower their trust in the system, greatly reducing risk.

In the era of the ever-growing number of smart devices, fraudulent practices through Phishing Websites have become an increasingly severe threat to modern computers and internet security. These websites are designed to steal the personal information from the user and spread over the internet without the knowledge of the user using the system. These websites give a false impression of genuinity to the user by mirroring the real trusted web pages which then leads to the loss of important credentials of the user. So, Detection of such fraudulent websites is an essence and the need of the hour. In this paper, various classifiers have been considered and were found that ensemble classifiers predict to utmost efficiency. The idea behind was whether a combined classifier model performs better than a single classifier model leading to a better efficiency and accuracy. In this paper, for experimentation, three Meta Classifiers, namely, AdaBoostM1, Stacking, and Bagging have been taken into consideration for performance comparison. It is found that Meta Classifier built by combining of simple classifier(s) outperform the simple classifier's performance.

Collaborative filtering (CF) is a successful and hence most widely used technique for recommender systems. However, it is vulnerable to shilling attack due to its open nature, which results in generating biased or false recommendations for users. In literature, segment attack (push attack) has been widely studied and investigated while rare studies have been performed on nuke attack, to the best of our knowledge. Further, the robustness of binary collaborative filtering and hybrid approach has not been investigated against segment-focused attack. In this paper, from the perspective of robustness, binary collaborative filtering, hybrid approach, stand-alone rating user-based, and stand-alone rating item- based recommendation have been evaluated against segment attack on a large dataset (100K ratings) which is found to be more successful as it attacks target set of items. With an aim to find an approach which reflects a higher accuracy in recommending items and is less vulnerable to segment-based attack, the possibility of any relationship between accuracy and vulnerability of six CF approaches were studied. Such an approach needs to be re-examined by the researchers marking the future of recommender system (RS). Experimental results show negligible positive correlation between accuracy and vulnerability of techniques. Publicly available dataset namely MovieLens was used for conducting experiments. Robustness and accuracy of CF techniques were calculated using prediction shift and F-measure, respectively.

Big Data Cyber Security Analytics (BDCA) leverages big data technologies for collecting, storing, and analyzing a large volume of security events data to detect cyber-attacks. Accuracy and response time, being the most important quality concerns for BDCA, are impacted by changes in security events data. Whilst it is promising to adapt a BDCA system's architecture to the changes in security events data for optimizing accuracy and response time, it is important to consider large search space of architectural configurations. Searching a large space of configurations for potential adaptation incurs an overwhelming adaptation time, which may cancel the benefits of adaptation. We present an adaptation approach, QuickAdapt, to enable quick adaptation of a BDCA system. QuickAdapt uses descriptive statistics (e.g., mean and variance) of security events data and fuzzy rules to (re) compose a system with a set of components to ensure optimal accuracy and response time. We have evaluated QuickAdapt for a distributed BDCA system using four datasets. Our evaluation shows that on average QuickAdapt reduces adaptation time by 105× with a competitive adaptation accuracy of 70% as compared to an existing solution.

keystroke dynamics authenticates the system user by analyzing his typing rhythm. Given that each of us has his own typing rhythm and that the method is based on the keyboard makes it available in all computer machines, these two reasons (uniqueness and reduced cost) have made the method very solicit by administrators of security. In addition, the researchers used the method in different fields that are listed later in the paper.

The traditional smart-home is designed to integrate the concept of the Internet of Things(IoT) into our home environment, and to improve the comfort of home. It connects electrical products and household goods to the network, and then monitors and controls them. However, this paper takes home safety as the main axis of research. It combines the past concept of smart-home and technology of machine learning to improve the whole system of smart-home. Through systematic self-learning, it automatically figure out whether it is normal or abnormal, and reports to remind building occupants safety. At the same time, it saves the cost of human resources preservation. This paper make a set of rules table as the basic criteria first, and then classify a part of data which collected by traditional Internet of Things of smart-home by manual way, which includes the opening and closing of doors and windows, the starting and stopping of motors, the connection and interruption of the system, and the time of sending each data to label, then use Support Vector Machine(SVM) algorithm to classify and build models, and then train it. The executed model is applied to our smart-home system. Finally, we verify the Accuracy of anomaly reporting in our system.

Nowadays data is always stored in a computer in the hyper-connected world and, a company or an organization or a person can come across financial loss, reputation loss, business disruption and intellectual property loss because of data leakage or data disclosure. Remote Access Trojans are used to invade a victim's PC and collect information from it. There have been signatures for these that have already emerged and defined as malwares, but there is no available signature yet if a malware or a remote access Trojan is a zero-day threat. In this circumstance network behavioral analysis is more useful than signature-based anti-virus scanners in order to detect the different behavior of malware. When the traffic will be cut or stoppedis important in capturing network traffic. In this paper, effective features for detecting RATs are proposed. These features are extracted from the first twenty packets. Our approach achieves 98% accuracy and 10% false negative rate by random forest algorithm.

Poisoning attack in which an adversary misleads the learning process by manipulating its training set significantly affect the performance of classifiers in security applications. This paper proposed a robust learning method which reduces the influences of attack samples on learning. The sensitivity, defined as the fluctuation of the output with small perturbation of the input, in Localized Generalization Error Model (L-GEM) is measured for each training sample. The classifier's output on attack samples may be sensitive and inaccurate since these samples are different from other untainted samples. An import score is assigned to each sample according to its localized generalization error bound. The classifier is trained using a new training set obtained by resampling the samples according to their importance scores. RBFNN is applied as the classifier in experimental evaluation. The proposed model outperforms than the traditional one under the well-known label flip poisoning attacks including nearest-first and farthest-first flips attack.

There are vast amounts of information in our world. Accessing the most accurate information in a speedy way is becoming more difficult and complicated. A lot of relevant information gets ignored which leads to much duplication of work and effort. The focuses tend to provide rapid and intelligent retrieval systems. Information retrieval (IR) is the process of searching for information that is related to some topics of interest. Due to the massive search results, the user will normally have difficulty in identifying the relevant ones. To alleviate this problem, a recommendation system is used. A recommendation system is a sort of filtering information system, which predicts the relevance of retrieved information to the user's needs according to some criteria. Hence, it can provide the user with the results that best fit their needs. The services provided through the web normally provide massive information about any requested item or service. An efficient recommendation system is required to classify this information result. A recommendation system can be further improved if augmented with a level of trust information. That is, recommendations are ranked according to their level of trust. In our research, we produced a recommendation system combined with an efficient level of trust system to guarantee that the posts, comments and feedbacks from users are trusted. We customized the concept of LoT (Level of Trust) [1] since it can cover medical, shopping and learning through social media. The proposed system TRS\_LoT provides trusted recommendations to the users with a high percentage of accuracy. Whereas a 300 post with more than 5000 comments from ``Amazon'' was selected to be used as a dataset, the experiment has been conducted by using same dataset based on ``post rating''.

Existing access control mechanisms are based on the concept of identity enrolment and recognition and assume that recognized identity is a synonym to ethical actions, yet statistics over the years show that the most severe security breaches are the results of trusted, identified, and legitimate users who turned into malicious insiders. Insider threat damages vary from intellectual property loss and fraud to information technology sabotage. As insider threat incidents evolve, there exist demands for a nonidentity-based authentication measure that rejects access to authorized individuals who have mal-intents of access. In this paper, we study the possibility of using the user's intention as an access control measure using the involuntary electroencephalogram reactions toward visual stimuli. We propose intent-based access control (IBAC) that detects the intentions of access based on the existence of knowledge about an intention. IBAC takes advantage of the robustness of the concealed information test to assess access risk. We use the intent and intent motivation level to compute the access risk. Based on the calculated risk and risk accepted threshold, the system makes the decision whether to grant or deny access requests. We assessed the model using experiments on 30 participants that proved the robustness of the proposed solution.