Biblio

Demand response analysis in smart grid deployment substantiated itself as an important research area in recent few years. Two-way communication between utility and users makes peak load reduction feasible by delaying the operation of deferrable appliances. Flexible appliance rescheduling is preferred to the users compared to traditional load curtailment. Again, if users' preferences are accounted into appliance transferring process, then customers concede a little discomfort to help the utility in peak reduction. This paper presents a novel Utility-User Cooperative Algorithm (UUCA) to lower total electricity cost and gross peak demand while preserving users' privacy and preferences. Main driving force in UUCA to motivate the consumers is a new cost function for their flexible appliances. As a result, utility will experience low peak and due to electricity cost decrement, users will get reduced bill. However, to maintain privacy, the behaviors of one customer have not be revealed either to other customers or to the central utility. To justify the effectiveness, UUCA is executed separately on residential, commercial and industrial customers of a distribution grid. Harmony search optimization technique has proved itself superior compared to other heuristic search techniques to prove efficacy of UUCA.

Cybersecurity education is a pressing need, when computer systems and mobile devices are ubiquitous and so are the associated threats. However, in the teaching and learning process of cybersecurity, it is challenging when the students are from diverse disciplines with various academic backgrounds. In this project, a number of virtual laboratories are developed to facilitate the teaching and learning process in a cybersecurity course. The aim of the laboratories is to strengthen students’ understanding of cybersecurity topics, and to provide students hands-on experience of encountering various security threats. The results of this project indicate that virtual laboratories do facilitate the teaching and learning process in cybersecurity for diverse discipline students. Also, we observed that there is an underestimation of the difficulty of studying cybersecurity by the students due to the general image of cybersecurity in public, which had a negative impact on the student’s interest in studying cybersecurity.

The disclosure of an important yet sensitive link may cause serious privacy crisis between two users of a social graph. Only deleting the sensitive link referred to as a target link which is often the attacked target of adversaries is not enough, because the adversarial link prediction can deeply forecast the existence of the missing target link. Thus, to defend some specific adversarial link prediction, a budget limited number of other non-target links should be optimally removed. We first propose a path-based dissimilarity function as the optimizing objective and prove that the greedy link deletion to preserve target link privacy referred to as the GLD2Privacy which has monotonicity and submodularity properties can achieve a near optimal solution. However, emulating all length limited paths between any pair of nodes for GLD2Privacy mechanism is impossible in large scale social graphs. Secondly, we propose a Walk2Privacy mechanism that uses self-avoiding random walk which can efficiently run in large scale graphs to sample the paths of given lengths between the two ends of any missing target link, and based on the sampled paths we select the alternative non-target links being deleted for privacy purpose. Finally, we compose experiments to demonstrate that the Walk2Privacy algorithm can remarkably reduce the time consumption and achieve a very near solution that is achieved by the GLD2Privacy.

PRIME protocol is a narrowband power line communication protocol whose security is based on Advanced Encryption Standard. However, the key expansion process of AES algorithm is not unidirectional, and each round of keys are linearly related to each other, it is less difficult for eavesdroppers to crack AES encryption algorithm, leading to threats to the security of PRIME protocol. To solve this problem, this paper proposes an improvement of PRIME protocol based on chaotic cryptography. The core of this method is to use Chebyshev chaotic mapping and Logistic chaotic mapping to generate each round of key in the key expansion process of AES algorithm, In this way, the linear correlation between the key rounds can be reduced, making the key expansion process unidirectional, increasing the crack difficulty of AES encryption algorithm, and improving the security of PRIME protocol.

Many consumers now rely on different forms of voice assistants, both stand-alone devices and those built into smartphones. Currently, these systems react to specific wake-words, such as "Alexa," "Siri," or "Ok Google." However, with advancements in natural language processing, the next generation of voice assistants could instead always listen to the acoustic environment and proactively provide services and recommendations based on conversations without being explicitly invoked. We refer to such devices as "always listening voice assistants" and explore expectations around their potential use. In this paper, we report on a 178-participant survey investigating the potential services people anticipate from such a device and how they feel about sharing their data for these purposes. Our findings reveal that participants can anticipate a wide range of services pertaining to a conversation; however, most of the services are very similar to those that existing voice assistants currently provide with explicit commands. Participants are more likely to consent to share a conversation when they do not find it sensitive, they are comfortable with the service and find it beneficial, and when they already own a stand-alone voice assistant. Based on our findings we discuss the privacy challenges in designing an always-listening voice assistant.

Cryptographic hash functions play a crucial role in information security. Cryptographic hash functions are used in various cryptographic applications to verify the message authenticity and integrity. In this paper we propose a Cellular Automata Based Hashing Algorithm (CABHA) for generating strong cryptographic hash function. The proposed CABHA algorithm uses the cellular automata rules and a custom transformation function to create a strong hash from an input message and a key.

The exponential growth of the Internet and its users and the emergence of Web 2.0 have caused a large volume of textual data to be created. Automatic analysis of such data can be used in making decisions. As online text is created by different producers with different styles of writing, pre-processing is a necessity prior to any processes related to natural language tasks. An essential part of textual preprocessing prior to the recognition of the word vocabulary is normalization, which includes the correction of spaces that particularly in the Persian language this includes both full-spaces between words and half-spaces. Through the review of user comments within social media services, it can be seen that in many cases users do not adhere to grammatical rules of inserting both forms of spaces, which increases the complexity of the identification of words and henceforth, reducing the accuracy of further processing on the text. In this study, current issues in the normalization and tokenization of preprocessing tools within the Persian language and essentially identifying and correcting the separation of words are and the correction of spaces are proposed. The results obtained and compared to leading preprocessing tools highlight the significance of the proposed methodology.

The growing number of internet based services and applications along with increasing adoption rate of connected wired and wireless devices presents opportunities as well as technical challenges and threads. Distributed Denial of Service (DDoS) attacks have huge devastating effects on internet enabled services. It can be implemented diversely with a variety of tools and codes. Therefore, it is almost impossible to define a single solution to prevent DDoS attacks. The available solutions try to protect internet services from DDoS attacks, but there is no accepted best-practice yet to this security breach. On the other hand, distinguishing DDoS attacks from analogous Flash Events (FEs) wherein huge number of legitimate users try to access a specific internet based services and applications is a tough challenge. Both DDoS attacks and FEs result in unavailability of service, but they should be treated with different countermeasures. Therefore, it is worthwhile to investigate novel methods which can detect well disguising DDoS attacks from similar FE traffic. This paper will contribute to this topic by proposing a hybrid DDoS and FE detection scheme; taking 3 isolated approaches including Kernel Online Anomaly Detection (KOAD), Shannon Entropy and Mahalanobis Distance. In this study, Shannon entropy is utilized with an online machine learning technique to detect abnormal traffic including DDoS attacks and FE traffic. Subsequently, the Mahalanobis distance metric is employed to differentiate DDoS and FE traffic. the purposed method is validated using simulated DDoS attacks, real normal and FE traffic. The results revealed that the Mahalanobis distance metric works well in combination with machine learning approach to detect and discriminate DDoS and FE traffic in terms of false alarms and detection rate.

Vehicular ad hoc network is one of most recent research areas to deploy intelligent Transport System. Due to their highly dynamic topology, energy constrained and no central point coordination, routing with minimal delay, minimal energy and maximize throughput is a big challenge. Software Defined Networking (SDN) is new paradigm to improve overall network lifetime. It incorporates dynamic changes with minimal end-end delay, and enhances network intelligence. Along with this, intelligence secure routing is also a major constraint. This paper proposes a novel approach to Energy efficient secured routing protocol for Software Defined Internet of vehicles using Restricted Boltzmann Algorithm. This algorithm is to detect hostile routes with minimum delay, minimum energy and maximum throughput compared with traditional routing protocols.

This paper reports the definition, setup and obtained results of the Fed4FIRE + medium experiment ERASER, aimed to evaluate the actual Quality of Service (QoS) guarantees that the clean-slate Recursive InterNetwork Architecture (RINA) can deliver to heterogeneous applications at large-scale. To this goal, a 37-Node 5G metro/regional RINA network scenario, spanning from the end-user to the server where applications run in a datacenter has been configured in the Virtual Wall experimentation facility. This scenario has initially been loaded with synthetic application traffic flows, with diverse QoS requirements, thus reproducing different network load conditions. Next,their experienced QoS metrics end-to-end have been measured with two different QTA-Mux (i.e., the most accepted candidate scheduling policy for providing RINA with its QoS support) deployment scenarios. Moreover, on this RINA network scenario loaded with synthetic application traffic flows, a real HD (1080p) video streaming demonstration has also been conducted, setting up video streaming sessions to end-users at different network locations, illustrating the perceived Quality of Experience (QoE). Obtained results in ERASER disclose that, by appropriately deploying and configuring QTA-Mux, RINA can yield effective QoS support, which has provided perfect QoE in almost all locations in our demo when assigning video traffic flows the highest (i.e., Gold) QoS Cube.

The increasing demand and the use of mobile ad hoc network (MANET) in recent days have attracted the attention of researchers towards pursuing active research work largely related to security attacks in MANET. Gray hole attack is one of the most common security attacks observed in MANET. The paper focuses on gray hole attack analysis in Ad hoc on demand distance vector(AODV) routing protocol based MANET with reliability as a metric. Simulation is performed using ns-2.35 simulation software under varying number of network nodes and varying number of gray hole nodes. Results of simulation indicates that increasing the number of gray hole node in the MANET will decrease the reliability of MANET.

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.

The problem statement is that at present there is no stable algorithm which provides security for resource constrained devices because classic cryptography algorithms are too heavy to be implemented. So we will provide a model about the various cryptographic algorithms in this field which can be modified to be implement on constrained devices. The advantages and disadvantages of IOT devices will be taken into consideration to develop a model. Mainly IOT devices works on three layers which are physical layer, application and commutation layer. We have discuss how IOT devices individually works on these layers and how security is compromised. So, we can build a model where minimum intervention of third party is involved i.e. hackers and we can have higher and tight privacy and security system [1].we will discuss about the different ciphers(block and stream) and functions(hash algorithms) through which we can achieve cryptographic algorithms which can be implemented on resource constrained devices. Cost, safety and productivity are the three parameters which determines the ratio for block cipher. Mostly programmers are forced to choose between these two; either cost and safety, safety and productivity, cost and productivity. The main challenge is to optimize or balance between these three factors which is extremely a difficult task to perform. In this paper we will try to build a model which will optimize these three factors and will enhance the security of IOT devices.

Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.

The relationship between magnetic domain structures and magnetic properties of Nd-doped Sm(Fe, Cu, Zr, Co)7.5 was investigated. In the preparation process, slow cooling between sintering and solution treatment was employed to promote homogenization of microstructures. The developed magnet achieved a maximum energy product, [BH]m, of 33.8 MGOe and coercivity, Hcb, of 11.2 kOe at 25 °C, respectively. Moreover, B-H line at 150 °C was linear, which means that irreversible demagnetization does not occur even at 150 °C. Temperature coefficients of remanent magnetic flux density, Br, and intrinsic coercivity, Hcj, were 0.035%/K and 0.24%/K, respectively, as usual the conventional Sm-Co magnet. Magnetic domain structures were observed with a Kerr effect microscope with a magnetic field applied from 0 to -20 kOe, and then reverse magnetic domains were generated evenly from grain boundaries. Microstructures referred to as “cell structures” were observed with a scanning transmission electron microscope. Fe and Cu were separated to 2-17 and 1-5 phases, respectively. Moreover, without producing impurity phases, Nd showed the same composition behavior with Sm in a cell structure.

In this paper, an image encryption scheme based on dynamic DNA sequence and two dimension logistic map is proposed. Firstly two different pseudo random sequences are generated using two dimension Sine-Henon alteration map. These sequences are used for altering the positions of each pixel of plain image row wise and column wise respectively. Secondly each pixels of distorted image and values of random sequences are converted into a DNA sequence dynamically using one dimension logistic map. Reversible DNA operations are applied between DNA converted pixel and random values. At last after decoding the results of DNA operations cipher image is obtained. Different theoretical analyses and experimental results proved the effectiveness of this algorithm. Large key space proved that it is possible to protect different types of attacks using our proposed encryption scheme.

Multi-tenant cloud networks have various security and monitoring service functions (SFs) that constitute a service function chain (SFC) between two endpoints. SF rule ordering overlaps and policy conflicts can cause increased latency, service disruption and security breaches in cloud networks. Software Defined Network (SDN) based Network Function Virtualization (NFV) has emerged as a solution that allows dynamic SFC composition and traffic steering in a cloud network. We propose an SDN enabled Universal Policy Checking (SUPC) framework, to provide 1) Flow Composition and Ordering by translating various SF rules into the OpenFlow format. This ensures elimination of redundant rules and policy compliance in SFC. 2) Flow conflict analysis to identify conflicts in header space and actions between various SF rules. Our results show a significant reduction in SF rules on composition. Additionally, our conflict checking mechanism was able to identify several rule conflicts that pose security, efficiency, and service availability issues in the cloud network.

SVM has been one of the most popular machine learning method for the binary classification such as sentiment analysis and spam message filtering. We explored a word embedding method for the construction of a feature vector and the deep learning method for the binary classification. CBOW is used as a word embedding technique and feedforward neural network is applied to classify SMS messages into ham or spam. The accuracy of the two classification methods of SVM and neural network are compared for the binary classification. The experimental result shows that the accuracy of deep learning method is better than the conventional machine learning method of SVM-light in the binary classification.

Advance persistent threat is a primary security concerns to the big organizations and its technical infrastructure, from cyber criminals seeking personal and financial information to state sponsored attacks designed to disrupt, compromising infrastructure, sidestepping security efforts thus causing serious damage to organizations. A skilled cybercriminal using multiple attack vectors and entry points navigates around the defenses, evading IDS/Firewall detection and breaching the network in no time. To understand the big picture, this paper analyses an approach to advanced persistent threat by doing the same things the bad guys do on a network setup. We will walk through various steps from foot-printing and reconnaissance, scanning networks, gaining access, maintaining access to finally clearing tracks, as in a real world attack. We will walk through different attack tools and exploits used in each phase and comparative study on their effectiveness, along with explaining their attack vectors and its countermeasures. We will conclude the paper by explaining the factors which actually qualify to be an Advance Persistent Threat.

Conventional methods for anomaly detection include techniques based on clustering, proximity or classification. With the rapidly growing social networks, outliers or anomalies find ingenious ways to obscure themselves in the network and making the conventional techniques inefficient. In this paper, we utilize the ability of Deep Learning over topological characteristics of a social network to detect anomalies in email network and twitter network. We present a model, Graph Neural Network, which is applied on social connection graphs to detect anomalies. The combinations of various social network statistical measures are taken into account to study the graph structure and functioning of the anomalous nodes by employing deep neural networks on it. The hidden layer of the neural network plays an important role in finding the impact of statistical measure combination in anomaly detection.

Intentionally deceptive content presented under the guise of legitimate journalism is a worldwide information accuracy and integrity problem that affects opinion forming, decision making, and voting patterns. Most so-called `fake news' is initially distributed over social media conduits like Facebook and Twitter and later finds its way onto mainstream media platforms such as traditional television and radio news. The fake news stories that are initially seeded over social media platforms share key linguistic characteristics such as making excessive use of unsubstantiated hyperbole and non-attributed quoted content. In this paper, the results of a fake news identification study that documents the performance of a fake news classifier are presented. The Textblob, Natural Language, and SciPy Toolkits were used to develop a novel fake news detector that uses quoted attribution in a Bayesian machine learning system as a key feature to estimate the likelihood that a news article is fake. The resultant process precision is 63.333% effective at assessing the likelihood that an article with quotes is fake. This process is called influence mining and this novel technique is presented as a method that can be used to enable fake news and even propaganda detection. In this paper, the research process, technical analysis, technical linguistics work, and classifier performance and results are presented. The paper concludes with a discussion of how the current system will evolve into an influence mining system.

The future fifth-generation (5G) mobile communications system has already become a focus around the world. A large number of late-model services and applications including high definition visual communication, internet of vehicles, multimedia interaction, mobile industry automation, and etc, will be added to 5G network platform in the future. Different application services have different security requirements. However, the current user authentication for services and applications: Extensible Authentication Protocol (EAP) suggested by the 3GPP committee, is only a unitary authentication model, which is unable to meet the diversified security requirements of differentiated services. In this paper, we present a new diversified identity management as well as a flexible and composable three-factor authentication mechanism for different applications in 5G multi-service systems. The proposed scheme can provide four identity authentication methods for different security levels by easily splitting or assembling the proposed three-factor authentication mechanism. Without a design of several different authentication protocols, our proposed scheme can improve the efficiency, service of quality and reduce the complexity of the entire 5G multi-service system. Performance analysis results show that our proposed scheme can ensure the security with ideal efficiency.

In the wake of diversity of service requirements and increasing push for extreme efficiency, adaptability propelled by machine learning (ML) a.k.a self organizing networks (SON) is emerging as an inevitable design feature for future mobile 5G networks. The implementation of SON with ML as a foundation requires significant amounts of real labeled sample data for the networks to train on, with high correlation between the amount of sample data and the effectiveness of the SON algorithm. As generally real labeled data is scarce therefore it can become bottleneck for ML empowered SON for unleashing their true potential. In this work, we propose a method of expanding these sample data sets using Generative Adversarial Networks (GANs), which are based on two interconnected deep artificial neural networks. This method is an alternative to taking more data to expand the sample set, preferred in cases where taking more data is not simple, feasible, or efficient. We demonstrate how the method can generate large amounts of realistic synthetic data, utilizing the GAN's ability of generation and discrimination, able to be easily added to the sample set. This method is, as an example, implemented with Call Data Records (CDRs) containing the start hour of a call and the duration of the call, in minutes taken from a real mobile operator. Results show that the method can be used with a relatively small sample set and little information about the statistics of the true CDRs and still make accurate synthetic ones.

This paper provides hardware-independent authentication named as Intelligent Authentication Scheme, which rectifies the design weaknesses that may be exploited by various security attacks. The Intelligent Authentication Scheme protects against various types of security attacks such as password-guessing attack, replay attack, streaming bots attack (denial of service), keylogger, screenlogger and phishing attack. Besides reducing the overall cost, it also balances both security and usability. It is a unique authentication scheme.

In multi-tenant datacenters, the hardware may be homogeneous but the traffic often is not. For instance, customers who pay an equal amount of money can get an unequal share of the bottleneck capacity when they do not open the same number of TCP connections. To address this problem, several recent proposals try to manipulate the traffic that TCP sends from the VMs. VCC and AC/DC are two new mechanisms that let the hypervisor control traffic by influencing the TCP receiver window (rwnd). This avoids changing the guest OS, but has limitations (it is not possible to make TCP increase its rate faster than it normally would). Seawall, on the other hand, completely rewrites TCP's congestion control, achieving fairness but requiring significant changes to both the hypervisor and the guest OS. There seems to be a need for a middle ground: a method to control TCP's sending rate without requiring a complete redesign of its congestion control. We introduce a minimally-invasive solution that is flexible enough to cater for needs ranging from weighted fairness in multi-tenant datacenters to potentially offering Internet-wide benefits from reduced interflow competition.