Multilayer Self-Defense System to Protect Enterprise Cloud

A data breach can seriously impact organizational intellectual property, resources, time, and product value. The risk of system intrusion is augmented by the intrinsic openness of commonly utilized technologies like TCP/IP protocols. As TCP relies on IP addresses, an attacker may easily trace the IP address of the organization. Given that many organizations run the risk of data breach and cyber-attacks at a certain point, a repeatable and well-developed incident response framework is critical to shield them. Enterprise cloud possesses the challenges of security, lack of transparency, trust and loss of controls. Technology eases quickens the processing of information but holds numerous risks including hacking and confidentiality problems. The risk increases when the organization outsources the cloud storage services through the vendor and suffers from security breaches and need to create security systems to prevent data networks from being compromised. The business model also leads to insecurity issues which derail its popularity. An attack mitigation system is the best solution to protect online services from emerging cyber-attacks. This research focuses on cloud computing security, cyber threats, machine learning-based attack detection, and mitigation system. The proposed SDN-based multilayer machine learning-based self-defense system effectively detects and mitigates the cyber-attack and protects cloud-based enterprise solutions. The results show the accuracy of the proposed machine learning techniques and the effectiveness of attack detection and the mitigation system.     

Critical current density and magnetization studies on Bi(Pb)SrCaCuO

HighT _c phase (T _c ∼ 110 K) has been obtained in Bi-Sr-Ca-Cu-O system by partially substituting Bi by Pb. Magnetic hysteresis has been measured as a function of temperature. Critical current densities have been measured at 77 K both by transport and a.c. magnetization method in bulk samples for various concentrations of Pb. The results show that substitution of 15 at % Pb for Bi is most preferable for higher critical current density.     

Qualitative Evolution of Asymmetric Raman Line-Shape for NanoStructures

A qualitative evolution of an asymmetric Raman line-shape function from a Lorentzian line-shape is discussed here for application in low dimensional semiconductors. The step-by-step evolution reported here is based on the phonon confinement model which is successfully used in literature to explain the asymmetric Raman line-shape from semiconductor nanostructures. Physical significance of different terms in the theoretical asymmetric Raman line-shape has been explained here. Better understanding of theoretical reasoning behind each term allows one to use the theoretical Raman line-shape without going into the details of theory from first principle. This will enable one to empirically derive a theoretical Raman line-shape function for any material if information about its phonon dispersion relation, size dependence, etc., is known.     

Electrical and photovoltaic response of bulk hetero-junction device made from poly (3-phenyl azo methine thiophene) (PPAT) and 1, 1′-diallyl substituted 4, 4′-dipyridine (DADP)

Electrical and photovoltaic properties of donor–acceptor composite system comprised of poly (3-phenyl azo methine thiophene) (PPAT) and 1, 1′–diallyl substituted 4, 4′-dipyridine (DADP) were investigated. A significant enhancement of photocurrent was observed when PPAT was blended with DADP. The increase in photocurrent has been explained in terms of efficient charge separation that resulted from the transfer of photo-excited electrons from PPAT to DADP. The strong quenching of fluorescence of PPAT was caused by the presence of DADP that indicates the photo-induced charge transfer from PPAT to DADP. The open circuit voltage (V_oc) generated in the device is independent of the variation of work function of negative metal electrode that has been explained in terms of Fermi level pinning between DADP and metal via surface charges. The electrical characteristics of ITO/PPAT: DADP/Al photovoltaic device were determined by analyzing the dependence of short circuit photocurrent density (J_sc) and V_oc under illumination at different temperatures. The V_oc decreases almost linearly with increasing temperature, while short-circuit photocurrent increases logarithmically with temperature and saturates at higher temperature above 330 K. This dependence of J_sc and V_oc on temperature has been discussed in terms of possible mechanism that involves the photovoltage generation and charge carrier transport in the device under thermally activated state. The photovoltaic device made from PPAT: DADP blend has shown three times higher photosensitivity than that of made from pure PPAT.     

A new customized document categorization scheme using rough membership

One of the problems that plague document ranking is the inherent ambiguity, which arises due to the nuances of natural language. Though two documents may contain the same set of words, their relevance may be very different to a single user, since the context of the words usually determines the relevance of a document. Context of a document is very difficult to model mathematically other than through user preferences. Since it is difficult to perceive all possible user interests a priori and install filters for the same at the server side, we propose a rough-set-based document filtering scheme which can be used to build customized filters at the user end. The documents retrieved by a traditional search engine can then be filtered automatically by this agent and the user is not flooded with a lot of irrelevant material. A rough-set-based classificatory analysis is used to learn the user's bias for a category of documents. This is then used to filter out irrelevant documents for the user. To do this we have proposed the use of novel rough membership functions for computing the membership of a document to various categories.     

M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT

In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture known as a Multi-class Classification based Intrusion Detection Model (M-IDM), which typically relies on data collected by real devices and the use of convolutional neural networks (i.e., it exhibits better performance compared with conventional machine learning algorithms, such as naïve Bayes, support vector machine (SVM)). Unlike existing studies, the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices, such as a patient’s monitors (i.e., electrocardiogram and thermometers). The proposed architecture classifies the data into multiple classes: Critical, informal, major, and minor, for intrusion detection. Further, we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms, including naïve Bayes, SVM, and logistic regression, using neural networks.     

Heating rate dependent delamination of metal–polymer interfaces: experiments and modeling

Bimaterial interfaces in microelectronics packages are the most common regions of failure under thermo-mechanical excursions. In this work, we report experimentally observed role of heating rate on the delamination initiation and propagation across a metal-polymer interface in a microelectronic package. We observe that the rate of delamination propagation increases with increasing heating rate. When the heating rate increases, in addition to the higher amount of delamination growth per unit time, experimental results suggests that higher growth will also incur per unit temperature (loading). Correspondingly, the temperature at which complete delamination occur decreases. Using finite element modeling with cohesive interfaces, we provide a plausible explanation to this observed phenomenon. The analyses indicate that the mechanical behavior of the bimaterial interface is sensitive to both temperature and thermal rate.     

ICT-driven financial inclusion initiatives for urban poor in a developing economy: implications for public policy

A key agenda of policy-makers in both the developing and the developed worlds is to have an inclusive growth. Financial services have been recognised as one of the fundamental services that have the potential to achieve this objective and help move towards a more just and equitable society. This paper attempts to assess the impact of three information and communication technology-driven initiatives in the field of financial services by differentiating them on the basis of service design. This study goes beyond the stated objective of these services and uses Sen’s capability perspective to study their impact across three non-economic outcome variables viz. literacy, service-specific well-being and empowerment. The data for the study are collected from the urban poor across different locations in a large metropolitan city in South India where the three different services are being offered. The study follows a quasi-experimental design (field study) and ensures matching of the treatment and control groups through propensity score matching. The matched data are analysed using an independent sample t-test. The result of the analysis shows that service design plays a key role in increasing service literacy, improving service-specific well-being and empowering end users. The study suggests that the true challenge for providers (government and partner agencies) of these services is in designing a service that ensures a balance of structure and flexibility without constraining the choice of end users through innovative service blueprints.     

Predictors of user perceptions of web recommender systems: How the basis for generating experience and search product recommendations affects user responses

One critical question suggested by Web 2.0 is as follows: When is it better to leverage the knowledge of other users vs. rely on the product characteristic-based metrics for online product recommenders? Three recent and notable changes of recommender systems have been as follows: (1) a shift from characteristic-based recommendation algorithms to social-based recommendation algorithms; (2) an increase in the number of dimensions on which algorithms are based; and (3) availability of products that cannot be examined for quality before purchase. The combination of these elements is affecting users’ perceptions and attitudes regarding recommender systems and the products recommended by them, but the psychological effects of these trends remain unexplored. The current study empirically examines the effects of these elements, using a 2 (recommendation approach: content-based vs. collaborative-based, within)×2 (dimensions used to generate recommendations: 6 vs. 30, between)×2 (product type: experience products (fragrances) vs. search products (rugs), between) Web-based study (N=80). Participants were told that they would use two recommender systems distinguished by recommendation approach (in fact, the recommendations were identical). There were no substantive main effects, but all three variables exhibited two-way interactions, indicating that design strategies must be grounded in a multi-dimensional understanding of these variables. The implications of this research for the psychology and design of recommender systems are presented.