Security anomaly detection in software‐defined networking based on a prediction technique

Nowadays, software‐defined networking (SDN) is regarded as the best solution for the centralized handling and monitoring of large networks. However, it should be noted that SDN architecture suffers from the same security issues, which are the case with common networks. As a case in point, one of the shortcomings of SDNs is related to its high vulnerability to distributed denial of service (DDoS) attacks and other similar ones. Indeed, anomaly detection systems have been considered to deal with these attacks. The challenges are related to designing these systems including gathering data, extracting effective features, and selecting the best model for anomaly detection. In this paper, a novel combined approach is proposed; this method uses NetFlow protocol for gathering information and generating dataset, information gain ratio (IGR), in order to select the effective and relevant features and ensemble learning scheme (Stacking) for developing a structure with desirable performance and efficiency for detecting anomaly in SDN environment. The results obtained from the experiments revealed that the proposed method performs better than other methods in terms of enhancing accuracy (AC) and detection rate (DR) and reducing classification error (CE) and false alarm rate (FAR). The AC, DR, CE, and FAR of the proposed model were measured as 99.92%, 99.83%, 0.08%, and 0.03%, respectively. Furthermore, the proposed method prevents the occurrence of excessive overload on the controller and OpenFlow.     

Modelling upper echelons’ behavioural drivers of Green IT/IS adoption using an integrated Interpretive Structural Modelling – Analytic Network Process approach

The purpose of this paper is to understand and identify various psychological drivers which motivate managers of organizations to adopt Green information technology (Green IT) and Green information system (Green IS) within their corporations. These psychological factors are modelled through the lens of Upper Echelon Theory (UET) to study their interrelationship and interdependency and further prioritize them to strategize appropriate managerial actions. The factors were identified through an exhaustive literature review and further confirmed by the experts in the field. A hybrid Interpretive Structural Modelling – Analytic Network Process (ISM-ANP) approach has been adopted to establish the complex interrelationship among the psychological drivers and further to cluster and prioritize them. The main contribution of this study is composing directions and dominance of various psychological drivers to enhance decision-making process of managers towards the adoption of Green IT/IS. As long as decisions are made by individuals within organizations, this paper forms the basis of identifying individual-level factors important in motivating managers towards Green IT/IS adoption. The results of this study would help organizations and policy makers to understand and develop strategies to target and select an individual for managerial position with mind-set more towards environmental sustainability and Green IT/IS concepts. By applying the proposed methodology, organizations can classify and prioritize their action plans such as several educational methods to reinforce, foster and incline psychological factors to motivate their individuals to behave proenvironmentally and practice Green IT/IS initiatives to enhance environmental sustainability of their corporations.     

Public Key Cryptosystem Based on Low Density Lattice Codes

McEliece and Goldreich–Goldwasser–Halevi (GGH) cryptosystems are two instances of code and lattice-based cryptosystems whose security are based on the hardness of coding theoretic and lattice problems, respectively. However, such cryptosystems have a number of drawbacks which make them inefficient in practice. On the other hand, low density lattice codes (LDLCs) are practical lattice codes which can achieve capacity over additive white Gaussian noise channel and also can be encoded and decoded efficiently. This paper introduces a public key cryptosystem based on Latin square LDLCs, by which a relationship can be attained between code and lattice-based cryptography. In this way, we can exploit the efficient properties of codes and lattices, simultaneously to improve the security and efficiency of the proposed scheme. For instance, the security of this scheme is based on the hard problems related to lattices, i.e., closest vector problem and shortest basis problem, which in turn lead to increase the security level. On the other hand, we exploit the low complexity decoding algorithm of LDLCs to reduce the computational complexity. Moreover, this property allows using the larger values of the codeword length. Also, we use the special Gaussian vector, whose variance is upper bounded by Poltyrev bound, as the perturbation (error) vector. These strategies make the proposed scheme to be secure against the conventional cryptanalytic attacks.     

Optimal visual sensor placement for coverage based on target location profile

The proper placement of visual sensors across a sensor field for covering targets with arbitrary location and orientation is a mission-critical decision in surveillance applications. The specifics of sensor deployment in these applications not only determine the maximum achievable coverage, but it also affects the quality of the target’s appearance in cameras for subsequent use in vision tasks. However, the inaccuracies inherent in localization techniques and the lack of knowledge regarding the target orientation render existing proposals insufficient for real-life scenarios. In this paper, we address both challenges. First, we extend the conventional point representation of targets with a circular model to account for full-angle coverage of targets with known location yet with unknown orientation from all directions. We then assume, in the absence of precise location information, a trajectory profile of targets could instead be generated through the importance sampling of the environment, indicating spots where the target is most likely located. This profile-based abstraction enables us to capture the uncertainty in target’s location by encircling every agglomeration of proximal samples within one cluster. Each cluster can then be viewed as a virtual macroscopic circular target for which we formulate the coverage problem in terms of a Binary Integer Programming (BIP) model. We have also taken into account the presence of obstruction in between multiple targets by calculating the angles of view of the sensors in an occlusion-dependant manner, effectively determining optimal placement for maximal instead of full-angle coverage. Evaluation results derived from our simulation experiments reveal that the proposed mechanism can effectively achieve high coverage accuracy with minimum number of deployed sensors.     

A low power tunable Gm–C filter based on double CMOS inverters in 0.35?μm

In this article, an inverter based transconductor using double CMOS pair is proposed for implementation of a second order lowpass G_m?CC Filter. The proposed operational transconductance amplifier (OTA) and biquad filter are designed using standard 0.35???m CMOS technology. Simulation results demonstrate the central frequency tunability from 10?kHz to 2.8?MHz which is suitable for the wireless specifications of Bluetooth (650?kHz), CDMA 2000 (700?kHz) and Wideband CDMA (2.2?MHz) applications. The power consumption of the filter is 445?nW and 178???W at 10?kHz and 2.8?MHz from 3.3?V supply voltage, respectively. The active area occupied by the designed filter on the silicon is 215?×?720???m². The proposed approach guarantees the upper bound on THD to be ?40?dB for 300?mVpp signal swing. Employing the double CMOS pair in the inverters causes PSRR to reach 68.6?dB which is higher than similar works.     

Photoactive Electrodes Incorporating Electrosprayed Bacterial Reaction Centers

Highly efficient light absorption and charge separation within the photosystem and reaction center (RC) complexes of photosynthetic plants and bacteria are of great interest for solar cell and photo detector applications, since they offer almost unity quantum yield and expected ultimate power conversion efficiencies of more than 18% and 12%, respectively. In addition, the charge separated states created by these protein complexes are very long lived compared to conventional semiconductor solar cells. In this work, a novel technique is presented for the deposition of photosynthetic protein complexes, by electrospraying RCs of Rhodobacter sphaeroides onto highly ordered pyrolytic graphite (HOPG) electrodes. Remarkably, it is shown that the RCs not only survive exposure to the high electric fields but also yield peak photocurrent densities of up to 7 μA cm^?2, which is equal to the highest value reported to date.     

Transient electroluminescence determination of carrier mobility and charge trapping effects in heavily doped phosphorescent organic light-emitting diodes

Transient electroluminescence (EL) was used to measure the delay between the excitation pulse and onset of emission in OLEDs based on phosphorescent bis[3,5-bis(2-pyridyl)-1,2,4-triazolato] platinum(ΙΙ) doped into 4,4′-bis(carbazol-9-yl) triphenylamine (CBP), from which an electron mobility of 3.2 × 10⁻⁶ cm²/V s was approximated. Delayed recombination was observed after the drive pulse had been removed and based on its dependence on frequency and duty cycle, ascribed to trapping and de-trapping processes associated with disorder-induced carrier localization at the interface between the emissive layer and electron blocker. The data suggests that the exciton recombination zone is at, or close to the interface between the emissive layer and electron blocker. Despite the charge trapping effects, a peak power efficiency of 24 lm/W and peak external quantum efficiency of 10.64% were obtained. Mechanisms for the electroluminescence and delayed recombination are proposed.     

Comparative study of two CPU router time management algorithms in cellular IP networks

Providing good quality of service (QoS) in cellular IP networks is an important requirement for performance improvement of the cellular IP network. Resource reservation is one of the methods used in achieving this goal and is proven to be effective. The main resources to be reserved in a cellular IP network are bandwidth, buffer and central processing unit (CPU) cycles. Router CPU cycle is the time taken by the router to process the packet of the flow before forwarding it to the next router (hop). This paper proposes a model for CPU cycle optimization of routers for real‐time flows in a cellular IP network. The model applies both genetic algorithm (GA) and particle swarm optimization (PSO) as soft computing tools to optimize the CPU cycles and reduces the flow processing time at each router in the route taken by a flow. Simulation experiments illustrate a comparative study of the model.     

A new learning automata based approach for increasing utility of service providers

Utility is an important factor for serviceproviders, and they try to increase their utilities through adopting different policies and strategies. Because of unpredictable failures in systems, there are many scenarios in which the failures may cause random losses for service providers. Loss sharing can decrease negative effects of unexpected random losses. Because of capabilities of learning automata in random and stochastic environments, in this paper, a new learning automaton based method is presented for loss sharing purpose. It is illustrated that the loss sharing can be useful for service providers and helps them to decrease negative effect of the random losses. The presented method can be used especially in collaborative environments such as federated clouds. Results of the conducted experiments show the usefulness of the presented approach to improve utility of service providers.