DDoS Detection in SDN using Machine Learning Techniques

Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of the SDN that has a network-wide impact. Machine learning is now widely used for fast detection of these attacks. In this paper, some important feature selection methods for machine learning on DDoS detection are evaluated. The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller. A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks. The experimental results show that the Random forest (RF) classifier trains the more accurate model with 99.97% accuracy using features subset by the Recursive feature elimination (RFE) method.     

Diffusion Based Channel Gains Estimation in WSN Using Fractional Order Strategies

In this study, it is proposed that the diffusion least mean square (LMS) algorithm can be improved by applying the fractional order signal processing methodologies. Application of Caputo’s fractional derivatives are considered in the optimization of cost function. It is suggested to derive a fractional order variant of the diffusion LMS algorithm. The applicability is tested for the estimation of channel parameters in a distributed environment consisting of randomly distributed sensors communicating through wireless medium. The topology of the network is selected such that a smaller number of nodes are informed. In the network, a random sleep strategy is followed to conserve the transmission power at the nodes. The proposed fractional order modified diffusion LMS algorithms are applied in the two configurations of combine-then-adapt and adapt-then-combine. The average squared error performance of the proposed algorithms along with its traditional counterparts are evaluated for the estimation of the Rayleigh channel parameters. A mathematical proof of convergence is provided showing that the addition of the nonlinear term resulting from fractional derivatives helps adjusts the autocorrelation matrix in such a way that the spread of its eigenvalues decreases. This increases the convergence as well as the steady state response even for the larger step sizes. Experimental results are shown for different number of nodes and fractional orders. The simulation results establish that the accuracy of the proposed scheme is far better than its classical counterparts, therefore, helps better solves the channel gains estimation problem in a distributed wireless environment. The algorithm has the potential to be applied in other applications related to learning and adaptation.     

CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks

In cognitive radio network, the secondary users (SUs) use the spectrum of primary users for communication which arises the security issues. The status of SUs as legitimate users is compulsory for the stability of the system. This paper addresses the issue of delay caused by a band-selection decision process that directly affects the security and performance. The model cluster-based distributed cooperative spectrum sensing is proposed. In this model, cluster heads (CHs) exchange control information with other CHs and ordinary nodes. This model significantly reduced the delay, sensing, convergence, routing, in band-selection process. This also reduces the energy consumption while sensing the spectrum which seriously leads to performance upgradation. The simulated results show the improved performance of cognitive radio networks in terms of delay, packet loss ratio and bandwidth usage as compared to cluster-based cooperative spectrum sensing model. The opportunity for primary user emulation attacker is minimized as the overall delay is reduced.     

Multiple RGB images encryption algorithm based on elliptic curve,improved Diffie Hellman protocol

Multimedia Tools and Applications - For the asymmetric key, the Diffie Hellman Key (DHK) protocol is very efficient, but sometimes it is vulnerable against brute force attacks if the parameters are...     

A Novel Hybrid Method for River Discharge Prediction

Accurate prediction of river discharge is essential for the planning and management of water resources. This study proposes a novel hybrid method named HD-SKA by integrating two decomposition techniques (termed as HD) with support vector regression (SVR), K-nearest neighbor (KNN) and ARIMA models (combined as SKA) respectively. Firstly, the proposed method utilizes local mean decomposition (LMD) to decompose the original river discharge series into sub-series. Next, ensemble empirical mode decomposition (EEMD) is employed to further decompose the LMD-based sub-series into intrinsic mode functions. Further, the EEMD decomposed components are used as inputs in three data-driven models to predict river discharge respectively. The prediction of all components is then aggregated to obtain the results of HD-SVR, HD-KNN and HD-ARIMA models. The final prediction is obtained by taking the average prediction of these models. The proposed method is illustrated using five rivers in Indus Basin System. In five case studies, six models were built to compare the performance of the proposed HD-SKA model. The data analysis results show that the HD-SKA model performs better than all other considered models. The Diebold-Mariano test confirms the superiority of the proposed HD-SKA model over ARIMA, SVR, KNN, EEMD-ARIMA, EEMD-KNN, and EEMD-SVR models.

     

Synthesis,characterization, and properties of new 3‐hexyl‐2,5‐diphenylthiophene: Phenylene vinylenes copolymers as colorimetric sensor for iodide anion

Four new conjugated copolymers P1 ‐ P4 have been prepared by the Horner‐Emmons and Knoevenagel polymerization reactions. P1 ‐ P4 were characterized by NMR, FTIR, cyclic voltammetry (CV), diffuse reflectance UV–vis spectroscopy (DR UV–vis), and thermal gravimetric analysis (TGA). The optical band gaps of these polymers, calculated from the onset absorption edge, were found between 2.15 and 2.34 eV. The band gaps calculated by CV were ranged between 1.94 and 2.57 eV. The presence of nitrile moiety on the phenylene vinylene unit is believed to influence the optical properties of these polymers, i.e., P3 and P4 have shown lower band gaps than P1 and P2 . All polymers possess good iodide anion sensing property over a wide range of other anions (F^?, Cl^?, Br^?, , CN^?), indicating their promise in fabricating selective iodide sensors. The initial colorless solution of polymers in THF changed to deep yellow upon the addition of aqueous solution of iodide salts along with significant changes in the UV–vis spectra of the polymers. The limit of detection (LOD) for P1‐P4 were found between 0.43 and 2.54 mM . These polymers constitute long alkoxy and alkyl side chains, bearing excellent solubility in most common organic solvents which warrants their suitability for photovoltaic devices application. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 44948.     

Influence of steel fibres on strength and ductility of normal and lightweight high strength concrete

This paper presents the results of a series of experiments conducted to investigate the effectiveness of fibre inclusion in the improvement of mechanical performance of concrete with regard to concrete type and specimen size. Lightweight aggregate concrete and limestone aggregate concrete with and without steel fibres were used in the study. The compressive strength of the concrete mixes varied between 90 and 115 MPa and the fibre content was 1% by volume. Splitting tests on prisms and three-point bending test on notched beams were carried out on specimens of varying sizes to examine the size effect on splitting strength, flexural strength and toughness.

The experimental findings indicate that the low volume of fibre has little effect on compressive strength but improve remarkably splitting tensile strength, flexural strength and toughness. The increase in splitting tensile strength, flexural strength and toughness index for lightweight concrete seems much higher than that of normal aggregate concrete.

The size effect on prism splitting tensile strength is not significant beyond a critical (transition) size. There are apparent size effects on flexural strength and toughness index. As the specimen size increases, splitting and flexural strengths appear to decrease, and fracture behaviour tends to be more brittle.     

Flexural strengthening of reinforced lightweight polystyrene aggregate concrete beams with near-surface mounted GFRP bars

Application of near-surface mounted (NSM) fibre reinforced polymer (FRP) bars is emerging as a promising technology for increasing flexural and shear strength of deficient reinforced concrete (RC) members. In order for this technique to perform effectively, the structural behaviour of RC elements strengthened with NSM FRP bars needs to be fully characterized. This paper focuses on the characterization of flexural behaviour of RC members strengthened with NSM glass-FRP bars. Totally, 10 beams were tested using symmetrical two-point loads test. The parameters examined under the beam tests were type of concretes (lightweight polystyrene aggregate concrete and normal concrete), type of reinforcing bars (GFRP and steel), and type of adhesives. Flexural performance of the tested beams including modes of failure, moment–deflection response and ultimate moment capacity are presented and discussed in this paper. Results of this investigation showed that beams with NSM GFRP bars showed a reduction in ultimate deflection and an improvement in flexural stiffness and bending capacity, depending on the PA content of the beams. In general, beams strengthened with NSM GFRP bars overall showed a significant increase in ultimate moment ranging from 23% to 53% over the corresponding beams without NSM GFRP bars. The influence of epoxy type was found conspicuously dominated the moment–deflection response up to the peak moment. Besides, the ultimate moment of concrete beams reinforced with GFRP bars could be predicted satisfactorily using the equation provided in ACI 318-95 Building Code.     

Physical and chemical modification of distillery sludge for Pb(II) biosorption

The waste distillery sludge from sugar-cane industry was pretreated physically (boiled, heated and autoclaved) as well as chemically (HCl, H(2)SO(4), H(3)PO(4), NaOH, Ca(OH)(2), Al(OH)(3), C(6)H(6), HCHO, CH(3)OH and C1(2)H(25)OSO(3)Na (sodium dodecyl sulphate (SDS)) for assessing the comparative sorption capacity of untreated and modified distillery sludge for Pb(II) biosorption from aqueous solutions. Experiments were conducted in shake flasks on a batch basis to access the effect of different experimental parameters such as pH, biosorbent dosage, biosorbent size, initial Pb(II) concentration and contact time. The uptake capacity 'q' (mg/g) of untreated and pretreated distillery sludge was in following order: NaOH (51.29+/-1.21)>HCl (49.82+/-1.22)>HCHO (49.56+/-1.14)>H(2)SO(4) (47.71+/-1.20)>HgCl(2) (45.32+/-1.06)>Ca(OH)(2) (44.01+/-1.18)>MeOH (43.73+/-1.23)>C(6)H(6) (42.72+/-1.19)>H(3)PO(4) (42.01+/-1.17)>SDS (40.87+/-1.27)>autoclaved (40.23+/-1.24)>Boiled (39.95+/-1.19)>heated (38.87+/-1.32)>Al (OH)(3) (38.30+/-1.14)>untreated (37.76+/-1.21). In further parameter studies, the optimized biosorbent size was 0.250 mm at pH 5 and best dose was 0.05 g of biosorbent. The applicability of the Langmuir and Freundlich models for sorption process was tested and best fitted model was Langmuir with the coefficient of determination (R(2)) value, 0.97, the process followed second order kinetic mechanism.