Design of Hybrid Network Anomalies Detection System (H-NADS) Using IP Gray Space Analysis

In Network Security, there is a major issue to secure the public or private network from abnormal users. It is because each network is made up of users, services and computers with a specific behavior that is also called as heterogeneous system. To detect abnormal users, anomaly detection system (ADS) is used. In this paper, we present a novel and hybrid Anomaly Detection System with the uses of IP gray space analysis and dominant scanning port identification heuristics used to detect various anomalous users with their potential behaviors. This methodology is the combination of both statistical and rule based anomaly detection which detects five types of anomalies with their three types of potential behaviors and generates respective alarm messages to GUI.Network Security, Anomaly Detection, Suspicious Behaviors Detection

A New Approach for Handling Null Values in Web Log Using KNN and Tabu Search KNN

Abstract When the data mining procedures deals with the extraction of interesting knowledge from web logs is known as Web usage mining. The result of any mining is successful, only if the dataset under consideration is well preprocessed. One of the important preprocessing steps is handling of null/missing values. Handlings of null values have been a great bit of test for researcher. Various methods are available for estimation of null value such as k-means clustering algorithm, MARE algorithm and fuzzy logic approach. Although all these process are not always efficient. We propose an efficient approach for handling null values in web log. We are using a hybrid tabu search – k nearest neighbor classifier with multiple distance function. Tabu search – KNN classifier perform feature selection of K-NN rules. We are handling null values efficiently by using different distance function. It is called Ensemble of function. It gives different set of feature vector. Feature selection is useful for improving the classification accuracy of NN rule. We are using different distance metric with different set of feature, so it reduces the possibility that some error will common. Therefore, proposed method is better for handling null values. The proposed method is using hybrid classifier with different distance metrics and different feature vector. It is evaluated using our MANIT database. Results have indicated that a significant increase in the performance when compared with simple K-NN classifier. Original Source URL : http://aircconline.com/ijdkp/V1N5/0911ijdkp02.pdf For more details : http://airccse.org/journal/ijdkp/vol1.htm

Min Max Normalization Based Data Perturbation Method for Privacy Protection

Data mining system contain large amount of private and sensitive data such as healthcare, financial and criminal records. These private and sensitive data can not be share to every one, so privacy protection of data is required in data mining system for avoiding privacy leakage of data. Data perturbation is one of the best methods for privacy preserving. We used data perturbation method for preserving privacy as well as accuracy. In this method individual data value are distorted before data mining application. In this paper we present min max normalization transformation based data perturbation. The privacy parameters are used for measurement of privacy protection and the utility measure shows the performance of data mining technique after data distortion. We performed experiment on real life dataset and the result show that min max normalization transformation based data perturbation method is effective to protect confidential information and also maintain the performance of data mining technique after data distortion

Securing Data Using JPEG Image Over Mobile Phone

In recent past years, Internet and Mobile is widely used for communication. Multimedia messaging (MMS) and Short Service Messaging (SMS) are the popular services provided by the telecommunication companies. In MMS we can easily send picture with text message. In SMS we can send text only. These techniques make the communication so fast. As well as the communication became easy attention toward information security increased. Data Security is the main concern for research. Mostly used techniques for secure communication are Cryptography and Steganography. There are so many techniques for steganography and cryptography. Mostly used techniques are image steganography and there are so many algorithms for this. For the cryptography mainly AES techniques is being used. In this paper we are presenting a technique using cryptography and steganography for securing information over mobile in MMS. It is very common practice to hide data in LSB of pixel. Spatial and frequency domains are generally used for image processing. Spatial domain have so many computations comparatively frequency domain. There different transform techniques are used for transformation e.g. DCT, FFT and wavelets. Here we are using Discrete Cosine transform (DCT) for image steganography and tiny encryption algorithm for cryptography. Tiny encryption algorithm (TEA) is block cipher algorithm.It is simple and fast but best for mobile application

Development and Evaluation of Combined Drug Formulation for Autoject-injector, for Emergency Application in Organophosphate Poisoning

Atropine sulphate and pralidoxime chloride are considered as essential antidotes in the treatment of nerve agent poisoning. Now in India these antidotes are available in the form of self injectable autoinjectors. This study is designed with aim to replace two individual autoinjectors with single one. Stability of the components plays a vital role in the development of any dosage form, in this study we investigated the stability of the antidotes in combination (atropine sulphate+2 PAMCl) in single drug cartridges. In the present work shelf life of pralidoxime chloride (300 mg/ml) and atropine sulphate (1 mg/ml) solution in combination was evaluated by accelerated studies. The derived model is based on the rate equation and Arrhenius equation was used for extrapolation. Further, antidotal efficacy of atropine sulphate in vitro, using rat’s isolated ileum and pralidoxime chloride by survival studies in vivo against dichlorvas in mice were evaluated, for further confirmation of analytical findings. The constituted formulation was found to be stable for 24 months.Defence Science Journal, 2012, 62(2), pp.105-111, DOI:http://dx.doi.org/10.14429/dsj.62.113

AN IMPROVEMENT OF AODV PROTOCOL BASED ON THREADED ROUTING IN MOBILE AD HOC NETWORKS

The Mobile Ad hoc NETworks (MANETS) are suitable to be utilized in the context of an extreme emergency conditions such as battle field and disaster recovery because less requirement of infrastructure and dynamic nature. These, along with applications of these networks in military, government and in commercial area, MANETs are being researched by many organizations and institutes. Many protocols have been developed for data link layer and network layer. Many researchers have been conducted numerous simulations for comparing the performance of these protocols under varying conditions and constraints. The widely-used protocol in Mobile Ad hoc Network (MANET) achieves a dynamic, self-organizing and on-demand multi-hop routing by means of the AODV routing protocol. MANETs are characterized by self-organized, dynamic changes of network topology, limited bandwidth, and instability of link capacity, etc. The reliability of data transmission in the network cannot be guaranteed, in some special application conditions with harsh requirements on Packet Delivery Ratio (PDR) and link quality, higher criteria for routing protocol will have been laid out. This paper presents an AODV with threaded routing (AODV-FLTR) for security purpose. To reduce routing overhead and to increase packet delivery ratio, local route discovery process is used when link breaks during transmission. The performance comparison of AODV-FLTR with DSR and AODV is also carried out

TRUST BASED SECURE AODV IN MANET

Abstract- The nature of self-organization and the limitation of individual resources, MANET always confront security and selfishness issues. In this thesis, we design trusted routing protocols using trusted frame works and intrusion detection system (secure protocol) f or MANET. Trust combination algorithms and trust mapping functions are provided in this model, where the former can aggregate different opinions together to get a new recommendation opinion. Based on this trust model, we design our trusted routing protocols for MANET called TAODV on top of Ad Hoc On-demand Distance Vector (AODV) routing protocol. We extend the routing table and the routing messages of ADOV with trust information which can be updated directly through monitoring in the neighborhood. When performing trusted routing discovery, unlike those cryptographic schemes that perform signature generation or verification at every routing packet, we just combine the recommen ded opinions together and make a routing judgment based on each element of the new opinion. In this way the computation overhead can be largely reduced, and the trustworthiness of the routing procedure can be guaranteed as well. In this thesis, we implement the security and sel fishness issues of wireless networks, either in non-cooperative form or in cooperative form. Our results show that the cumulative utilities of cooperative nodes are increased steadily and the selfish nodes cannot get more utilities by behaving selfishly than cooperatively

Cognitive Visual Tracking of Hand Gestures in Real-Time RGB Videos

Real-time visual hand tracking is quite different from commonly tracked objects in RGB videos. Because the hand is a biological object and hence suffers from both physical and behavioral variations during its movement. Furthermore, the hand acquires a very small area in the image frame, and due to its erratic pattern of movement, the quality of images in the video is affected considerably, if recorded from a simple RGB camera. In this chapter, we propose a hybrid framework to track the hand movement in RGB video sequences. The framework integrates the unique features of the Faster Region-based Convolutional Neural Network (Faster R-CNN) built on Residual Network and Scale-Invariant Feature Transform (SIFT) algorithm. This combination is enriched with the discriminative learning power of deep neural networks and the fast detection capability of hand-crafted features SIFT. Thus, our method online adapts the variations occurring in real-time hand movement and exhibits high efficiency in cognitive recognition of hand trajectory. The empirical results shown in the chapter demonstrate that the approach can withstand the intrinsic as well as extrinsic challenges associated with visual tracking of hand gestures in RGB videos