Biblio
With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platforms, videos are widely circulated often at a high compression factor. In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenarios and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification. Using less number of frames per video to assess its realism, the metric learning approach using a triplet network architecture proves to be fruitful. It learns to enhance the feature space distance between the cluster of real and fake videos embedding vectors. We validated our approaches on two datasets to analyze the behavior in different environments. We achieved a state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of 90.71% on a highly compressed Neural Texture dataset. Our approach is especially helpful on social media platforms where data compression is inevitable.
The exchange of data has expanded utilizing the web nowadays, but it is not dependable because, during communication on the cloud, any malicious client can alter or steal the information or misuse it. To provide security to the data during transmission is becoming hot research and quite challenging topic. In this work, our proposed algorithm enhances the security of the keys by increasing its complexity, so that it can't be guessed, breached or stolen by the third party and hence by this, the data will be concealed while sending between the users. The proposed algorithm also provides more security and authentication to the users during cloud communication, as compared to the previously existing algorithm.
Data mining visualization is an important aspect of big data visualization and analysis. The impact of the nature-inspired algorithm along with the impact of computing traditions for the complete visualization of the storage and data communication needs have been studied. This paper also explores the possibilities of the hybridization of data mining in terms of association of cloud computing. It also explores the data analytical view in the exploration of these approaches in terms of data storage in big data. Based on these aspects the methodological advancement along with the problem statements has been analyzed. This will help in the exploration of computational capability along with the new insights in this domain.
Big data provides a way to handle and analyze large amount of data or complex set. It provides a systematic extraction also. In this paper a hybrid security analysis based on intelligent adaptive learning in big data has been discussed with the current trends. This paper also explores the possibility of cloud computing collaboration with big data. The advantages along with the impact for the overall platform evaluation has been discussed with the traditional trends. It has been useful in the analysis and the exploration of future research. This discussion also covers the computational variability and the connotation in terms of data reliability, availability and management in big data with data security aspects.
Advent of Cyber has converted the entire World into a Global village. But, due to vurneabilites in SCADA architecture [1] national assests are more prone to cyber attacks.. Cyber invasions have a catastrophic effect in the minds of the civilian population, in terms of states security system. A robust cyber security is need of the hour to protect the critical information infastructrue & critical infrastructure of a country. Here, in this paper we scrutinize cyber terrorism, vurneabilites in SCADA network systems [1], [2] and concept of cyber resilience to combat cyber attacks.
Machine-to-Machine (M2M) networks being connected to the internet at large, inherit all the cyber-vulnerabilities of the standard Information Technology (IT) systems. Since perfect cyber-security and robustness is an idealistic construct, it is worthwhile to design intrusion detection schemes to quickly detect and mitigate the harmful consequences of cyber-attacks. Volumetric anomaly detection have been popularized due to their low-complexity, but they cannot detect low-volume sophisticated attacks and also suffer from high false-alarm rate. To overcome these limitations, feature-based detection schemes have been studied for IT networks. However these schemes cannot be easily adapted to M2M systems due to the fundamental architectural and functional differences between the M2M and IT systems. In this paper, we propose novel feature-based detection schemes for a general M2M uplink to detect Distributed Denial-of-Service (DDoS) attacks, emergency scenarios and terminal device failures. The detection for DDoS attack and emergency scenarios involves building up a database of legitimate M2M connections during a training phase and then flagging the new M2M connections as anomalies during the evaluation phase. To distinguish between DDoS attack and emergency scenarios that yield similar signatures for anomaly detection schemes, we propose a modified Canberra distance metric. It basically measures the similarity or differences in the characteristics of inter-arrival time epochs for any two anomalous streams. We detect device failures by inspecting for the decrease in active M2M connections over a reasonably large time interval. Lastly using Monte-Carlo simulations, we show that the proposed anomaly detection schemes have high detection performance and low-false alarm rate.
For sharing resources using ad hoc communication MANET are quite effective and scalable medium. MANET is a distributed, decentralized, dynamic network with no fixed infrastructure, which are self- organized and self-managed. Achieving high security level is a major challenge in case of MANET. Layered architecture is one of the ways for handling security challenges, which enables collection and analysis of data from different security dimensions. This work proposes a novel multi-layered outlier detection algorithm using hierarchical similarity metric with hierarchical categorized data. Network performance with and without the presence of outlier is evaluated for different quality-of-service parameters like percentage of APDR and AT for small (100 to 200 nodes), medium (200 to 1000 nodes) and large (1000 to 3000 nodes) scale networks. For a network with and without outliers minimum improvements observed are 9.1 % and 0.61 % for APDR and AT respectively while the maximum improvements of 22.1 % and 104.1 %.
The base station (BS) is the main device in a wireless sensor network (WSN) and used to collect data from all the sensor nodes. The information of the whole network is stored in the BS and hence it is always targeted by the adversaries who want to interrupt the operation of the network. The nodes transmit their data to the BS using multi-hop technique and hence form an eminent traffic pattern that can be easily observed by a remote adversary. The presented research aims to increase the anonymity of the BS. The proposed scheme uses a mobile BS and ring nodes to complete the above mentioned objective. The simulation results show that the proposed scheme has superior outcomes as compared to the existing techniques.
In the development of smart cities across the world VANET plays a vital role for optimized route between source and destination. The VANETs is based on infra-structure less network. It facilitates vehicles to give information about safety through vehicle to vehicle communication (V2V) or vehicle to infrastructure communication (V2I). In VANETs wireless communication between vehicles so attackers violate authenticity, confidentiality and privacy properties which further effect security. The VANET technology is encircled with security challenges these days. This paper presents overview on VANETs architecture, a related survey on VANET with major concern of the security issues. Further, prevention measures of those issues, and comparative analysis is done. From the survey, found out that encryption and authentication plays an important role in VANETS also some research direction defined for future work.
Image Denoising nowadays is a great Challenge in the field of image processing. Since Discrete wavelet transform (DWT) is one of the powerful and perspective approaches in the area of image de noising. But fixing an optimal threshold is the key factor to determine the performance of denoising algorithm using (DWT). The optimal threshold can be estimated from the image statistics for getting better performance of denoising in terms of clarity or quality of the images. In this paper we analyzed various methods of denoising from the sonar image by using various thresholding methods (Vishnu Shrink, Bayes Shrink and Neigh Shrink) experimentally and compare the result in terms of various image quality parameters. (PSNR,MSE,SSIM and Entropy). The results of the proposed method show that there is an improvenment in the visual quality of sonar images by suppressing the speckle noise and retaining edge details.
The recent trend of mobile ad hoc network increases the ability and impregnability of communication between the mobile nodes. Mobile ad Hoc networks are completely free from pre-existing infrastructure or authentication point so that all the present mobile nodes which are want to communicate with each other immediately form the topology and initiates the request for data packets to send or receive. For the security perspective, communication between mobile nodes via wireless links make these networks more susceptible to internal or external attacks because any one can join and move the network at any time. In general, Packet dropping attack through the malicious node (s) is one of the possible attack in the mobile ad hoc network. This paper emphasized to develop an intrusion detection system using fuzzy Logic to detect the packet dropping attack from the mobile ad hoc networks and also remove the malicious nodes in order to save the resources of mobile nodes. For the implementation point of view Qualnet simulator 6.1 and Mamdani fuzzy inference system are used to analyze the results. Simulation results show that our system is more capable to detect the dropping attacks with high positive rate and low false positive.
Vehicular ad-hoc networks (VANETs) provides infrastructure less, rapidly deployable, self-configurable network connectivity. The network is the collection vehicles interlinked by wireless links and willing to store and forward data for their peers. As vehicles move freely and organize themselves arbitrarily, message routing is done dynamically based on network connectivity. Compared with other ad-hoc networks, VANETs are particularly challenging due to the part of the vehicles' high rate of mobility and the numerous signal-weakening barrier, such as buildings, in their environments. Due to their enormous potential, VANET have gained an increasing attention in both industry and academia. Research activities range from lower layer protocol design to applications and implementation issues. A secure VANET system, while exchanging information should protect the system against unauthorized message injection, message alteration, eavesdropping. The security of VANET is one of the most critical issues because their information transmission is propagated in open access (wireless) environments. A few years back VANET has received increased attention as the potential technology to enhance active and preventive safety on the road, as well as travel comfort Safekeeping and privacy are mandatory in vehicular communications for a grateful acceptance and use of such technology. This paper is an attempt to highlight the problems occurred in Vehicular Ad hoc Networks and security issues.
With the advent of World Wide Web, information sharing through internet increased drastically. So web applications security is today's most significant battlefield between attackers and resources of web service. It is likely to remain so for the foreseeable future. By considering recent attacks it has been found that major attacks in Web Applications have been carried out even when system having most significant network level security. Poor input validation mechanisms that using in Web Applications shall causes to launching vulnerable web applications, which easy to exploit easy in future stages. Critical Web Application Vulnerabilities like Cross Site Scripting (XSS) and Injections (SQL, PHP, LDAP, SSL, XML, Command, and Code) are happen because of base level Validations, and it is enough to update system in unauthorized way or may be causes to exploit the system. In this paper we present those issues in data validations strategies, to avoid deployment of vulnerable web applications.