Biblio

The three-phase grid-connected converter control strategy, which applies to the battery energy storage system, generally ignores the interference of harmonic components in the grid voltage. As a result, it is difficult to meet the practical application requirements. To deal with this problem, it is necessary to optimize and improve the traditional control strategy, taking harmonics into consideration. And its bases are analysis of the harmonic characteristics and study of its control mechanism in the grid-connected converter. This paper proposes a method of harmonic decomposition, classifies the grid voltage harmonics and explores the control mechanism in the grid-connected converter. With the help of the simulation model built by Matlab/Simulink, the comparative simulation of the energy storage control system carried out under the control of the ideal grid voltage input and the actual one, verifies the correctness of the analytical method proposed in the article.

This paper proposes an audio watermarking algorithm having good balance between perceptual transparency, robustness, and payload. The proposed algorithm is based on Cordic QR decomposition and multi-resolution decomposition meeting all the necessary audio watermarking design requirements. The use of Cordic QR decomposition provides good robustness and use of detailed coefficients of multi-resolution decomposition help to obtain good transparency at high payload. Also, the proposed algorithm does not require original signal or the embedded watermark for extraction. The binary data embedding capacity of the proposed algorithm is 960.4 bps and the highest SNR obtained is 35.1380 dB. The results obtained in this paper show that the proposed method has good perceptual transparency, high payload and robustness under various audio signal processing attacks.

Digital images are extensively used and exchanged through internet, which gave rise to the need of establishing authorship of images. Image watermarking has provided a solution to prevent false claims of ownership of the media. Information about the owner, generally in the form of a logo, text or image is imperceptibly hid into the subject. Many transforms have been explored by the researcher community for image watermarking. Many watermarking techniques have been developed based on Singular Value Decomposition (SVD) of images. This paper analyses Singular Value Decomposition to understand its use, ability and limitations to hide additional information into the cover image for Digital Image Watermarking application.

ASA systems (firewall, IDS, IPS) are probable to become communication bottlenecks in networks with growing network bandwidths. To alleviate this issue, we suggest to use Application-aware mechanism based on Deep Packet Inspection (DPI) to bypass chosen traffic around firewalls. The services of Internet video sharing gained importance and expanded their share of the multimedia market. The Internet video should meet strict service quality (QoS) criteria to make the broadcasting of broadcast television a viable and comparable level of quality. However, since the Internet video relies on packet communication, it is subject to delays, transmission failures, loss of data and bandwidth restrictions that may have a catastrophic effect on the quality of multimedia.

With the growing number of streaming services, internet providers are increasingly needing to be able to identify the types of data and content providers that are being used on their networks. Traditional methods, such as IP and port scanning, are not always available for clients using VPNs or with providers using varying IP addresses. As such, in this paper we explore a potential method using neural networks and Markov Decision Process in order to augment deep packet inspection techniques in identifying the source and class of video streaming services.

Deep packet inspection via regular expression (RE) matching is a crucial task of network intrusion detection systems (IDSes), which secure Internet connection against attacks and suspicious network traffic. Monitoring high-speed computer networks (100 Gbps and faster) in a single-box solution demands that the RE matching, traditionally based on finite automata (FAs), is accelerated in hardware. In this paper, we describe a novel FPGA architecture for RE matching that is able to process network traffic beyond 100 Gbps. The key idea is to reduce the required FPGA resources by leveraging approximate nondeterministic FAs (NFAs). The NFAs are compiled into a multi-stage architecture starting with the least precise stage with a high throughput and ending with the most precise stage with a low throughput. To obtain the reduced NFAs, we propose new approximate reduction techniques that take into account the profile of the network traffic. Our experiments showed that using our approach, we were able to perform matching of large sets of REs from SNORT, a popular IDS, on unprecedented network speeds.

Safety is one of basic human needs so we need a security system that able to prevent crime happens. Commonly, we use surveillance video to watch environment and human behaviour in a location. However, the surveillance video can only used to record images or videos with no additional information. Therefore we need more advanced camera to get another additional information such as human position and movement. This research were able to extract those information from surveillance video footage by using human detection and tracking algorithm. The human detection framework is based on Deep Learning Convolutional Neural Networks which is a very popular branch of artificial intelligence. For tracking algorithms, channel and spatial correlation filter is used to track detected human. This system will generate and export tracked movement on footage as an additional information. This tracked movement can be analysed furthermore for another research on surveillance video problems.

Video retrieval technology faces a series of challenges with the tremendous growth in the number of videos. In order to improve the retrieval performance in efficiency and accuracy, a novel deep hash method for video data hierarchical retrieval is proposed in this paper. The approach first uses cluster-based method to extract key frames, which reduces the workload of subsequent work. On the basis of this, high-level semantical features are extracted from VGG16, a widely used deep convolutional neural network (deep CNN) model. Then we utilize a hierarchical retrieval strategy to improve the retrieval performance, roughly can be categorized as coarse search and fine search. In coarse search, we modify simHash to learn hash codes for faster speed, and in fine search, we use the Euclidean distance to achieve higher accuracy. Finally, we compare our approach with other two methods through practical experiments on two videos, and the results demonstrate that our approach has better retrieval effect.

The reliability of nuclear command, control and communications has long been identified as a critical component of the strategic stability among nuclear states. Advances in offensive cyber weaponry have the potential to negatively impact this reliability, threatening strategic stability. In this paper we present a game theoretic model of preemptive cyber attacks against nuclear command, control and communications. The model is a modification of the classic two-player game of Chicken, a standard game theoretic model for nuclear brinksmanship. We fully characterize equilibria in both the complete information game and two distinct two-sided incomplete information games. We show that when both players have advanced cyber capabilities conflict is more likely in equilibrium, regardless of information structure. On the other hand, when at most one player has advanced cyber capabilities, strategic stability depends on the information structure. Under complete information, asymmetric cyber capabilities have a stabilizing effect in which the player with strong cyber has the resolve to stand firm in equilibrium. Under incomplete information, asymmetric cyber capabilities can have both stabilizing and destabilizing effects depending on prior beliefs over opponent cyber capabilities.

Software defined networks represent a new centralized network abstraction that aims to ease configuration and facilitate applications and services deployment to manage the upper layers. However, SDN faces several challenges that slow down its implementation such as security which represents one of the top concerns of SDN experts. Indeed, SDN inherits all security matters from traditional networks and suffers from some additional vulnerability due to its centralized and unique architecture. Using traditional security devices and solutions to mitigate SDN threats can be very complicated and can negatively effect the networks performance. In this paper we propose a study that measures the impact of using some well-known security solution to mitigate intrusions on SDN's performances. We will also present an algorithm named KPG-MT adapted to SDN architecture that aims to mitigate threats such as a Man in the Middle, Deny of Services and malware-based attacks. An implementation of our algorithm based on multiple attacks' scenarios and mitigation processes will be made to prove the efficiency of the proposed framework.

In today's world the number of consumers of cloud computing is increasing day by day. So, security is a big concern for cloud computing environment to keep user's data safe and secure. Among different types of attacks in cloud one of the harmful and frequently occurred attack is Distributed Denial of Service (DDoS) attack. DDoS is one type of flooding attack which is initiated by sending a large number of invalid packets to limit the services of the victim server. As a result, server can not serve the legitimate requests. DDoS attack can be done by a lot of strategies like malformed packets, IP spoofing, smurf attack, teardrop attack, syn flood attack, local area network denial (LAND) attack etc. This paper focuses on IP spoofing and LAND based DDoS attack. The objective of this paper is to propose an algorithm to detect and prevent IP spoofing and LAND attack. To achieve this objective a new approach is proposed combining two existing solutions of DDoS attack caused by IP spoofing and ill-formed packets. The proposed approach will provide a transparent solution, filter out the spoofed packets and minimize memory exhaustion through minimizing the number of insertions and updates required in the datatable. Finally, the approach is implemented and simulated using CloudSim 3.0 toolkit (a virtual cloud environment) followed by result analysis and comparison with existing algorithms.

Software defined Network is a network defined by software, which is one of the important feature which makes the legacy old networks to be flexible for dynamic configuration and so can cater to today's dynamic application requirement. It is a programmable network but it is prone to different type of attacks due to its centralized architecture. The author provided a solution to detect and prevent Distributed Denial of service attack in the paper. Mininet [5] which is a popular emulator for Software defined Network is used. We followed the approach in which collection of the traffic statistics from the various switches is done. After collection we calculated the packet rate and bandwidth which shoots up to high values when attack take place. The abrupt increase detects the attack which is then prevented by changing the forwarding logic of the host nodes to drop the packets instead of forwarding. After this, no more packets will be forwarded and then we also delete the forwarding rule in the flow table. Hence, we are finding out the change in packet rate and bandwidth to detect the attack and to prevent the attack we modify the forwarding logic of the switch flow table to drop the packets coming from malicious host instead of forwarding it.

SDN is new networking concept which has revolutionized the network architecture in recent years. It decouples control plane from data plane. Architectural change provides re-programmability and centralized control management of the network. At the same time it also increases the complexity of underlying physical infrastructure of the network. Unfortunately, the centralized control of the network introduces new vulnerabilities and attacks. Attackers can exploit the limitation of centralized control by DDoS attack on control plane. The entire network can be compromised by DDoS attack. Based on packet entropy, a solution for mitigation of DDoS attack provided in the proposed scheme.

Software-defined networking (SDN) is a relatively new technology that promotes network revolution. The most distinct characteristic of SDN is the transformation of control logic from the basic packet forwarding equipment to a centralized management unit called controller. However, the centralized control of the network resources is like a double-edged sword, for it not only brings beneficial features but also introduces single point of failure if the controller is under distributed denial of service (DDoS) attacks. In this paper, we introduce a light-weight approach based on conditional entropy to improve the SDN security with an aim of defending DDoS at the early stage. The experimental results show that the proposed method has a high average detection rate of 99.372%.

Distributed Denial of Service (DDoS) attacks have been the prominent attacks over the last decade. A Network Intrusion Detection System (NIDS) should seamlessly configure to fight against these attackers' new approaches and patterns of DDoS attack. In this paper, we propose a NIDS which can detect existing as well as new types of DDoS attacks. The key feature of our NIDS is that it combines different classifiers using ensemble models, with the idea that each classifier can target specific aspects/types of intrusions, and in doing so provides a more robust defense mechanism against new intrusions. Further, we perform a detailed analysis of DDoS attacks, and based on this domain-knowledge verify the reduced feature set [27, 28] to significantly improve accuracy. We experiment with and analyze NSL-KDD dataset with reduced feature set and our proposed NIDS can detect 99.1% of DDoS attacks successfully. We compare our results with other existing approaches. Our NIDS approach has the learning capability to keep up with new and emerging DDoS attack patterns.

For controllers of Software Defined Network (SDN), Distributed Denial of Service (DDoS) attacks are still the simplest and most effective way to attack. Aiming at this problem, a real-time DDoS detection attack method for SDN controller is proposed. The method first uses the entropy to detect whether the flow is abnormal. After the abnormal warning is issued, the flow entry of the OpenFlow switch is obtained, and the DDoS attack feature in the SDN environment is analyzed to extract important features related to the attack. The BiLSTM-RNN neural network algorithm is used to train the data set, and the BiLSTM model is generated to classify the real-time traffic to realize the DDoS attack detection. Experiments show that, compared with other methods, this method can efficiently implement DDoS attack traffic detection and reduce controller overhead in SDN environment.

Data security is the main challenge in Internet of Things (IoT) applications. Security strength and the immunity to security attacks depend mainly on the available power budget. The power-security level trade-off is the main challenge for low power IoT applications, especially, energy limited IoT applications. In this paper, multiple encryption modes that provide different power consumption and security level values are hardware implemented. In other words, some modes provide high security levels at the expense of high power consumption and other modes provide low power consumption with low security level. Dynamic Partial Reconfiguration (DPR) is utilized to adaptively configure the hardware security module based on the available power budget. For example, for a given power constraint, the DPR controller configures the security module with the security mode that meets the available power constraint. ZC702 evaluation board is utilized to implement the proposed encryption modes using DPR. A Lightweight Authenticated Cipher (ACORN) is the most suitable encryption mode for low power IoT applications as it consumes the minimum power and area among the selected candidates at the expense of low throughput. The whole DPR system is tested with a maximum dynamic power dissipation of 10.08 mW. The suggested DPR system saves about 59.9% of the utilized LUTs compared to the individual implementation of the selected encryption modes.

Chinese Remainder Theorem (CRT) is one of the spatial domain methods that is more implemented in the data hiding method watermarking. CRT is used to improve security and imperceptibility in the watermarking method. CRT is rarely studied in studies that discuss steganographic images. Steganography research focuses more on increasing imperceptibility, embedded payload, and message security, so methods like LSB are still popular to be developed to date. CRT and LSB have some similarities such as default payload capacity and both are methods in the spatial domain which can produce good imperceptibility quality of stego image. But CRT is very superior in terms of security, so CRT is also widely used in cryptographic algorithms. Some ways to increase imperceptibility in image steganography are edge detection and spread spectrum embedding. This research proposes a combination of edge detection techniques and spread-spectrum embedding based on the CRT method to produce imperceptibility and safe image steganography method. Based on the test results it is proven that the combination of the proposed methods can increase imperceptibility of CRT-based steganography based on SSIM metric.

The amount of connected devices in the industrial environment is growing continuously, due to the ongoing demands of new features like predictive maintenance. New business models require more data, collected by IIoT edge node sensors based on inexpensive and low performance Microcontroller Units (MCUs). A negative side effect of this rise of interconnections is the increased attack surface, enabled by a larger network with more network services. Attaching badly documented and cheap devices to industrial networks often without permission of the administrator even further increases the security risk. A decent method to monitor the network and detect “unwanted” devices is network scanning. Typically, this scanning procedure is executed by a computer or server in each sub-network. In this paper, we introduce network scanning and mapping as a building block to scan directly from the Industrial Internet of Things (IIoT) edge node devices. This module scans the network in a pseudo-random periodic manner to discover devices and detect changes in the network structure. Furthermore, we validate our approach in an industrial testbed to show the feasibility of this approach.

In the current society, people pay more and more attention to identity security, especially in the case of some highly confidential or personal privacy, one-to-one identification is particularly important. The iris recognition just has the characteristics of high efficiency, not easy to be counterfeited, etc., which has been promoted as an identity technology. This paper has carried out research on daugman algorithm and iris edge detection.

The Internet of Things (IoT) is connecting the world in a way humanity has never seen before. With applications in healthcare, agricultural, transportation, and more, IoT devices help in bridging the gap between the physical and the virtual worlds. These devices usually carry sensitive data which requires security and protection in transit and rest. However, the limited power and energy consumption make it harder and more challenging to implementing security protocols, especially Public-Key Cryptosystems (PKC). In this paper, we present a hardware/software co-design for Elliptic-Curve Cryptography (ECC) PKC suitable for lightweight devices. We present the implementation results for our design on an edge node to be used for indoor localization in a healthcare facilities.

Withgrowing times and technology, and the data related to it is increasing on daily basis and so is the daunting task to manage it. The present solution to this problem i.e our present databases, are not the long-term solutions. These data volumes need to be stored safely and retrieved safely to use. This paper presents an overview of security issues for big data. Big Data encompasses data configuration, distribution and analysis of the data that overcome the drawbacks of traditional data processing technology. Big data manages, stores and acquires data in a speedy and cost-effective manner with the help of tools, technologies and frameworks.

Digital image security is now a severe issue, especially when sending images to telecommunications networks. There are many ways where digital images can be encrypted and decrypted from secure communication. Digital images contain data that is important when captured or disseminated to preserve and preserve data. The technique of encryption is one way of providing data on digital images. A key cipher block chaining and Gingerbreadman Map are used in our search to encrypt images. This new system uses simplicity, high quality, enhanced by the vehicle's natural efficiency and the number of the chain. The proposed method is performed for experimental purposes and the experiments are performed in- depth, highly reliable analysis. The results confirm that by referring to several known attacks, the plan cannot be completed. Comparative studies with other algorithms show a slight rise in the security of passwords with the advantages of security of the chain. The results of this experiment are a comparison of button sensitivity and a comparison after encryption and decryption of the initial image using the amount of pixel change rate and unified average change intensity.

The main objective of this paper is to present a more secured and computationally efficient procedure of encrypting and decrypting images using the enigma algorithm in comparison to the existing methods. Available literature on image encryptions and descriptions are not highly secured in every case.To achieve more secured image processing for highly advanced technologies, a proposed algorithm can be the process used in enigma machine for image encryption and decryption. Enigma machine is piece of spook hardware that was used frequently during the World War II by the Germans. This paper describes the detailed algorithm along with proper demonstration of several essential components present in an enigma machine that is required for image security. Each pixel in a colorful picture can be represented by RGB (Red, Green, Blue) value. The range of RGB values is 0 to 255 that states the red, green and blue intensity of a particular picture.These RGB values are accessed one by one and changed into another by various steps and hence it is not possible to track the original RGB value. In order to retrieve the original image, the receiver needs to know the setting of the enigma. To compare the decrypted image with the original one,these two images are subtracted and their results are also discussed in this paper.

Cloud Computing is the most promising paradigm in recent times. It offers a cost-efficient service to individual and industries. However, outsourcing sensitive data to entrusted Cloud servers presents a brake to Cloud migration. Consequently, improving the security of data access is the most critical task. As an efficient cryptographic technique, Ciphertext Policy Attribute Based Encryption(CP-ABE) develops and implements fine-grained, flexible and scalable access control model. However, existing CP-ABE based approaches suffer from some limitations namely revocation, data owner overhead and computational cost. In this paper, we propose a sliced revocable solution resolving the aforementioned issues abbreviated RS-CPABE. We applied splitting algorithm. We execute symmetric encryption with Advanced Encryption Standard (AES)in large data size and asymmetric encryption with CP-ABE in constant key length. We re-encrypt in case of revocation one single slice. To prove the proposed model, we expose security and performance evaluation.