Biblio

With the advent of the network economy and the network society, the network will enter a ubiquitous and omnipresent situation. Economic, cultural, military and social life will strongly depend on the network, while network security issues have become a common concern of all countries in the world. DDOS attack is undoubtedly one of the greatest threats to network security and the defense against DDOS attack is very important. In this paper, the principle of DDOS attack is summarized from the defensive purpose. Then the attack prevention in software definition network is analyzed, and the source, intermediate network, victim and distributed defense strategies are elaborated.

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.

The application of the concept of software-defined networks (SDN) has, on the one hand, led to the simplification and reduction of switches price, and on the other hand, has created a significant number of problems related to the security of the SDN network. In several studies was noted that these problems are related to the lack of flexibility and programmability of the data plane, which is likely first to suffer potential denial-of-service (DoS) attacks. One possible way to overcome this problem is to increase the flexibility of the data plane by increasing the depth of programmability of the packet-switching nodes below the level of flow table management. Therefore, this paper investigates the opportunity of using the architecture of deeply programmable packet-switching nodes (DPPSN) in the implementation of a firewall. Then, an architectural model of the firewall based on a hybrid FPGA/CPU data plane architecture has been proposed and implemented. Realized firewall supports three models of DoS attacks mitigation: DoS traffic filtering on the output interface, DoS traffic filtering on the input interface, and DoS attack redirection to the honeypot. Experimental evaluation of the implemented firewall has shown that DoS traffic filtering at the input interface is the best strategy for DoS attack mitigation, which justified the application of the concept of deep network programmability.

Content Delivery Networks(CDN) is a standout amongst the most encouraging innovations that upgrade performance for its clients' websites by diverting web demands from browsers to topographically dispersed CDN surrogate nodes. However, due to the variable nature of CDN, it suffers from various security and resource allocation issues. The most common attack which is used to bring down a whole network as well as CDN without even finding a loophole in the security is DDoS. In this proposal, we proposed a distributed virtual honeypot model for diminishing DDoS attacks and prevent intrusion in securing CDN. Honeypots are specially utilized to imitate the primary server with the goal that the attack is alleviated to the fake rather than the main server. Our proposed layer based model utilizes honeypot to be more effective reducing the cost of the system as well as maintaining the smooth delivery in geographically dispersed servers without performance degradation.

A DDoS attack is a spiteful attempt to disrupt legitimate traffic to a server by overwhelming the target with a flood of requests from geographically dispersed systems. Today attackers prefer DDoS attack methods to disrupt target services as they generate GBs to TBs of random data to flood the target. In existing mitigation strategies, because of lack of resources and not having the flexibility to cope with attacks by themselves, they are not considered to be that effective. So effective DDoS mitigation techniques can be provided using emerging technologies such as blockchain and SDN(Software-Defined Networking). We propose an architecture where a smart contract is deployed in a private blockchain, which facilitates a collaborative DDoS mitigation architecture across multiple network domains. Blockchain application is used as an additional security service. With Blockchain, shared protection is enabled among all hosts. With help of smart contracts, rules are distributed among all hosts. In addition, SDN can effectively enable services and security policies dynamically. This mechanism provides ASes(Autonomous Systems) the possibility to deploy their own DPS(DDoS Prevention Service) and there is no need to transfer control of the network to the third party. This paper focuses on the challenges of protecting a hybridized enterprise from the ravages of rapidly evolving Distributed Denial of Service(DDoS) attack.

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%.

The growing number of internet based services and applications along with increasing adoption rate of connected wired and wireless devices presents opportunities as well as technical challenges and threads. Distributed Denial of Service (DDoS) attacks have huge devastating effects on internet enabled services. It can be implemented diversely with a variety of tools and codes. Therefore, it is almost impossible to define a single solution to prevent DDoS attacks. The available solutions try to protect internet services from DDoS attacks, but there is no accepted best-practice yet to this security breach. On the other hand, distinguishing DDoS attacks from analogous Flash Events (FEs) wherein huge number of legitimate users try to access a specific internet based services and applications is a tough challenge. Both DDoS attacks and FEs result in unavailability of service, but they should be treated with different countermeasures. Therefore, it is worthwhile to investigate novel methods which can detect well disguising DDoS attacks from similar FE traffic. This paper will contribute to this topic by proposing a hybrid DDoS and FE detection scheme; taking 3 isolated approaches including Kernel Online Anomaly Detection (KOAD), Shannon Entropy and Mahalanobis Distance. In this study, Shannon entropy is utilized with an online machine learning technique to detect abnormal traffic including DDoS attacks and FE traffic. Subsequently, the Mahalanobis distance metric is employed to differentiate DDoS and FE traffic. the purposed method is validated using simulated DDoS attacks, real normal and FE traffic. The results revealed that the Mahalanobis distance metric works well in combination with machine learning approach to detect and discriminate DDoS and FE traffic in terms of false alarms and detection rate.

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.

Distributed Denial of Service (DDoS) attack is one of the major threats to the network services. In this paper, we propose a DDoS attack detection algorithm based on the probability distributions of source IP addresses and destination IP addresses. According to the behavior of source and destination IP addresses during DDoS attack, the distance between these features is calculated and used.It is calculated with using the Greedy algorithm which eliminates some requirements associated with Kullback-Leibler divergence such as having the same rank of the probability distributions. Then frequency modulation is proposed in the detection phase to reduce false alarm rates and to avoid using static threshold. This algorithm is tested on the real data collected from Boğaziçi University network.

It is important to provide strong security for IoT devices with limited security related resources. We introduce a new dynamic security agent management framework, which dynamically chooses the best security agent to support security functions depending on the applications' security requirements of IoT devices in the system. This framework is designed to overcome the challenges including high computation costs, multiple security protocol compatibility, and efficient energy management in IoT system.

We consider the problem of attack detection for IoT networks based only on passively collected network parameters. For the first time in the literature, we develop a blind attack detection method based on data conformity evaluation. Network parameters collected passively, are converted to their conformity values through iterative projections on refined L1-norm tensor subspaces. We demonstrate our algorithmic development in a case study for a simulated star topology network. Type of attack, affected devices, as well as, attack time frame can be easily identified.

Nowadays, secure transmission of multimedia files has become more significant concern with the evolution of technologies. Cryptography is the well-known technique to safeguard the files from various destructive hacks. In this work, a colour image encryption scheme is suggested using chaos and Deoxyribo Nucleic Acid (DNA) coding. The encryption scheme is carried out in two stages namely confusion and diffusion. As the first stage, chaos aided inter-planar row and column shuffling are performed to shuffle the image pixels completely. DNA coding and decoding operations then diffuse the resultant confused image with the help of eight DNA XOR rules. This confusion-diffusion process has achieved the entropy value equal to 7.9973 and correlation coefficient nearer to zero with key space of 10140. Various other analyses are also done to ensure the effectiveness of the developed algorithm. The results show that the proposed scheme can withstand different attacks and better than the recent state-of-art methods.

Unauthorized access of the data is one of the major threat for the real world digital data communication. Digital images are one of the most vital subset of the digital data. Several important and sensitive information is conveyed through digital images. Hence, digital image security is one of the foremost interest of the researchers. Cryptographic algorithms Biological sequences are often used to encrypt data due to their inherent features. DNA encryption is one of the widely used method used for data security which is based on the properties of the biological sequences. To protect the images from unwanted accesses, a new two stage method is proposed in this work. DNA Encryption and Polymerase Chain Reaction (PCR) Amplification is used to enhance the security. The proposed method is evaluated using different standard parameters that shows the efficiency of the algorithm.

Named Data Networking (NDN) is a new network architecture that has been projected as the future of internet architecture. Unlike the traditional internet approach which currently relies on client-server communication models to communicate each other, NDN relies on data as an entity. Hence the users only need the content and applications based on data naming, as there is no IP addresses needed. NDN is different than TCP/IP technology as NDN signs the data with Digital Signature to secure each data authenticity. Regarding huge number of uses on IP-based network, and the minimum number of NDN-based network implementation, the NDN-IP gateway are needed to map and forward the data from IP-based network to NDN-based network, and vice versa. These gateways are called Custom-Router Gateway in this study. The Custom-Router Gateway requires a new mechanism in conducting Digital Signature so that authenticity the data can be verified when it passes through the NDN-IP Custom-Router Gateway. This study propose a method to process the Digital Signature for the packet flows from IP-based network through NDN-based network. Future studies are needed to determine the impact of Digital Signature processing on the performance in forwarding the data from IP-based to NDN-based network and vice versa.

This paper proposes a new random forest classification algorithm based on differential privacy protection. In order to reduce the impact of differential privacy protection on the accuracy of random forest classification, a hybrid decision tree algorithm is proposed in this paper. The hybrid decision tree algorithm is applied to the construction of random forest, which balances the privacy and classification accuracy of the random forest algorithm based on differential privacy. Experiment results show that the random forest algorithm based on differential privacy can provide high privacy protection while ensuring high classification performance, achieving a balance between privacy and classification accuracy, and has practical application value.

Differential privacy, which is due to its rigorous mathematical proof and strong privacy guarantee, has become a standard for the release of statistics with privacy protection. Recently, a lot of dynamic data publishing algorithms based on differential privacy have been proposed, but most of the algorithms use a native method to allocate the privacy budget. That is, the limited privacy budget is allocated to each time point uniformly, which may result in the privacy budget being unreasonably utilized and reducing the utility of data. In order to make full use of the limited privacy budget in the dynamic data publishing and improve the utility of data publishing, we propose a dynamic data publishing algorithm based on reinforcement learning in this paper. The algorithm consists of two parts: privacy budget allocation and data release. In the privacy budget allocation phase, we combine the idea of reinforcement learning and the changing characteristics of dynamic data, and establish a reinforcement learning model for the allocation of privacy budget. Finally, the algorithm finds a reasonable privacy budget allocation scheme to publish dynamic data. In the data release phase, we also propose a new dynamic data publishing strategy to publish data after the privacy budget is exhausted. Extensive experiments on real datasets demonstrate that our algorithm can allocate the privacy budget reasonably and improve the utility of dynamic data publishing.

Deep learning is a popular powerful machine learning solution to the computer vision tasks. The most criticized vulnerability of deep learning is its poor tolerance towards adversarial images obtained by deliberately adding imperceptibly small perturbations to the clean inputs. Such negatives can delude a classifier into wrong decision making. Previous defensive techniques mostly focused on refining the models or input transformation. They are either implemented only with small datasets or shown to have limited success. Furthermore, they are rarely scrutinized from the hardware perspective despite Artificial Intelligence (AI) on a chip is a roadmap for embedded intelligence everywhere. In this paper we propose a new discriminative noise injection strategy to adaptively select a few dominant layers and progressively discriminate adversarial from benign inputs. This is made possible by evaluating the differences in label change rate from both adversarial and natural images by injecting different amount of noise into the weights of individual layers in the model. The approach is evaluated on the ImageNet Dataset with 8-bit truncated models for the state-of-the-art DNN architectures. The results show a high detection rate of up to 88.00% with only approximately 5% of false positive rate for MobileNet. Both detection rate and false positive rate have been improved well above existing advanced defenses against the most practical noninvasive universal perturbation attack on deep learning based AI chip.

Security challenges present in Machine-to-Machine Communication (M2M-C) and big data paradigm are fundamentally different from conventional network security challenges. In M2M-C paradigms, “Trust” is a vital constituent of security solutions that address security threats and for such solutions,it is important to quantify and evaluate the amount of trust in the information and its source. In this work, we focus on Machine Learning (ML) Based Trust (MLBT) evaluation model for detecting malicious activities in a vehicular Based M2M-C (VBM2M-C) network. In particular, we present an Entropy Based Feature Engineering (EBFE) coupled Extreme Gradient Boosting (XGBoost) model which is optimized with Binary Particle Swarm optimization technique. Based on three performance metrics, i.e., Accuracy Rate (AR), True Positive Rate (TPR), False Positive Rate (FPR), the effectiveness of the proposed method is evaluated in comparison to the state-of-the-art ensemble models, such as XGBoost and Random Forest. The simulation results demonstrates the superiority of the proposed model with approximately 10% improvement in accuracy, TPR and FPR, with reference to the attacker density of 30% compared with the start-of-the-art algorithms.

We demonstrate secure fiber-optic transmission utilizing quantum-noise signal masking by 217-level random phase modulation. Masking of 157 signal phase levels at a BER of HD-FEC threshold is achieved without significant impacts on the transmission performance.

The general idea to achieve error detection and correction is to add some extra bit to an original message, in which the receiver can use to check the flexibility of the message which has been delivered, and to recover the noisy data. Turbo code is one of the forward error correction method, which is able to achieve the channel capacity, with nearer Shannon limit, encoding and decoding of text and images are performed. Methods and the working have been explained in this paper. The error has also introduced and detection and correction of errors have been achieved. Transmission will be secure it can secure the information by the theft.

Recently, hashing has attracted considerable attention for nearest neighbor search due to its fast query speed and low storage cost. However, existing unsupervised hashing algorithms have two problems in common. Firstly, the widely utilized anchor graph construction algorithm has inherent limitations in local weight estimation. Secondly, the locally linear structure in the original feature space is seldom taken into account for binary encoding. Therefore, in this paper, we propose a novel unsupervised hashing method, dubbed “discrete locally-linear preserving hashing”, which effectively calculates the adjacent matrix while preserving the locally linear structure in the obtained hash space. Specifically, a novel local anchor embedding algorithm is adopted to construct the approximate adjacent matrix. After that, we directly minimize the reconstruction error with the discrete constrain to learn the binary codes. Experimental results on two typical image datasets indicate that the proposed method significantly outperforms the state-of-the-art unsupervised methods.

The objective of this research is to develop a novel image encryption method that can be used to considerably increase the security of encrypted images. To solve this image security problem, we propose a distributed homomorphic image encryption scheme where the images of interest are those in the visible electromagnetic spectrum. In our encryption phase, a red green blue (RGB) image is first separated into its constituent channel images, and then the numerical intensity value of a pixel from each channel is written as a sum of smaller pixel intensity sub-values, leading to having several component images for each of the R, G, and B-channel images. A homomorphic encryption function is used to separately encrypted each of the pixel intensity sub-values in each component image using an encryption key, leading to a distributed image encryption approach. Each of the encrypted component images can be compressed before transmission and/or storage. In our decryption phase, each encrypted component image is decompressed if necessary, and then the homomorphic property of the encryption function is used to transform the product of individually encrypted pixel intensity sub-values in each encrypted component images, to the encryption of their sum, before applying the corresponding decryption function with a decryption key to recover the original pixel's intensity values for each channel image, and then recovering the original RGB image. Furthermore, a special case of an RGB image encryption and decryption where a pixel's intensity value from each channel is written as a sum of only two sub-values is implemented and simulated with a software. The resulting cipher-images are subject to a range of security tests and analyses. Results from these tests shown that our proposed homomorphic image encryption scheme is robust and can resist security attacks, as well as increases the security of the associated encrypted images. Our proposed homomorphic image encryption scheme has produced highly secure encrypted images.

This paper focuses on the creation of information centric Cyber-Human Learning Frameworks involving Virtual Reality based mediums. A generalized framework is proposed, which is adapted for two educational domains: one to support education and training of residents in orthopedic surgery and the other focusing on science learning for children with autism. Users, experts and technology based mediums play a key role in the design of such a Cyber-Human framework. Virtual Reality based immersive and haptic mediums were two of the technologies explored in the implementation of the framework for these learning domains. The proposed framework emphasizes the importance of Information-Centric Systems Engineering (ICSE) principles which emphasizes a user centric approach along with formalizing understanding of target subjects or processes for which the learning environments are being created.

Digital forensics is process of identifying, preserving, analyzing and preserving digital evidence. Due to increasing cybercrimes now a days, it is important for all organizations to have a well-managed digital forensics cell. So to overcome this, we propose a framework called digital forensics capability analyser. [1]The main advantage of developing digital analyzer is cost minimization. This tool will provide fundamental information for setting up a digital forensic cell and will also offer services like online sessions. [2] [3]It will help organizations by providing them with a perfect solution according to their requirements to start a digital forensic cell in their respective lnstitution.[4] [5].