Biblio

Conditional Generative Adversarial Nets [1](cGAN) was recently proposed as a novel conditional learning method by feeding some extra information into the network. In this paper we propose an improved conditional GANs which use divided z-vector (DzGAN). The computation amount will be reduced because DzGAN can implement conditional learning using not images but one-hot vector by dividing the range of z-vector (e.g. -1\textasciitilde1 to -1\textasciitilde0 and 0\textasciitilde1). In the DzGAN, the discriminator is fed by the images with label using one-hot vector and the generator is fed by divided z-vector (e.g. there are 10 classes In MNIST dataset, the divided z-vector will be z1\textasciitildez10 accordingly) with corresponding label fed into the discriminator, thus we can implement conditional learning. In this paper we use conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) [7] instead of cGAN because cDCGAN can generate clear image better than cGAN. Heuristic experiments of conditional learning which compare the computation amount demonstrate that DzGAN is superior than cDCGAN.

Edge detection is one of the most important topics of image processing. In the scenario of cloud computing, performing edge detection may also consider privacy protection. In this paper, we propose an edge detection and image segmentation scheme on an encrypted image with Sobel edge detector. We implement Gaussian filtering and Sobel operator on the image in the encrypted domain with homomorphic property. By implementing an adaptive threshold decision algorithm in the encrypted domain, we obtain a threshold determined by the image distribution. With the technique of garbled circuit, we perform comparison in the encrypted domain and obtain the edge of the image without decrypting the image in advanced. We then propose an image segmentation scheme on the encrypted image based on the detected edges. Our experiments demonstrate the viability and effectiveness of the proposed encrypted image edge detection and segmentation.

Botnet is one of the major threats on the Internet for committing cybercrimes, such as DDoS attacks, stealing sensitive information, spreading spams, etc. It is a challenging issue to detect modern botnets that are continuously improving for evading detection. In this paper, we propose a machine learning based botnet detection system that is shown to be effective in identifying P2P botnets. Our approach extracts convolutional version of effective flow-based features, and trains a classification model by using a feed-forward artificial neural network. The experimental results show that the accuracy of detection using the convolutional features is better than the ones using the traditional features. It can achieve 94.7% of detection accuracy and 2.2% of false positive rate on the known P2P botnet datasets. Furthermore, our system provides an additional confidence testing for enhancing performance of botnet detection. It further classifies the network traffic of insufficient confidence in the neural network. The experiment shows that this stage can increase the detection accuracy up to 98.6% and decrease the false positive rate up to 0.5%.

Although the deep learning technology effectively improves the effect of person re-identification (re-ID) in video surveillance, there is still a lack of efficient framework in practical, especially in terms of computational cost, which usually requires GPU support. So this paper explores to solve the actual running performance and an effective person re-ID framework is proposed. A tiny network is designed for person detection and a triplet network is adopted for training feature extraction network. The motion detection and person detection is combined to speed up the whole process. The proposed framework is tested in practice and the results show that it can run in real-time on an ordinary PC machine. And the accuracy achieves 91.6% in actual data set. It has a good guidance for person re-ID in actual application.

In this paper, we analyze the effectiveness of quantum algorithms to solve some classical computing complexities. In fact, we focus in this study on several famous quantum algorithms, where we discussed their impact on classical computing using in computer science.

Personal privacy is an important issue when publishing social network data. An attacker may have information to reidentify private data. So, many researchers developed anonymization techniques, such as k-anonymity, k-isomorphism, l-diversity, etc. In this paper, we focus on graph k-degree anonymity by editing edges. Our method is divided into two steps. First, we propose an efficient algorithm to find a new degree sequence with theoretically minimal edit cost. Second, we insert and delete edges based on the new degree sequence to achieve k-degree anonymity.

Approaches for the automatic analysis of security policies on source code level cannot trivially be applied to binaries. This is due to the lacking high-level semantics of low-level object code, and the fundamental problem that control-flow recovery from binaries is difficult. We present a novel approach to recover the control-flow of binaries that is both safe and efficient. The key idea of our approach is to use the information contained in security mechanisms to approximate the targets of computed branches. To achieve this, we first define a restricted control transition intermediate language (RCTIL), which restricts the number of possible targets for each branch to a finite number of given targets. Based on this intermediate language, we demonstrate how a safe model of the control flow can be recovered without data-flow analyses. Our evaluation shows that that makes our solution more efficient than existing solutions.

Deep packet inspection (DPI) is a critical component to prevent intrusion detection. This requires a detailed analysis of each network packet header and body. Although this is often done on dedicated high-power servers in most networked systems, mobile systems could potentially be vulnerable to attack if utilized on an unprotected network. In this case, having DPI hardware on the mobile system would be highly beneficial. Unfortunately, DPI hardware is generally area and power consuming, making its implementation difficult in mobile systems. We developed a memristor crossbar-based approach, inspired by memristor crossbar neuromorphic circuits, for a low-power, low-area, and high-throughput DPI system that examines both the header and body of a packet. Two key types of circuits are presented: static pattern matching and regular expression circuits. This system is able to reduce execution time and power consumption due to its high-density grid and massive parallelism. Independent searches are performed using low-power memristor crossbar arrays giving rise to a throughput of 160Gbps with no loss in the classification accuracy.

Cloud computing belongs to distributed network technology for computing and storage capabilities purpose. It is a kind of cost-effective technology dedicated to information technology. Using the Internet, the accessibility and retrieving of cloud data have become much more accessible. The service providers can expand the storage space in a cloud environment. Security is well-thought-out to be the essential attribute in a distributed system. Cryptography can be described as a method of securing the data from attackers and eavesdroppers. Third Party Auditor is responsible for the authentication of secret files in cloud system on behalf of the data owner. The data auditability technique allows the user to make the data integrity check using a third party. Cloud computing offers unlimited data space for storage to its users and also serves sharing of data and planned use of heterogeneous resources in distributed systems. This paper describes privacy-preserving enabled public auditing method using cryptographic techniques for low-performance based end devices.

In this paper, we propose a new randomized response algorithm that can achieve differential-privacy and utility guarantees for consumer's behaviors, and process a batch of data at each time. Firstly, differing from traditional differential private approach-es, we add randomized response noise into the behavior signa-tures matrix to achieve an acceptable utility-privacy tradeoff. Secondly, a behavior signature modeling method based on sparse coding is proposed. After some lightweight trainings us-ing the energy consumption data, the dictionary will be associat-ed with the behavior characteristics of the electric appliances. At last, through the experimental results verification, we find that our Algorithm can preserve consumer's privacy without comprising utility.

This paper proposes a lightweight and privacy-preserving data aggregation scheme for dynamic electricity pricing based billing in smart grids using the concept of single-pass authenticated encryption (AE). Unlike existing literature that only considers static pricing, to the best of our knowledge, this is the first paper to address privacy under dynamic pricing.

This paper proposes an efficient auditing scheme for checking the integrity of dynamic data shared among a static group of users outsourced at untrusted cloud storage. The scheme is designed based on CDH-based ring signature scheme. The scheme enables a third party auditor to audit the client's data without knowing the content while also preserving the identity privacy of the group member who is signing the data from the auditor as well as from the cloud server. The identity of the group member who is signing the data block can be revealed only by the authorized opener, if needed. The paper presents a comparative performance study and security analysis of the proposed scheme.

Electromagnetic (EM) analysis is to reveal the secret information by analyzing the EM emission from a cryptographic device. EM analysis (EMA) attack is emerging as a serious threat to hardware security. It has been noted that the on-chip power grid (PG) has a security implication on EMA attack by affecting the fluctuations of supply current. However, there is little study on exploiting this intrinsic property as an active countermeasure against EMA. In this paper, we investigate the effect of PG on EM emission and propose an active countermeasure against EMA, i.e. EM Equalizer (EME). By adjusting the PG impedance, the current waveform can be flattened, equalizing the EM profile. Therefore, the correlation between secret data and EM emission is significantly reduced. As a first attempt to the co-optimization for power and EM security, we extend the EME method by fixing the vulnerability of power analysis. To verify the EME method, several cryptographic designs are implemented. The measurement to disclose (MTD) is improved by 1138x with area and power overheads of 0.62% and 1.36%, respectively.

Embedded and cyber-physical systems are critically dependent on the integrity of input and output signals for proper operation. Input signals acquired from sensors are assumed to correspond to the phenomenon the system is monitoring and responding to. Similarly, when such systems issue an actuation signal it is expected that the mechanism being controlled will respond in a predictable manner. Recent work has shown that sensors can be manipulated through the use of intentional electromagnetic interference (IEMI). In this work, we demonstrate thatboth input and output signals, analog and digital, can be remotely manipulated via the physical layer—thus bypassing traditional integrity mechanisms. Through the use of specially crafted IEMI it is shown that the physical layer signaling used for sensor input to, and digital communications between, embedded systems may be undermined to an attacker's advantage. Three attack scenarios are analyzed and their efficacy demonstrated. In the first scenario the analog sensing channel is manipulated to produce arbitrary sensor readings, while in the second it is shown that an attacker may induce bit flips in serial communications. Finally, a commonly used actuation signal is shown to be vulnerable to IEMI. The attacks are effective over appreciable distances and at low power.

The physical unclonable functions (PUFs) have been attracted attention to prevent semiconductor counterfeits. However, the risk of machine learning attack for an arbiter PUF, which is one of the typical PUFs, has been reported. Therefore, an XOR arbiter PUF, which has a resistance against the machine learning attack, was proposed. However, in recent years, a new machine learning attack using power consumption during the operation of the PUF circuit was reported. Also, it is important that the detailed tamper resistance verification of the PUFs to consider the security of the PUFs in the future. Therefore, this study proposes a new machine learning attack using electromagnetic waveforms for the XOR arbiter PUF. Experiments by an actual device evaluate the validity of the proposed method and the security of the XOR arbiter PUF.

Observing semantic dependencies in large and heterogeneous networks is a critical task, since it is quite difficult to find the actual source of a malfunction in the case of an error. Dependencies might exist between many network nodes and among multiple hops in paths. If those dependency structures are unknown, debugging errors gets quite difficult. Since CPS and other large networks change at runtime and consists of custom software and hardware, as well as components off-the-shelf, it is necessary to be able to not only include own components in approaches to detect dependencies between nodes. In this paper we present an extension to the Information Flow Monitor approach. Our goal is that this approach should be able to handle unalterable blackbox nodes. This is quite challenging, since the IFM originally requires each network node to be compliant with the IFM protocol.

In this paper, the literature survey of different algorithms for generating encryption keys using fingerprints is presented. The focus is on fingerprint features called minutiae points where fingerprint ridges end or bifurcate. Minutiae points require less memory and are processed faster than other fingerprint features. In addition, presented is the proposed efficient method for cryptographic key generation using finger-codes. The results show that the length of the key, computing time and the memory it requires is efficient for use as a biometric key or even as a password during verification and authentication.

The advent of smart grids offers us the opportunity to better manage the electricity grids. One of the most interesting challenges in the modern grids is the consumer demand management. Indeed, the development in Information and Communication Technologies (ICTs) encourages the development of demand-side management systems. In this paper, we propose a distributed energy demand scheduling approach that uses minimal interactions between consumers to optimize the energy demand. We formulate the consumption scheduling as a constrained optimization problem and use game theory to solve this problem. On one hand, the proposed approach aims to reduce the total energy cost of a building's consumers. This imposes the cooperation between all the consumers to achieve the collective goal. On the other hand, the privacy of each user must be protected, which means that our distributed approach must operate with a minimal information exchange. The performance evaluation shows that the proposed approach reduces the total energy cost, each consumer's individual cost, as well as the peak to average ratio.

Deep neural networks (DNNs) are effective machine learning models to solve a large class of recognition problems, including the classification of nonlinearly separable patterns. The applications of DNNs are, however, limited by the large size and high energy consumption of the networks. Recently, stochastic computation (SC) has been considered to implement DNNs to reduce the hardware cost. However, it requires a large number of random number generators (RNGs) that lower the energy efficiency of the network. To overcome these limitations, we propose the design of an energy-efficient deep belief network (DBN) based on stochastic computation. An approximate SC activation unit (A-SCAU) is designed to implement different types of activation functions in the neurons. The A-SCAU is immune to signal correlations, so the RNGs can be shared among all neurons in the same layer with no accuracy loss. The area and energy of the proposed design are 5.27% and 3.31% (or 26.55% and 29.89%) of a 32-bit floating-point (or an 8-bit fixed-point) implementation. It is shown that the proposed SC-DBN design achieves a higher classification accuracy compared to the fixed-point implementation. The accuracy is only lower by 0.12% than the floating-point design at a similar computation speed, but with a significantly lower energy consumption.

There are two types of network architectures are presents those are wired network and wireless network. MANETs is one of the examples of wireless network. Each and every network has their own features which make them different from other types of network. Some of the features of MANETs are; infrastructure less network, mobility, dynamic network topology which make it different and more popular from wired network but these features also generate different problems for achieving security due to the absence of centralized authority inside network as well as sending of data due to its mobility features. Achieving security in wired network is little-bit easy compare to MANETs because in wired network user need to just protect main centralized authority for achieving security whereas in MANETs there is no centralized authority available so protecting server in MANETs is difficult compare to wired network. Data sending and receiving process is also easy in wired network but mobility features makes this data sending and receiving process difficult in MANETs. Protecting server or central repository without making use of secrete sharing in wired network will create so many challenges and problem in terms of security. The proposed system makes use of Secrete sharing method to protect server from malicious nodes and `A New particle Swarm Optimization Method for MANETs' (NPSOM) for performing data sending and receiving operation in optimization way. NPSOM technique get equated with the steady particle swarm optimizer (PSO) technique. PSO was essentially designed by Kennedy, Eberhart in 1995. These methods are based upon 4 dissimilar types of parameters. These techniques were encouraged by common performance of animals, some of them are bird assembling and fish tuition, ant colony. The proposed system converts this PSO in the form of MANETs where Particle is nothing but the nodes in the network, Swarm means collection of multiple nodes and Optimization means finding the best and nearer root to reach to destination. Each and every element study about their own previous best solution which they are having with them for the given optimization problem, likewise they see for the groups previous best solution which they got for the same problem and finally they correct its solution depending on these values. This same process gets repeated for finding of the best and optimal solutions value. NPSOM technique, used in proposed system there every element changes its location according to the solution which they got previously and which is poorest as well as their collection's earlier poorest solution for finding best, optimal value. In this proposed system we are concentrating on, sidestepping element's and collections poorest solution which they got before.

Many customers ranked cloud security as a major challenge that threaten their work and reduces their trust on cloud service's provider. Hence, a significant improvement is required to establish better adaptations of security measures that suit recent technologies and especially distributed architectures. Considering the meaningful recorded data in cloud generated log files, making analysis on them, mines insightful value about hacker's activities. It identifies malicious user behaviors and predicts new suspected events. Not only that, but centralizing log files, prevents insiders from causing damage to system. In this paper, we proposed to take away sensitive log files into a single server provider and combining both MapReduce programming and k-means on the same algorithm to cluster observed events into classes having similar features. To label unknown user behaviors and predict new suspected activities this approach considers cosine distances and deviation metrics.

Password auditing can enhance the cyber situational awareness of defenders, e.g. cyber security/IT professionals, with regards to the strength of text-based authentication mechanisms utilized in an organization. Auditing results can proactively indicate if weak passwords exist in an organization, decreasing the risks of compromisation. Password cracking is a typical and time-consuming way to perform password auditing. Given that defenders perform password auditing within a specific evaluation timeframe, the cracking process needs to be optimized to yield useful results. Existing password cracking tools do not provide holistic features to optimize the process. Therefore, the need arises to build new password auditing toolkits to assist defenders to achieve their task in an effective and efficient way. Moreover, to maximize the benefits of password auditing, a security policy should be utilized. Currently the efforts focus on the specification of password security policies, providing rules on how to construct passwords. This work proposes the functionality that should be supported by next-generation password auditing toolkits and provides guidelines to drive the specification of a relevant password auditing policy.

The Cloud computing in simple terms is storing and accessing data through internet. The data stored in the cloud is managed by cloud service providers. Storing data in cloud saves users time and memory. But once user stores data in cloud, he loses the control over his data. Hence there must be some security issues to be handled to keep users data safely in the cloud. In this work, we projected a secure auditing system using Third Party Auditor (TPA). We used Advanced Encryption Standard (AES) algorithm for encrypting user's data and Secure Hash Algorithm (SHA-2) to compute message digest. The system is executed in Amazon EC2 cloud by creating windows server instance. The results obtained demonstrates that our proposed work is safe and takes a firm time to audit the files.

Loitering is a suspicious behavior that often leads to criminal actions, such as pickpocketing and illegal entry. Tracking methods can determine suspicious behavior based on trajectory, but require continuous appearance and are difficult to scale up to multi-camera systems. Using the duration of appearance of features works on multiple cameras, but does not consider major aspects of loitering behavior, such as repeated appearance and trajectory of candidates. We introduce an entropy model that maps the location of a person's features on a heatmap. It can be used as an abstraction of trajectory tracking across multiple surveillance cameras. We evaluate our method over several datasets and compare it to other loitering detection methods. The results show that our approach has similar results to state of the art, but can provide additional interesting candidates.

The growing use of smart phones has also given opportunity to the intruders to create malicious apps thereby the security and privacy concerns of a novice user has also grown. This research focuses on the privacy concerns of a user who unknowingly installs a malicious apps created by the programmer. In this paper we created an attack scenario and created an app capable of compromising the privacy of the users. After accepting all the permissions by the user while installing the app, the app allows us to track the live location of the Android device and continuously sends the GPS coordinates to the server. This spying app is also capable of sending the call log details of the user. This paper evaluates two leading smart phone operating systems- Android and IOS to find out the flexibility provided by the two operating systems to their programmers to create the malicious apps.