Biblio

Generative Adversarial Network (GAN) and its variants have shown promising results in generating synthetic data. However, the issues with GANs are: (i) the learning happens around the training samples and the model often ends up remembering them, consequently, compromising the privacy of individual samples - this becomes a major concern when GANs are applied to training data including personally identifiable information, (ii) the randomness in generated data - there is no control over the specificity of generated samples. To address these issues, we propose imdpGAN-an information maximizing differentially private Generative Adversarial Network. It is an end-to-end framework that simultaneously achieves privacy protection and learns latent representations. With experiments on MNIST dataset, we show that imdpGAN preserves the privacy of the individual data point, and learns latent codes to control the specificity of the generated samples. We perform binary classification on digit pairs to show the utility versus privacy trade-off. The classification accuracy decreases as we increase privacy levels in the framework. We also experimentally show that the training process of imdpGAN is stable but experience a 10-fold time increase as compared with other GAN frameworks. Finally, we extend imdpGAN framework to CelebA dataset to show how the privacy and learned representations can be used to control the specificity of the output.

The article presents a proposal for implementing asymmetric cryptography, specifically the elliptic curves for the protection of high-speed data transmission in a corporate network created on the platform of PLC (Power Line Communications). The solution uses an open-source software library OpenSSL. As part of the design, an experimental workplace was set up, a DHCP and FTP server was established. The possibility of encryption with the selected own elliptic curve from the OpenSSL library was tested so that key pairs (public and private keys) were generated using a software tool. A shared secret was created between communication participants and subsequently, data encryption and decryption were performed.

Well-known approaches to risk analysis suggest considering the level of an information system risk as one frame in a film. This means that we only can perform a risk assessment for the current point in time. This article explores the idea of risk assessment in a future period, as a prediction of what we will see in the film later. In other words, the article presents an approach to predicting a potential future risk and suggests the idea of relying on forecasting the likelihood of an attack on information system assets. To establish the risk level at a selected time interval in the future, one has to perform a mathematical decomposition. To do this, we need to select the required information system parameters for the predictions and their statistical data for risk assessment. This method can be used to ensure more detailed budget planning when ensuring the protection of the information system. It can be also applied in case of a change of the information protection configuration to satisfy the accepted level of risk associated with projected threats and vulnerabilities.

Trusted Platform Modules (TPMs) are specialized chips that store RSA keys specific to the host system for hardware authentication. The RSA keys refer to an encryption technology developed by RSA Data Security. The RSA algorithm accounts for the fact that there is no efficient way to factor extremely large numbers. Each TPM chip contains an RSA Key pair known as the Endorsement Key that cannot be accessed by software. The TPM contains an additional key, called the Attestation Identity Key that protects the device itself against unauthorized firmware and software modification by implementing hash functions on critical sections of the software and firmware before execution. As a result, the TPM can be used as a chip for handling encryption for a larger system to offer an additional layer of security. Furthermore, the TPM can also be used for managing encryption keys, as a Storage Root Key is created when a user or administrator takes ownership of the system. However, merging the TPM into a system does come with additional costs along with potential benefits. This paper focuses on integrating a TPM into a system implemented on an ARM processor that engages with power electronics, and then presents the security benefits associated with a TPM.

The number of Mobile device users in the word has evolved rapidly. Many internet users currently want to connect the internet for all utilities automatically. One of the technologies in the IPv6 network, which supports data access from moving users, is IPv6 Mobile protocol. In its mobility, the users on a range of networks can move the range to another network. High demand for this technology will interest to a hacker or a cracker to carry out an attack. One of them is a DoS attack that compromises a target to denial its services. A firewall is usually used to protect networks from external attacks. However, since the firewall based on the attacker database, the unknown may not be detected. In order to address the obstacle, a detection tool could be used. In this research, IDS as an intrusion detection tool was integrated with a firewall to be implemented in IPv6 Mobile to stop the DoS attack. The results of some experiments showed that the integration system could block the attack at 0.9 s in Correspondent Node and 1.2 s in Home Agent. The blocked attack can decrease the network throughput up to 27.44% when a Mobile Node in Home Agent, 28,87% when the Mobile Node in a Foreign Network. The final result of the blocked attack is reducing the average CPU utilization up to 30.99%.

In recent years, we have faced a series of online threats. The continuous malicious attacks on the network have directly caused a huge threat to the user's spirit and property. In order to deal with the complex security situation in today's network environment, an intelligent network situational awareness model based on deep neural networks is proposed. Use the nonlinear characteristics of the deep neural network to solve the nonlinear fitting problem, establish a network security situation assessment system, take the situation indicators output by the situation assessment system as a guide, and collect on the main data features according to the characteristics of the network attack method, the main data features are collected and the data is preprocessed. This model designs and trains a 4-layer neural network model, and then use the trained deep neural network model to understand and analyze the network situation data, so as to build the network situation perception model based on deep neural network. The deep neural network situational awareness model designed in this paper is used as a network situational awareness simulation attack prediction experiment. At the same time, it is compared with the perception model using gray theory and Support Vector Machine(SVM). The experiments show that this model can make perception according to the changes of state characteristics of network situation data, establish understanding through learning, and finally achieve accurate prediction of network attacks. Through comparison experiments, datatypized neural network deep neural network situation perception model is proved to be effective, accurate and superior.

Recently literature indicates that stack based three-dimensional (3D) integration techniques may bring in new security vulnerabilities, such as new attack surfaces for hardware Trojan (HT) insertion. Compared to its two-dimensional counterpart (2DHTs), a 3D hardware Trojan (3DHT) could be stealthily distributed in multiple tiers in a single 3D chip. Although the comprehensive models for 3DHTs are available in recent work, there still lacks 3DHT detection and mitigation methods, especially run-time countermeasures against 3DHTs. This work proposes to leverage the 3D communication infrastructure, 3D network-on-chips (NoCs), to tackle the cross-tier hardware Trojans in stacked multi-tier chips. An invariance checking method is further proposed to detect the Trojans that induce malicious NoC packets or facilitate information leak. The proposed method is successfully deployed in NoC routers and achieves a Trojan detection rate of over 94%. The synthesis result of a hardened router at a 45nm technology node shows that the proposed invariance checking only increases the area by 6.49% and consumes 3.76% more dynamic power than an existing 3D router. The NoC protected with the proposed method is applied to the image authentication in a 3D system. The case study indicates that the proposed security measure reduces the correlation coefficient by up to 31% over the baseline.

Cyber-security is facing a huge threat from malware and malware mass production due to its mutation factors. Classification of malware by their features is necessary for the security of information technology (IT) society. To provide security from malware, deep neural networks (DNN) can offer a superior solution for the detection and categorization of malware samples by using image classification techniques. To strengthen our ideology of malware classification through image recognition, we have experimented by comparing two perspectives of malware classification. The first perspective implements dense neural networks on binary files and the other applies deep layered convolutional neural network on malware images. The proposed model is trained to a set of malware samples, which are further distributed into 9 different families. The dataset of malware samples which is used in this paper is provided by Microsoft for Microsoft Malware Classification Challenge in 2015. The proposed model shows an accuracy of 97.80% on the provided dataset. By using the proposed model optimum classifications results can be attained.

The following topics are dealt with: learning (artificial intelligence); neural nets; feature extraction; pattern classification; convolutional neural nets; computer network security; security of data; recurrent neural nets; data privacy; and cloud computing.

With the increasing popularity of the Internet, mobile Internet and the Internet of Things, the current network environment continues to become more complicated. Due to the increasing variety and severity of cybersecurity threats, traditional means of network security protection have ushered in a huge challenge. The network security posture prediction can effectively predict the network development trend in the future time based on the collected network history data, so this paper proposes an algorithm based on simulated annealing-particle swarm algorithm to optimize improved Elman neural network parameters to achieve posture prediction for network security. Taking advantage of the characteristic that the value of network security posture has periodicity, a simulated annealing algorithm is introduced along with an improved particle swarm algorithm to solve the problem that neural network training is prone to fall into a local optimal solution and achieve accurate prediction of the network security posture. Comparison of the proposed scheme with existing prediction methods validates that the scheme has a good posture prediction accuracy.

In this study, our aim is to extend the scope for public key cryptography. We offered a new efficient public key encryption scheme using partial discrete logarithm problem (PDLP). It is known as the Quadratic Exponentiation Randomized Public Key Cryptosystem (QERPKC). Security of the presented scheme is based on the hardness of PDLP. We reflect the safety in contrast to trick of certain elements in the offered structure and demonstrated the prospect of creating an extra safety structure. The presented new efficient QERPKC structure is appropriate for low-bandwidth communication, low-storage and low-computation environments.

Wide-area protection and control (WAPAC) systems are widely applied in the energy management system (EMS) that rely on a wide-area communication network to maintain system stability, security, and reliability. As technology and grid infrastructure evolve to develop more advanced WAPAC applications, however, so do the attack surfaces in the grid infrastructure. This paper presents an attack-resilient system (ARS) for the WAPAC cybersecurity by seamlessly integrating the network intrusion detection system (NIDS) with intrusion mitigation and prevention system (IMPS). In particular, the proposed NIDS utilizes signature and behavior-based rules to detect attack reconnaissance, communication failure, and data integrity attacks. Further, the proposed IMPS applies state transition-based mitigation and prevention strategies to quickly restore the normal grid operation after cyberattacks. As a proof of concept, we validate the proposed generic architecture of ARS by performing experimental case study for wide-area protection scheme (WAPS), one of the critical WAPAC applications, and evaluate the proposed NIDS and IMPS components of ARS in a cyber-physical testbed environment. Our experimental results reveal a promising performance in detecting and mitigating different classes of cyberattacks while supporting an alert visualization dashboard to provide an accurate situational awareness in real-time.

A novel efficiency-shifting radial-axial hybrid permanent magnet synchronous motor that can realize two high-efficiency regions at low and high speeds is developed to extend the maximum driving distance and track the reference speed more accurately for electric vehicle application. The motor has two stators, which are radial and axial, to rotate one shared rotor. The rotor employs two combined topologies, i.e., inner surface-inset-mounted and outer V-shaped interior-mounted. For both outer and inner permanent magnets, Nd-Fe-B, having the remanent flux density of 1.23 T and coercivity of 890 kA/m, is used. The simulation result shows that the designed motor exhibits not only high maximum torque of 400 Nm and the maximum speed of 18,000 rpm but also two high-efficiency regions of 97.6 % and 92.0 % at low and high speed, respectively. Lastly, the developed motor shows better performance than corresponding separated radial and axial permanent magnet motor.

A wide range of tools exist to assist developers in creating secure software. Many of these tools, such as static analysis engines or security checkers included in compilers, use warnings to communicate security issues to developers. The effectiveness of these tools relies on developers heeding these warnings, and there are many ways in which these warnings could be displayed. Johnson et al. [46] conducted qualitative research and found that warning presentation and integration are main issues. We built on Johnson et al.'s work and examined what developers want from security warnings, including what form they should take and how they should integrate into their workflow and work context. To this end, we conducted a Grounded Theory study with 14 professional software developers and 12 computer science students as well as a focus group with 7 academic researchers to gather qualitative insights. To back up the theory developed from the qualitative research, we ran a quantitative survey with 50 professional software developers. Our results show that there is significant heterogeneity amongst developers and that no one warning type is preferred over all others. The context in which the warnings are shown is also highly relevant, indicating that it is likely to be beneficial if IDEs and other development tools become more flexible in their warning interactions with developers. Based on our findings, we provide concrete recommendations for both future research as well as how IDEs and other security tools can improve their interaction with developers.

In many-cores based on Network-on-Chip (NoC), several applications execute simultaneously, sharing computation, communication and memory resources. This resource sharing leads to security and trust problems. Hardware Trojans (HTs) may steal sensitive information, degrade system performance, and in extreme cases, induce physical damages. Methods available in the literature to prevent attacks include firewalls, denial-of-service detection, dedicated routing algorithms, cryptography, task migration, and secure zones. The goal of this paper is to add an HT in an NoC, able to execute three types of attacks: packet duplication, block applications, and misrouting. The paper qualitatively evaluates the attacks' effect against methods available in the literature, and its effects showed in an NoC-based many-core. The resulting system is an open-source NoC-based many-core for researchers to evaluate new methods against HT attacks.

Advanced Persistent Threats (APTs) refer to sophisticated, prolonged and multi-step attacks, planned and executed by skilled adversaries targeting government and enterprise networks. Attack graphs' topologies can be leveraged to detect, explain and visualize the progress of such attacks. However, due to the abundance of false-positives, such graphs are usually overwhelmingly large and difficult for an analyst to understand. Graph partitioning refers to the problem of reducing the graph of alerts to a set of smaller incidents that are easier for an analyst to process and better represent the actual attack plan. Existing approaches are oblivious to the security-context of the problem at hand and result in graphs which, while smaller, make little sense from a security perspective. In this paper, we propose an optimization approach allowing us to generate security-aware partitions, utilizing aspects such as the kill chain progression, number of assets involved, as well as the size of the graph. Using real-world datasets, the results show that our approach produces graphs that are better at capturing the underlying attack compared to state-of-the-art approaches and are easier for the analyst to understand.

This paper describes a realisation of a ResNet face recognition method through Zigbee-based wireless protocol. The system uses a CC2530 Zigbee-based radio frequency chip with connected VC0706 camera on it. The Arduino Nano had been used for organisation of data compression and effective division of Zigbee packets. The proposed solution also simplifies a data transmission within a strict bandwidth of Zigbee protocol and reliable packet forwarding in case of frequency distortion. The following investigation model uses Raspberry Pi 3 with connected Zigbee End Device (ZED) for successful receiving of important images and acceleration of deep learning interfaces. The model is integrated into a smart security system based on Zigbee modules, MySQL database, Android application and works in the background by using daemons procedures. To protect data, all wireless connections had been encrypted by the 128-bit Advanced Encryption Standard (AES-128) algorithm. Experimental results show a possibility to implement complex systems under restricted requirements of available transmission protocols.

In this paper, physical layer security (PLS) analysis of a cooperative wireless communication system in which the source and destination nodes communicate via a relay employing decode-and-forward protocol is performed for double Rayleigh fading channel model. For the system where the source, relay and target have single antenna, an eavesdropper with multiantenna listens the source and relay together by using maximum-ratio-combining, secrecy outage and positive secrecy capacity possibilities are obtained in closed-form. The theoretical results are verified by Monte-Carlo simulations. From the results, it is observed that as the number of antennas of the eavesdropper is increased, the PLS performance of the system worsens.

Deep Packet Inspection (DPI) is an essential building block implementing various services on data plane [5]. Usually, DPI engines are centered around efficient implementation of regular expressions both from the required memory and lookup time perspectives. In this paper, we explore and generalize original approaches used for packet classifiers [7] to regular expressions. Our preliminary results establish a promising direction for the efficient implementation of DPI engines.

When users in various web and mobile applications enjoy the convenience of recommendation systems, they are vulnerable to attribute inference attacks. The accumulating online behaviors of users (e.g., clicks, searches, ratings) naturally brings out user preferences, and poses an inevitable threat of privacy that adversaries can infer one's private profiles (e.g., gender, sexual orientation, political view) with AI-based algorithms. Existing defense methods assume the existence of a trusted third party, rely on computationally intractable algorithms, or have impact on recommendation utility. These imperfections make them impractical for privacy preservation in real-life scenarios. In this work, we introduce BiasBooster, a practical proactive defense method based on behavior segmentation, to protect user privacy against attribute inference attacks from user behaviors, while retaining recommendation utility with a heuristic recommendation aggregation module. BiasBooster is a user-centric approach from client side, which proactively divides a user's behaviors into weakly related segments and perform them with several dummy identities, then aggregates real-time recommendations for user from different dummy identities. We estimate its effectiveness of preservation on both privacy and recommendation utility through extensive evaluations on two real-world datasets. A Chrome extension is conducted to demonstrate the feasibility of applying BiasBooster in real world. Experimental results show that compared to existing defenses, BiasBooster substantially reduces the averaged accuracy of attribute inference attacks, with minor utility loss of recommendations.

Convergence of critical infrastructure and data, including government and enterprise, to the dynamic Internet of Things (IoT) environment and future digital ecosystems exhibit significant challenges for privacy and identity in these interconnected domains. There are an increasing variety of devices and technologies being introduced, rendering existing security tools inadequate to deal with the dynamic scale and varying actors. The IoT is increasingly data driven with user sovereignty being essential - and actors in varying scenarios including user/customer, device, manufacturer, third party processor, etc. Therefore, flexible frameworks and diverse security requirements for such sensitive environments are needed to secure identities and authenticate IoT devices and their data, protecting privacy and integrity. In this paper we present a review of the principles, techniques and algorithms that can be adapted from other distributed computing paradigms. Said review will be used in application to the development of a collaborative decision-making framework for heterogeneous entities in a distributed domain, whilst simultaneously highlighting privacy preserving issues in the IoT. In addition, we present our trust-based privacy preserving schema using Dempster-Shafer theory of evidence. While still in its infancy, this application could help maintain a level of privacy and nonrepudiation in collaborative environments such as the IoT.

As an important type of cloud data, digital provenance is arousing increasing attention on improving system performance. Currently, provenance has been employed to provide cues regarding access control and to estimate data quality. However, provenance itself might also be sensitive information. Therefore, provenance might be encrypted and stored in the Cloud. In this paper, we provide a mechanism to classify cloud documents by searching specific keywords from their encrypted provenance, and we prove our scheme achieves semantic security. In term of application of the proposed techniques, considering that files are classified to store separately in the cloud, in order to facilitate the regulation and security protection for the files, the classification policies can use provenance as conditions to determine the category of a document. Such as the easiest sample policy goes like: the documents have been reviewed twice can be classified as “public accessible”, which can be accessed by the public.

Quantized deep neural networks (QDNNs) are necessary for low-power, high throughput, and embedded applications. Previous studies mostly focused on developing optimization methods for the quantization of given models. However, quantization sensitivity depends on the model architecture. Also, the characteristics of weight and activation quantization are quite different. This study proposes a holistic approach for the optimization of QDNNs, which contains QDNN training methods as well as quantization-friendly architecture design. Synthesized data is used to visualize the effects of weight and activation quantization. The results indicate that deeper models are more prone to activation quantization, while wider models improve the resiliency to both weight and activation quantization.

To achieve a substantial reliability and safety level, it is imperative to provide electronic computing systems with appropriate mechanisms to tackle soft errors. This paper proposes a low-cost system-level soft error mitigation technique, which allocates the critical application function to a pool of specific general-purpose processor registers. Both the critical function and the register pool are automatically selected by a developed profiling tool. The proposed technique was validated through more than 320K fault injections considering a Linux kernel, different benchmarks and two multicore ARM processors. Results show that our technique significantly reduces the code size and performance overheads while providing reliability improvement, w.r.t. the Triple Modular Redundancy (TMR) technique.

The Internet of things is an extremely enormous space and still, IoT is spreading over a wide range of zones of development with very fast speed. The IoT is going to create a new world of efficient services. IoT is a collective system consisting of hardware like sensors, Radio Frequency Identification RFID, Bluetooth devices, Near Field Communication (NFC) devices, etc. and software that provides data queries, exchange, repository and exchanges, etc. Security of the IoT network is also a big and important issue of concern. This paper reviews the DDoS attack impact on IoT network and its mitigation methods for IoT in network, also discusses CoAP protocol, RPL protocol and 6LoWPAN network. This paper also represents the security framework to detect and monitor the DDoS attack for low power devices based IoT network.