Biblio

In this paper, the two methods for ciphering are presented and compared. The aim is to reveal the suitability of chaotic neural network approach to ciphering compared to AES cipher. The durations in seconds of both methods are presented and the two methods are compared. The results show, that the chaotic neural network is fast, suitable for ciphering of short plaintexts. AES ciphering is suitable for longer plaintexts or images and is also more reliable.

As cloud storage and computing gains popularity, data entrusted to the cloud has the potential to be exposed to more people and thus more vulnerable to attacks. It is important to develop mechanisms to protect data privacy and integrity so that clients can safely outsource their data to the cloud. We present a method for ensuring data completeness which is one facet of the data integrity problem. Our approach converts a standard database to a Completeness Protected Database (CPDB) by inserting some semantic fake data before outsourcing it to the cloud. These fake data are initially produced using our generating function which uses Order Preserving Encryption, which allows the user to be able to regenerate these fake data and match them to fake data returned from a range query to check for completeness. The CPDB is innovative in the following ways: (1) fake data is deterministically generated but is semantically indistinguishable from other existing data; (2) since fake data is generated by deterministic functions, data owners do not need to locally store the fake data that have been inserted, instead they can re-generate fake data using the functions; (3) no costly data encryption/signature is used in our scheme compared to previous work which encrypt/sign the entire database.

In operation centers, it is important to know the power transfer limit to guarantee the safety operation of the power system. The Voltage Stability Margin (VSM) is a widely used measure and needs to definition of a load growth direction (LGD) to be computed. However, different definitions of LGD can provide different VSMs and then the VSM may not be reliable. Besides, the measure of this power transfer limit usually is related to the Saddle-Node Bifurcation. In dynamic security assessment (DSA) is highly desirable to identify limit regions where the power system can operate safely due to Hopf (HB) and Saddle-Node (SNB) Bifurcations. This paper presents a modeling of the power system incorporating the LGD variation based on participation factors to evaluate the effects on the stability margin estimation due to HB and SNB. A direct method is used to calculate the stability margin of the power system for a given load direction. The analysis was performed in the IEEE 39 bus system.

Designing secure systems is a complex task, particularly for designers who are no security experts. Cyber security plays a key role in embedded systems, especially for the domain of the Internet of Things (IoT). IoT systems of this kind are becoming increasingly important in daily life as they simplify various tasks. They are usually small, either embedded into bigger systems or battery driven, and perform monitoring or one shot tasks. Thus, they are subject to manifold constraints in terms of performance, power consumption, chip area, etc. As they are continuously connected to the internet and utilize our private data to perform their tasks, they are interesting for potential attackers. Cyber security thus plays an important role for the design of an IoT system. As the usage of security measures usually increases both computation time, as well as power consumption, a conflict between these constraints must be solved. For the designers of such systems, balancing these constraints constitutes a highly complex task. In this paper we propose a novel approach for considering possible security attacks on embedded systems, simplifying the consideration of security requirements immediately at the start of the design process. We introduce a security aware design space exploration framework which based on an architectural, behavioral and security attack description, finds the optimal design for IoT systems. We also demonstrate the feasibility and the benefits of our framework based on a door access system use case.

The Internet of Things (IoT) provides connectivity between heterogeneous devices in different applications, such as smart wildlife, supply chain and traffic management. Trust management system (TMS) assesses the trustworthiness of service with respect to its quality. Under different context information, a service provider may be trusted in one context but not in another. The existing context-aware trust models usually store trust values under different contexts and search the closest (to a given context) record to evaluate the trustworthiness of a service. However, it is not suitable for distributed resource-constrained IoT devices which have small memory and low power. Reputation systems are applied in many trust models where trustor obtains recommendations from others. In context-based trust evaluation, it requires interactive queries to find relevant information from remote devices. The communication overhead and energy consumption are issues in low power networks like 6LoWPAN. In this paper, we propose a new context-aware trust model for lightweight IoT devices. The proposed model provides a trustworthiness overview of a service provider without storing past behavior records, that is, constant size storage. The proposed model allows a trustor to decide the significance of context items. This could result in distinctive decisions under the same trustworthiness record. We also show the performance of the proposed model under different attacks.

Networked Control Systems (NCS) are widely used in Industry 4.0 to obtain better management and operational capabilities, as well as to reduce costs. However, despite the benefits provided by NCSs, the integration of communication networks with physical plants can also expose these systems to cyber threats. This work proposes a link monitoring strategy to identify linear time-invariant transfer functions performed by a Man-in-the-Middle during controlled data injection attacks in NCSs. The results demonstrate that the proposed identification scheme provides adequate accuracy when estimating the attack function, and does not interfere in the plant behavior when the system is not under attack.

The accuracy of the image classification by the convolutional neural network is exceeding the ability of human being and contributes to various fields. However, the improvement of the image recognition technology gives a great blow to security system with an image such as CAPTCHA. In particular, since the character string CAPTCHA has already added distortion and noise in order not to be read by the computer, it becomes a problem that the human readability is lowered. Adversarial examples is a technique to produce an image letting an image classification by the machine learning be wrong intentionally. The best feature of this technique is that when human beings compare the original image with the adversarial examples, they cannot understand the difference on appearance. However, Adversarial examples that is created with conventional FGSM cannot completely misclassify strong nonlinear networks like CNN. Osadchy et al. have researched to apply this adversarial examples to CAPTCHA and attempted to let CNN misclassify them. However, they could not let CNN misclassify character images. In this research, we propose a method to apply FGSM to the character string CAPTCHAs and to let CNN misclassified them.

The aim of this work is to create a new style web browser. The other web browsers can have safety issues and have many ads and popups. The other web browsers can fill up cache with the logging of big history of visited web pages. This app is a light-weight web browser which is both secure and private with no ads and no popups, just the plain Internet shown in full screen. The app does not store all user data, so the navigation of webpages is done in incognito mode. The app was made to open any new HTML5 web page in a secure and private mode with big focus on loading speed of the web pages.

Polar codes are a talented coding technique with the ability to accomplish the discrete memoryless channel capacity for modern communication systems with high reliability, but it is not secured enough for such systems. A secured system counts on grouping polar codes with secret Mersenne- Twister pseudo-random number generator (MT PRNG) is presented in this paper. The proposed encoder security is deduced from the secret pre-shared initial state of MT PRNG which is considered as the crypto-system ciphering key. The generated sequences are random like and control the frozen bits' values and their indices in the polarized bit channels. When the decoding cipher key at the receiver has one-bit change from the original encoding cipher key, the receiver has an almost 0.5 BER probability. This means that the receiver, in this case, had no clue about the originally sent information data bits without prior knowledge of the utilized 232-bit ciphering key. Moreover, the security of the system can be enhanced by utilizing a pseudo-random number generator (PRBG) with longer seed to increase the system secrecy and decoding obscurity.

In this work, an asymmetric cryptography method for information security was developed, inspired by the fact that the human body generates chaotic signals, and these signals can be used to create sequences of random numbers. Encryption circuit was implemented in a Reconfigurable Hardware (FPGA). To encode and decode an image, the chaotic synchronization between two dynamic systems, such as Hopfield neural networks (HNNs), was used to simulate chaotic signals. The notion of Homotopy, an argument of topological nature, was used for the synchronization. The results show efficiency when compared to state of the art, in terms of image correlation, histogram analysis and hardware implementation.

Cybersecurity Experimentation is often viewed narrowly in terms of a single technology or experiment. This paper reviews the experimentation life-cycle for two large scale research efforts that span multiple technologies. We identify salient aspects of each cybersecurity program, and capture guidelines based on eight years of experience. Extrapolating, we identify four principles for building future experimental infrastructure: 1) Reduce the cognitive burden on experimenters when designing and operating experiments. 2) Allow experimenters to encode their goals and constraints. 3) Provide flexibility in experimental design. 4) Provide multifaceted guidance to help experimenters produce high-quality experiments. By following these principles, future cybersecurity testbeds can enable significantly higher-quality experiments.

In recent years, IPv6 will gradually replace IPv4 with IPv4 address exhaustion and the rapid development of the Low-Power Wide-Area network (LPWAN) wireless communication technology. This paper proposes a data acquisition and application system based on 6LoWPAN and IPv6 transition technology. The system uses 6LoWPAN and 6to4 tunnel to realize integration of the internal sensor network and Internet to improve the adaptability of the gateway and reduce the average forwarding delay and packet loss rate of small data packet. Moreover, we design and implement the functions of device access management, multiservice data storage and affair data service by combining the C/S architecture with the actual uploaded river quality data. The system has the advantages of flexible networking, low power consumption, rich IPv6 address, high communication security, and strong reusability.

Intentional electromagnetic interference (IEMI) of information and communication devices is based on high-power electromagnetic environments far exceeding the device immunity to electromagnetic interference. IEMI dramatically alters the electromagnetic environment throughout the device by interfering with the electromagnetic waves inside the device and destroying low-tolerance integrated circuits (ICs) and other elements, thereby reducing the availability of the device. In contrast, in this study, by using a hardware Trojan (HT) that is quickly mountable by physically accessing the devices, to locally weaken the immunity of devices, and then irradiating electromagnetic waves of a specific frequency, only the attack targets are intentionally altered electromagnetically. Therefore, we propose a method that uses these electromagnetic changes to rewrite or generate data and commands handled within devices. Specifically, targeting serial communication systems used inside and outside the devices, the installation of an HT on the communication channel weakens local immunity. This shows that it is possible to generate an electrical signal representing arbitrary data on the communication channel by applying electromagnetic waves of sufficiently small output compared with the conventional IEMI and letting the IC process the data. In addition, we explore methods for countering such attacks.

Graphics Processing Units (GPUs) are getting popularly utilized for multi-purpose applications in order to enhance highly performed parallelism of computation. As memory virtualization methods in GPU nodes are not efficiently provided to deal with diverse memory usage patterns for these applications, the success of their execution depends on exclusive and limited use of physical memory in GPU environments. Therefore, it is important to predict a pattern change of GPU memory usage during runtime execution of an application. Data provenance extracted from application characteristics, GPU runtime environments, input, and execution patterns from runtime monitoring, is defined for supporting application management to set runtime configuration and predict an experimental result, and utilize resource with co-located applications. In this paper, we define data provenance of an application on GPUs and manage data by profiling the execution of CUDA scientific applications. Data provenance management helps to predict execution patterns of other similar experiments and plan efficient resource configuration.

This paper presents a data-driven and physics-based method for detection of false data injection (FDI) in Smart Grids (SG). As the power grid transitions to the use of SG technology, it becomes more vulnerable to cyber-attacks like FDI. Current strategies for the detection of bad data in the grid rely on the physics based State Estimation (SE) process and statistical tests. This strategy is naturally vulnerable to undetected bad data as well as false positive scenarios, which means it can be exploited by an intelligent FDI attack. In order to enhance the robustness of bad data detection, the paper proposes the use of data-driven Machine Intelligence (MI) working together with current bad data detection via a combined Chi-squared test. Since MI learns over time and uses past data, it provides a different perspective on the data than the SE, which analyzes only the current data and relies on the physics based model of the system. This combined bad data detection strategy is tested on the IEEE 118 bus system.

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.

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.

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.

In this paper, a novel method for tomographic microwave imaging based on the Compressive Processing (CP) paradigm is proposed. The retrieval of the dielectric profiles of the scatterers is carried out by efficiently solving both the sampling and the sensing problems suitably formulated under the first order Born approximation. Selected numerical results are presented in order to show the improvements provided by the CP with respect to conventional compressive sensing (CSE) approaches.

Decentralized attestation methods for blockchains are currently being discussed and standardized for use cases such as certification, identity and existence proofs. In a blockchain-based attestation, a claim made about the existence of information can be cryptographically verified publicly and transparently. In this paper we explore the attestation of models through globally unique identifiers as a first step towards decentralized applications based on models. As a proof-of-concept we describe a prototypical implementation of a software connector for the ADOxx metamodeling platform. The connector allows for (a.) the creation of claims bound to the identity of an Ethereum account and (b.) their verification on the blockchain by anyone at a later point in time. For evaluating the practical applicability, we demonstrate the application on the Ethereum network and measure and evaluate limiting factors related to transaction cost and confirmation times.

Transacting IoT data must be different in many from traditional approaches in order to build much-needed trust in data marketplaces, trust that will be the key to their sustainability. Data generated internally to an organization is usually not enough to remain competitive, enhance customer experiences, or improve strategic decision-making. In this paper, we propose a decentralized and trustless architecture through the posting of trade records while including the transaction process on distributed ledgers. This approach can efficiently enhance the degree of transparency, as all contract-oriented interactions will be written on-chain. Storage via an end-to-end encrypted message channel allows transmitting and accessing trusted data streams over distributed ledgers regardless of the size or cost of the device, while simultaneously making a verifiable Auth-compliant request to the platform. Furthermore, the platform will complete matching, trading and refunding processes with-out human intervention, and it also protects the rights of data providers and consumers through trading policies which apply revolutionary game theory to the machine economy.

This article describes an approach to identification and certification in decentralized environment. The protocol proposes a way of integration for blockchain technology and web-of-trust concept to create decentralized public key infrastructure with flexible management for user identificators. Besides changing the current public key infrastructure, this system can be used in the Internet of Things (IoT). Each individual IoT sensor must correctly communicate with other components of the system it's in. To provide safe interaction, components should exchange encrypted messages with ability to check their integrity and authenticity, which is presented by this scheme.

Devices on the Internet of Things (IoT) are usually battery-powered and have limited resources. Hence, energy-efficient and lightweight protocols were designed for IoT devices, such as the popular Constrained Application Protocol (CoAP). Yet, CoAP itself does not include any defenses against denial-of-sleep attacks, which are attacks that aim at depriving victim devices of entering low-power sleep modes. For example, a denial-of-sleep attack against an IoT device that runs a CoAP server is to send plenty of CoAP messages to it, thereby forcing the IoT device to expend energy for receiving and processing these CoAP messages. All current security solutions for CoAP, namely Datagram Transport Layer Security (DTLS), IPsec, and OSCORE, fail to prevent such attacks. To fill this gap, Seitz et al. proposed a method for filtering out inauthentic and replayed CoAP messages "en-route" on 6LoWPAN border routers. In this paper, we expand on Seitz et al.'s proposal in two ways. First, we revise Seitz et al.'s software architecture so that 6LoWPAN border routers can not only check the authenticity and freshness of CoAP messages, but can also perform a wide range of further checks. Second, we propose a couple of such further checks, which, as compared to Seitz et al.'s original checks, more reliably protect IoT devices that run CoAP servers from remote denial-of-sleep attacks, as well as from remote exploits. We prototyped our solution and successfully tested its compatibility with Contiki-NG's CoAP implementation.

Return-Oriented Programming (ROP) is a sophisticated exploitation technique that is able to drive target applications to perform arbitrary unintended operations by constructing a gadget chain reusing existing small code sequences (gadgets) collected across the entire code space. In this paper, we propose to address ROP attacks from a different angle-shrinking available code space at runtime. We present ROPStarvation , a generic and transparent ROP countermeasure that defend against all types of ROP attacks with almost zero run-time overhead. ROPStarvation does not aim to completely stop ROP attacks, instead it attempts to significantly increase the bar by decreasing the possibility of launching a successful ROP exploit in reality. Moreover, shrinking available code space at runtime is lightweight that makes ROPStarvation practical for being deployed with high performance requirement. Results show that ROPStarvation successfully reduces the code space of target applications by 85%. With the reduced code segments, ROPStarvation decreases the probability of building a valid ROP gadget chain by 100% and 83% respectively, with the assumptions that whether the adversary knows the vulnerable applications are protected by ROPStarvation . Evaluations on the SPEC CPU2006 benchmark show that ROPStarvation introduces nearly zero (0.2% on average) run-time performance overhead.

In this paper, we describe several practically exploitable fault attacks against OpenSSL's implementation of elliptic curve cryptography, related to the singular curve point decompression attacks of Blömer and Günther (FDTC2015) and the degenerate curve attacks of Neves and Tibouchi (PKC 2016). In particular, we show that OpenSSL allows to construct EC key files containing explicit curve parameters with a compressed base point. A simple single fault injection upon loading such a file yields a full key recovery attack when the key file is used for signing with ECDSA, and a complete recovery of the plaintext when the file is used for encryption using an algorithm like ECIES. The attack is especially devastating against curves with j-invariant equal to 0 such as the Bitcoin curve secp256k1, for which key recovery reduces to a single division in the base field. Additionally, we apply the present fault attack technique to OpenSSL's implementation of ECDH, by combining it with Neves and Tibouchi's degenerate curve attack. This version of the attack applies to usual named curve parameters with nonzero j-invariant, such as P192 and P256. Although it is typically more computationally expensive than the one against signatures and encryption, and requires multiple faulty outputs from the server, it can recover the entire static secret key of the server even in the presence of point validation. These various attacks can be mounted with only a single instruction skipping fault, and therefore can be easily injected using low-cost voltage glitches on embedded devices. We validated them in practice using concrete fault injection experiments on a Rapsberry Pi single board computer running the up to date OpenSSL command line tools-a setting where the threat of fault attacks is quite significant.