Biblio

Protection of secured data content in volatile memories (processor caches, embedded RAMs etc) is essential in networking, wireless, automotive and other embedded secure applications. It is utmost important to protect secret data, like authentication credentials, cryptographic keys etc., stored over volatile memories which can be hacked during normal device operations. Several security attacks like cold boot, disclosure attack, data remanence, physical attack, cache attack etc. can extract the cryptographic keys or secure data from volatile memories of the system. The content protection of memory is typically done by assuring data deletion in minimum possible time to minimize data remanence effects. In today's state-of-the-art SoCs, dedicated hardwares are used to functionally erase the private memory contents in case of security violations. This paper, in general, proposes a novel approach of using existing memory built-in-self-test (MBIST) hardware to zeroize (initialize memory to all zeros) on-chip memory contents before it is being hacked either through different side channels or secuirty attacks. Our results show that the proposed MBIST based content zeroization approach is substantially faster than conventional techniques. By adopting the proposed approach, functional hardware requirement for memory zeroization can be waived.

Back-end-of-line compatible integration of NdFeB-based micro magnets onto 8 inch Si substrates is presented. Substrate conditioning procedures to enable further processing in a cleanroom environment are discussed. It is shown that permanent magnetic structures with lateral dimensions between 25μm and 2000μm and a depth up to 500μm can be fabricated reliably and reproducibly with a remanent magnetization of 340mT at a standard deviation as low as 5% over the substrate. To illustrate post-processing capabilities, the fabrication of micro magnet arrangements embedded in silicon frames is described.

In this paper, we consider the problem of designing system parameters to improve detection of attacks in control systems. Specifically, we study control systems which are vulnerable to integrity attacks on sensors and actuators. We aim to defend against strong model aware adversaries that can read and modify all sensors and actuators. Previous work has proposed a moving target defense for detecting integrity attacks on control systems. Here, an authenticating subsystem with time-varying dynamics coupled to the original plant is introduced. Due to this coupling, an attack on the original system will affect the authenticating subsystem and in turn be revealed by a set of sensors measuring the extended plant. Moreover, the time-varying dynamics of the extended plant act as a moving target, preventing an adversary from developing an effective adaptive attack strategy. Previous work has failed to consider the design of the time-varying system matrices and as such provides little in terms of guidelines for implementation in real systems. This paper proposes two optimization problems for designing these matrices. The first designs the auxiliary actuators to maximize detection performance while the second designs the coupling matrices to maximize system estimation performance. Numerical examples are presented that validate our approach.

Atomic multicast is a communication primitive that delivers messages to multiple groups of processes according to some total order, with each group receiving the projection of the total order onto messages addressed to it. To be scalable, atomic multicast needs to be genuine, meaning that only the destination processes of a message should participate in ordering it. In this paper we propose a novel genuine atomic multicast protocol that in the absence of failures takes as low as 3 message delays to deliver a message when no other messages are multicast concurrently to its destination groups, and 5 message delays in the presence of concurrency. This improves the latencies of both the fault-tolerant version of classical Skeen's multicast protocol (6 or 12 message delays, depending on concurrency) and its recent improvement by Coelho et al. (4 or 8 message delays). To achieve such low latencies, we depart from the typical way of guaranteeing fault-tolerance by replicating each group with Paxos. Instead, we weave Paxos and Skeen's protocol together into a single coherent protocol, exploiting opportunities for white-box optimisations. We experimentally demonstrate that the superior theoretical characteristics of our protocol are reflected in practical performance pay-offs.

The threat of side-channels is becoming increasingly prominent for resource-constrained internet-connected devices. While numerous power side-channel countermeasures have been proposed, a promising approach to protect the non-invasive electromagnetic side-channel attacks has been relatively scarce. Today's availability of high-resolution electromagnetic (EM) probes mandates the need for a low-overhead solution to protect EM side-channel analysis (SCA) attacks. This work, for the first time, performs a white-box analysis to root-cause the origin of the EM leakage from an integrated circuit. System-level EM simulations with Intel 32 nm CMOS technology interconnect stack, as an example, reveals that the EM leakage from metals above layer 8 can be detected by an external non-invasive attacker with the commercially available state-of-the-art EM probes. Equipped with this `white-box' understanding, this work proposes S℡LAR: Signature aTtenuation Embedded CRYPTO with Low-Level metAl Routing, which is a two-stage solution to eliminate the critical signal radiation from the higher-level metal layers. Firstly, we propose routing the entire cryptographic core within the local lower-level metal layers, whose leakage cannot be picked up by an external attacker. Then, the entire crypto IP is embedded within a Signature Attenuation Hardware (SAH) which in turn suppresses the critical encryption signature before it routes the current signature to the highly radiating top-level metal layers. System-level implementation of the S℡LAR hardware with local lower-level metal routing in TSMC 65 nm CMOS technology, with an AES-128 encryption engine (as an example cryptographic block) operating at 40 MHz, shows that the system remains secure against EM SCA attack even after 1M encryptions, with 67% energy efficiency and 1.23× area overhead compared to the unprotected AES.

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.

Current procedures for anomaly detection in self-organizing mobile communication networks use network-centric approaches to identify dysfunctional serving nodes. In this paper, a user-centric approach and a novel methodology for anomaly detection is proposed, where the Quality of Experience (QoE) metric is used to evaluate the end-user experience. The system model demonstrates how dysfunctional serving eNodeBs are successfully detected by implementing a parametric QoE model using machine learning for prediction of user QoE in a network scenario created by the ns-3 network simulator. This approach can play a vital role in the future ultra-dense and green mobile communication networks that are expected to be both self- organizing and self-healing.

The realization principle of zero-time self-healing network communication technology is introduced. According to the characteristics of ship monitoring, an integrated ship monitoring network is designed, which integrates the information of ship monitoring equipment. By setting up a network performance test environment, the information delay of self-healing network switch is tested, and the technical characteristics of "no packet loss" are verified. Zero-time self-healing network communication technology is an innovative technology in the design of ship monitoring network. It will greatly reduce the laying of network cables, reduce the workload of information upgrade and transformation of ships, and has the characteristics of continuous maintenance of the network. It has a wide application prospect.

Heterogeneous Networks (Het Nets) are introduced to fulfill the increasing demands of wireless communications. To be manageable, it is expected that these networks are self-organized and in particular, self-healing to detect and relief faults autonomously. In the Cooperative Cell Outage Detection (COD), the Macro-Base Station (MBS) and a group of Femto-Base Stations (FBSs) in a specific range are cooperatively communicating to find out if each FBS is working properly or not. In this paper, we discuss the impacts of the cooperation range on the detection delay and accuracy and then conclude that there is an optimal amount for cooperation range which maximizes detection accuracy. We then derive the optimal cooperative range that improves the detection accuracy by using network parameters such as FBS's transmission power, noise power, shadowing fading factor, and path-loss exponent and investigate the impacts of these parameters on the optimal cooperative range. The simulation results show the optimal cooperative range that we proposed maximizes the detection accuracy.

So-called IoT, based on use of enabling technologies like 5G, Wi-Fi, BT, NFC, RFID, IPv6 as well as being widely applied for sensor networks, robots, Wearable and Cyber-PHY, invades rapidly to our every day. There are a lot of apps and software platforms to IoT support. However, a most important problem of QoS optimization, which lays in Performance, Reliability and Scalability for IoT, is not yet solved. The extended Internet of the future needs these solutions based on the cooperation between fog and clouds with delegating of the analytics blocks via agents, adaptive interfaces and protocols. The next problem is as follows: IoT can generate large arrays of unmanaged, weakly-structured, and non-configured data of various types, known as "Big Data". The given papers deals with the both problems. A special problem is Security and Privacy in potentially "dangerous" IoTscenarios. Anyway, this subject needs as special discussion for risks evaluation and cooperative intrusion detection. Some advanced approaches for optimization of Performance, Reliability and Scalability for IoT-solutions are offered within the paper. The paper discusses the Best Practises and Case Studies aimed to solution of the established problems.

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.

Containers have become increasingly popular for deploying applications in cloud computing infrastructures. However, recent studies have shown that containers are prone to various security attacks. In this paper, we conduct a study on the effectiveness of various vulnerability detection schemes for containers. Specifically, we implement and evaluate a set of static and dynamic vulnerability attack detection schemes using 28 real world vulnerability exploits that widely exist in docker images. Our results show that the static vulnerability scanning scheme only detects 3 out of 28 tested vulnerabilities and dynamic anomaly detection schemes detect 22 vulnerability exploits. Combining static and dynamic schemes can further improve the detection rate to 86% (i.e., 24 out of 28 exploits). We also observe that the dynamic anomaly detection scheme can achieve more than 20 seconds lead time (i.e., a time window before attacks succeed) for a group of commonly seen attacks in containers that try to gain a shell and execute arbitrary code.

this paper first analyses the new challenges of power information network management, difficulties of the power information network resource survey and vulnerability detection are proposed. Then, a linkage model of network resource survey and vulnerability detection is designed, and the framework of three modules in the model is described, meanwhile the process of network resources survey and vulnerability detection linkage is proposed. Finally, the implementation technologies are given corresponding to the main functions of each module.

Wireless sensor networks are called wireless networks consisting of low-cost sensor nodes that use limited resources, collect and distribute data. Wireless sensor networks make observation and control of physical environments from distance easier. They are used in a variety of areas, such as environmental surveillance, military purposes, and the collection of information in specific areas. While the low cost of sensor nodes allows it to spread and increase it's quantitative, battery and computational constraints, noise and manipulation threats from the environment cause various challenges in wireless sensor applications. To overcome these challenges, researches have conducted a lot of researches on various fields like power consumption, use of resources and security approaches. In these studies, routing, placement algorithms and system designs are generally examined for efficient energy consumption. In this article, the relationship between the security of sensor networks and efficient resource usage and various scenarios are presented.

In order to improve the information security level of intelligent substation, this paper proposes an intelligent substation information security assessment tool through the research and analysis of intelligent substation information security risk and information security assessment method, and proves that the tool can effectively detect it. It is of great significance to carry out research on industrial control systems, especially intelligent substation information security.

While the general idea of Intelligent Transportation System (ITS) is to employ suitable, sophisticated information and communications technologies, however, such tool also encompass many system complexities. Fittingly, this paper aims to highlight the most contemporary system complications of ITS and in doing so, will also underline the safety, security and risk management concerns. More importantly, effectively treating such issues will ultimately improve the reliability and efficiency of transportation systems. Whereas such issues are among the most significant subjects for any intelligent system, for ITS in particular they the most dominant. For such intelligent systems, the safety, security and risk management issues must not only be decidedly prioritized, but also methodically integrated. As a part of such ITS integration, this paper will delicately examine the Emergency Management System (EMS) development and application. Accurate EMS is not only a mandatory feature of intelligent systems, but it is a fundamental component of ITS which will vigilantly respond to safety, security and risk management apprehensions. To further substantiate such scheme, the Sydney Metro's EMS will be also conferred. It was determined that, the Sydney Metro's EMS although highly advanced, it was also vigilantly aligned with specific designated safety, security and risk management strategies.

In this paper, we consider one of the approaches to the study of the characteristics of an information system that is under the influence of various factors, and their management using neural networks and wavelet transforms based on determining the relationship between the modified state of the information system and the possibility of dynamic analysis of effects. At the same time, the process of influencing the information system includes the following components: impact on the components providing the functions of the information system; determination of the result of exposure; analysis of the result of exposure; response to the result of exposure. As an input signal, the characteristics of the means that affect are taken. The system includes an adaptive response unit, the input of which receives signals about the prerequisites for changes, and at the output, this unit generates signals for the inclusion of appropriate means to eliminate or compensate for these prerequisites or directly the changes in the information system.

This paper studies secure communication from a multi-antenna transmitter to a single-antenna receiver in the presence of multiple multi-antenna eavesdroppers, considering constraints of security quality of service (QoS), i.e., minimum allowable signal-to-interference-and-noise ratio (SINR) at receiver and maximum tolerable SINR at eavesdroppers. The robust joint optimal beamforming (RJOBF) of secret signal and artificial noise (AN) is designed to minimize transmit power while estimation errors of channel state information (CSI) for wiretap channels are taken into consideration. The formulated design problem is shown to be nonconvex and we transfer it into linear matrix inequalities (LMIs) along with semidefinite relaxation (SDR) technique. The simulation results illustrate that our proposed RJOBF is efficient for power saving in security communication.

As chips become more and more connected, they are more exposed (both to network and to physical attacks). Therefore one shall ensure they enjoy a sufficient protection level. Security within chips is accordingly becoming a hot topic. Incident detection and reporting is one novel function expected from chips. In this talk, we explain why it is worthwhile to resort to Artificial Intelligence (AI) for security event handling. Drivers are the need to aggregate multiple and heterogeneous security sensors, the need to digest this information quickly to produce exploitable information, and so while maintaining a low false positive detection rate. Key features are adequate learning procedures and fast and secure classification accelerated by hardware. A challenge is to embed such security-oriented AI logic, while not compromising chip power budget and silicon area. This talk accounts for the opportunities permitted by the symbiotic encounter between chip security and AI.

Cyber security has been a top concern for electric power companies deploying smart meters and smart grid technology. Despite the well-known advantages of smart grid technology and the smart meters, it is not yet very clear how and to what extent, the Cyber attacks can hamper the operation of the smart meters, and remote data collections regarding the power usage from the customer sites. To understand these questions, we conducted experiments in a controlled lab environment of our cyber security lab to test a commercial grade smart meter. In this paper, we present results of our investigation for a commercial grade smart meter and measure the operation integrity of the smart meter under cyber-attack conditions.

Due to the rise of huge population in mankind and the large variety of upcoming utilization of power, the energy requirement has substantially increased. Smart Grid is a very important part of the Smart Cities initiative and is one of the crucial components in distribution and reconciliation of energy. Security of the smart grid infrastructure, which is an integral part of the smart grid framework, intended at transitioning the conventional power grid system into a robust, reliable, adaptable and intelligent energy utility, is an impending problem that needs to be arrested quickly. With the increasingly intensifying integration of smart devices in the smart grid infrastructure with other interconnected applications and the communication backbone is compelling both the energy users and the energy utilities to thoroughly look into the privacy and security issues of the smart grid. In this paper, we present challenges of the existing security mechanisms deployed in the smart grid framework and we tried to bring forward the unresolved problems that would highlight the security aspects of Smart Grid as a challenging area of research and development in the future.

The Internet of Things (IoT) is rapidly evolving, while introducing several new challenges regarding security, resilience and operational assurance. In the face of an increasing attack landscape, it is necessary to cater for the provision of efficient mechanisms to collectively verify software- and device-integrity in order to detect run-time modifications. Towards this direction, remote attestation has been proposed as a promising defense mechanism. It allows a third party, the verifier, to ensure the integrity of a remote device, the prover. However, this family of solutions do not capture the real-time requirements of industrial IoT applications and suffer from scalability and efficiency issues. In this paper, we present a lightweight dynamic control-flow property-based attestation architecture (CFPA) that can be applied on both resource-constrained edge and cloud devices and services. It is a first step towards a new line of security mechanisms that enables the provision of control-flow attestation of only those specific, critical software components that are comparatively small, simple and limited in function, thus, allowing for a much more efficient verification. Our goal is to enhance run-time software integrity and trustworthiness with a scalable and decentralized solution eliminating the need for federated infrastructure trust. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security do not hinder the deployment of intelligent edge computing systems.

This paper presents a study that examines whether social agents can elicit the social emotion shame as humans do. For that, we use job interviews, which are highly evaluative situations per se. We vary the interview style (shame-eliciting vs. neutral) and the job interviewer (human vs. social agent). Our dependent variables include observational data regarding the social signals of shame and shame regulation as well as self-assessment questionnaires regarding the felt uneasiness and discomfort in the situation. Our results indicate that social agents can elicit shame to the same amount as humans. This gives insights about the impact of social agents on users and the emotional connection between them.

The paper shows the influence of deactivated agents in social networks: switching between French-De Groot models and Friedkin-Johnsen model.

The tremendous advances in information and communications technology (ICT), as well as the embedded systems, have been led to the emergence of the novel concept of the internet of things (IoT). Enjoying IoT-based technologies, many objects and components can be connected to each other through the internet or other modern communicational platforms. Embedded systems which are computing machines for special purposes like those utilized in high-tech devices, smart buildings, aircraft, and vehicles including advanced controllers, sensors, and meters with the ability of information exchange using IT infrastructures. The phrase "internet", in this context, does not exclusively refer to the World Wide Web rather than any type of server-based or peer-to-peer networks. In this study, the application of IoT in smart grids is addressed. Hence, at first, an introduction to the necessity of deployment of IoT in smart grids is presented. Afterwards, the applications of IoT in three levels of generation, transmission, and distribution is proposed. The generation level is composed of applications of IoT in renewable energy resources, wind and solar in particular, thermal generation, and energy storage facilities. The deployment of IoT in transmission level deals with congestion management in power system and guarantees the security of the system. In the distribution level, the implications of IoT in active distribution networks, smart cities, microgrids, smart buildings, and industrial sector are evaluated.