Biblio

In ad-hoc networks, data messages are transmitted from a source wireless node to a destination one along a wireless multihop transmission route consisting of a sequence of intermediate wireless nodes. Each intermediate wireless node forwards data messages to its next-hop wireless node. Here, a wireless signal carrying the data message is broadcasted by using an omni antenna and it is not difficult for a eavesdropper wireless node to overhear the wireless signal to get the data message. Some researches show that it is useful to transmit noise wireless signal which collide to the data message wireless signal in order for interfering the overhearing. However, some special devices such as directional antennas and/or high computation power for complicated signal processing are required. For wireless multihop networks with huge number of wireless nodes, small and cheap wireless nodes are mandatory for construction of the network. This paper proposes the method for interfering the overhearing by the eavesdropper wireless nodes where routing protocol and data message transmission protocol with cooperative noise signal transmissions by 1-hop and 2-hop neighbor wireless nodes of each intermediate wireless node.

With the advancement of Internet in Things (IoT) more and more "things" are connected to each other through the Internet. Due to the fact that the collected information may contain personal information of the users, it is very important to ensure the security of the devices in IoT. Diversification is a promising technique that protects the software and devices from harmful attacks and malware by making interfaces unique in each separate system. In this paper we apply diversification on the interfaces of IoT operating systems. To this aim, we introduce the diversification in post-compilation and linking phase of the software life-cycle, by shuffling the order of the linked objects while preserving the semantics of the code. This approach successfully prevents malicious exploits from producing adverse effects in the system. Besides shuffling, we also apply library symbol diversification method, and construct needed support for it e.g. into the dynamic loading phase. Besides studying and discussing memory layout shuffling and symbol diversification as a security measures for IoT operating systems, we provide practical implementations for these schemes for Thingsee OS and Raspbian operating systems and test these solutions to show the feasibility of diversification in IoT environments.

Existing security mechanisms for managing the Internet infrastructural resources like IP addresses, AS numbers, BGP advertisements and DNS mappings rely on a Public Key Infrastructure (PKI) that can be potentially compromised by state actors and Advanced Persistent Threats (APTs). Ideally the Internet infrastructure needs a distributed and tamper-resistant resource management framework which cannot be subverted by any single entity. A secure, distributed ledger enables such a mechanism and the blockchain is the best known example of distributed ledgers. In this paper, we propose the use of a blockchain based mechanism to secure the Internet BGP and DNS infrastructure. While the blockchain has scaling issues to be overcome, the key advantages of such an approach include the elimination of any PKI-like root of trust, a verifiable and distributed transaction history log, multi-signature based authorizations for enhanced security, easy extensibility and scriptable programmability to secure new types of Internet resources and potential for a built in cryptocurrency. A tamper resistant DNS infrastructure also ensures that it is not possible for the application level PKI to spoof HTTPS traffic.

Vehicle localization is important in many applications of vehicular networks. The Global Positioning System (GPS) has been critical for vehicle localization. However, the case where the GPS is spoofed through a false data injection attack can be lead to devastating consequences, especially in localization solutions that make use of cooperation among multiple vehicles. Hence, resilient localization algorithms are needed that can achieve a baseline of performance in the case of a false data injection attack. This poster presents preliminary results of an inter-vehicle communication assisted localization algorithm that is resilient to false data injection attacks for the vehicles not directly attacked. The algorithm makes use of V2V and V2I communication – along with on-board GPS receiver, odometer, and compass – to achieve precise localization results.

While power grid systems benefit from utilizing communication network through networked control and protection, the addition of communication exposes the power system to new security vulnerabilities and potential attacks. To mitigate these attacks, such as denial of service, intrusion detection systems (IDS) are often employed. In this paper we investigate the relationship of IDS accuracy performance to the stability of power systems via its impact on communication latency. Several IDS machine learning algorithms are implemented on the NSL-KDD dataset to obtain accuracy performance, and a mathematical model for computing the latency when incorporating IDS detection information during network routing is introduced. Simulation results on the New England 39-bus power system suggest that during a cyber-physical attack, a practical IDS can achieve similar stability as an ideal IDS with perfect detection. In addition, false positive rate has been found to have a larger impact than false negative rate under the simulation conditions studied. These observations can contribute to the design requirements of future embedded IDS solutions for power systems.

By connecting devices, people, vehicles and infrastructures everywhere in a city, governments and their partners can improve community wellbeing and other economic and financial aspects (e.g., cost and energy savings). Nonetheless, smart cities are complex ecosystems that comprise many different stakeholders (network operators, managed service providers, logistic centers...) who must work together to provide the best services and unlock the commercial potential of the IoT. This is one of the major challenges that faces today's smart city movement, and more generally the IoT as a whole. Indeed, while new smart connected objects hit the market every day, they mostly feed "vertical silos" (e.g., vertical apps, siloed apps...) that are closed to the rest of the IoT, thus hampering developers to produce new added value across multiple platforms. Within this context, the contribution of this paper is twofold: (i) present the EU vision and ongoing activities to overcome the problem of vertical silos; (ii) introduce recent IoT standards used as part of a recent Horizon 2020 IoT project to address this problem. The implementation of those standards for enhanced sporting event management in a smart city/government context (FIFA World Cup 2022) is developed, presented, and evaluated as a proof-of-concept.

Background: The NASA datasets have previously been used extensively in studies of software defects. In 2013 Shepperd et al. presented an essential set of rules for removing erroneous data from the NASA datasets making this data more reliable to use. Objective: We have now found additional rules necessary for removing problematic data which were not identified by Shepperd et al. Results: In this paper, we demonstrate the level of erroneous data still present even after cleaning using Shepperd et al.'s rules and apply our new rules to remove this erroneous data. Conclusion: Even after systematic data cleaning of the NASA MDP datasets, we found new erroneous data. Data quality should always be explicitly considered by researchers before use.

Aliasing is a known source of challenges in the context of imperative object-oriented languages, which have led to important advances in type systems for aliasing control. However, their large-scale adoption has turned out to be a surprisingly difficult challenge. While new language designs show promise, they do not address the need of aliasing control in existing languages. This paper presents a new approach to isolation and uniqueness in an existing, widely-used language, Scala. The approach is unique in the way it addresses some of the most important obstacles to the adoption of type system extensions for aliasing control. First, adaptation of existing code requires only a minimal set of annotations. Only a single bit of information is required per class. Surprisingly, the paper shows that this information can be provided by the object-capability discipline, widely-used in program security. We formalize our approach as a type system and prove key soundness theorems. The type system is implemented for the full Scala language, providing, for the first time, a sound integration with Scala's local type inference. Finally, we empirically evaluate the conformity of existing Scala open-source code on a corpus of over 75,000 LOC.

There are seemingly many advantages to being able to identify, document, test, and trace single or "atomic" requirements. Why then has there been little attention to the topic and no widely used definition or process on how to define atomic requirements? Definitions of requirements and standards focus on user needs, system capabilities or functions; some definitions include making individual requirements singular or without the use of conjunctions. In a few cases there has been a description of atomic system events or requirements. This work is surveyed here although there is no well accepted and used best practice for generating atomic requirements. Due to their importance in software engineering, quality and metrics for requirements have received considerable attention. In the seminal paper on software requirements quality, Davis et al. proposed specific metrics including the "unambiguous quality factor" and the "verifiable quality factor"; these and other metrics work best with a clearly enumerable list of single requirements. Atomic requirements are defined here as a natural language statement that completely describes a single system function, feature, need, or capability, including all information, details, limits, and characteristics. A typical user login screen is used as an example of an atomic requirement which can include both functional and nonfunctional requirements. Individual atomic requirements are supported by a system glossary, references to applicable industry standards, mock ups of the user interface, etc. One way to identify such atomic requirements is from use case or system event analysis. This definition of atomic requirements is still a work in progress and offered to prompt discussion. Atomic requirements allow clear naming or numbering of requirements for traceability, change management, and importance ranking. Further, atomic requirements defined in this manner are suitable for rapid implementation approaches (implementing one requirement at a time), enable good test planning (testing can clearly indicate pass or fail of the whole requirement), and offer other management advantages in project control.

Lattice-based cryptography has gained credence recently as a replacement for current public-key cryptosystems, due to its quantum-resilience, versatility, and relatively low key sizes. To date, encryption based on the learning with errors (LWE) problem has only been investigated from an ideal lattice standpoint, due to its computation and size efficiencies. However, a thorough investigation of standard lattices in practice has yet to be considered. Standard lattices may be preferred to ideal lattices due to their stronger security assumptions and less restrictive parameter selection process. In this paper, an area-optimised hardware architecture of a standard lattice-based cryptographic scheme is proposed. The design is implemented on a FPGA and it is found that both encryption and decryption fit comfortably on a Spartan-6 FPGA. This is the first hardware architecture for standard lattice-based cryptography reported in the literature to date, and thus is a benchmark for future implementations. Additionally, a revised discrete Gaussian sampler is proposed which is the fastest of its type to date, and also is the first to investigate the cost savings of implementing with λ/2-bits of precision. Performance results are promising compared to the hardware designs of the equivalent ring-LWE scheme, which in addition to providing stronger security proofs; generate 1272 encryptions per second and 4395 decryptions per second.

This paper calls for the attention to investigate real-world malwares in large scales by examining the largest real malware repository, VirusTotal. As a first step, we analyzed two fundamental characteristics of Windows executable malwares from VirusTotal. We designed offline and online tools for this analysis. Our results show that malwares appear in bursts and that distributions of malwares are highly skewed.

From pencils to commercial aircraft, every man-made object must be designed and manufactured. When it is cheaper or easier to steal a design or a manufacturing process specification than to invent one's own, the incentive for theft is present. As more and more manufacturing data comes online, incidents of such theft are increasing. In this paper, we present a side-channel attack on manufacturing equipment that reveals both the form of a product and its manufacturing process, i.e., exactly how it is made. In the attack, a human deliberately or accidentally places an attack-enabled phone close to the equipment or makes or receives a phone call on any phone nearby. The phone executing the attack records audio and, optionally, magnetometer data. We present a method of reconstructing the product's form and manufacturing process from the captured data, based on machine learning, signal processing, and human assistance. We demonstrate the attack on a 3D printer and a CNC mill, each with its own acoustic signature, and discuss the commonalities in the sensor data captured for these two different machines. We compare the quality of the data captured with a variety of smartphone models. Capturing data from the 3D printer, we reproduce the form and process information of objects previously unknown to the reconstructors. On average, our accuracy is within 1 mm in reconstructing the length of a line segment in a fabricated object's shape and within 1 degree in determining an angle in a fabricated object's shape. We conclude with recommendations for defending against these attacks.

In IoT environments, the user may have many devices to connect each other and share the data. Also, the device will not have the powerful computation and storage ability. Many studies have focused on the lightweight authentication between the cloud server and the client in this environment. They can use the cloud server to help sensors or proxies to finish the authentication. But in the client side, how to create the group session key without the cloud capability is the most important issue in IoT environments. The most popular application network of IoT environments is the wireless body area network (WBAN). In WBAN, the proxy usually needs to control and monitor user's health data transmitted from the sensors. In this situation, the group authentication and group session key generation is needed. In this paper, in order to provide an efficient and robust group authentication and group session key generation in the client side of IoT environments, we propose a lightweight authentication scheme with dynamic group members in IoT environments. Our proposed scheme can satisfy the properties including the flexible generation of shared group keys, the dynamic participation, the active revocation, the low communication and computation cost, and no time synchronization problem. Also our scheme can achieve the security requirements including the mutual authentication, the group session key agreement, and prevent all various well-known attacks.

Despite a long history and numerous proposed defenses, memory corruption attacks are still viable. A secure and low-overhead defense against return-oriented programming (ROP) continues to elude the security community. Currently proposed solutions still must choose between either not fully protecting critical data and relying instead on information hiding, or using incomplete, coarse-grain checking that can be circumvented by a suitably skilled attacker. In this paper, we present a light-weighted memory protection approach (LMP) that uses Intel's MPX hardware extensions to provide complete, fast ROP protection without having to rely in information hiding. We demonstrate a prototype that defeats ROP attacks while incurring an average runtime overhead of 3.9%.

The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin is a peer-to-peer (P2P) cryptocurrency protocol enabling transactions between individuals without the need of a trusted authority. Its network is formed from resources contributed by individuals known as miners. Users of Bitcoin currency create transactions that are stored in a specialised data structure called a block chain. Bitcoin's security lies in a proof-of-work scheme, which requires high computational resources at the miners. These miners have to be synchronised with any update in the network, which produces high data traffic rates. Despite advances in mobile technology, no cryptocurrencies have been proposed for mobile devices. This is largely due to the lower processing capabilities of mobile devices when compared with conventional computers and the poorer Internet connectivity to that of the wired networking. In this work, we propose LocalCoin, an alternative cryptocurrency that requires minimal computational resources, produces low data traffic and works with off-the-shelf mobile devices. LocalCoin replaces the computational hardness that is at the root of Bitcoin's security with the social hardness of ensuring that all witnesses to a transaction are colluders. It is based on opportunistic networking rather than relying on infrastructure and incorporates characteristics of mobile networks such as users' locations and their coverage radius in order to employ an alternative proof-of-work scheme. Localcoin features (i) a lightweight proof-of-work scheme and (ii) a distributed block chain.

Mobile devices offer access to our digital lives and thus need to be protected against the risk of unauthorized physical access by applying strong authentication, which in turn adversely affects usability. The actual risk, however, depends on dynamic factors like day and time. In this paper we discuss the idea of using location-based risk assessment in combination with multi-modal biometrics to adjust the level of authentication necessary to the situational risk of unauthorized access.

Augmented reality is poised to become a dominant computing paradigm over the next decade. With promises of three-dimensional graphics and interactive interfaces, augmented reality experiences will rival the very best science fiction novels. This breakthrough also brings in unique challenges on how users can authenticate one another to share rich content between augmented reality headsets. Traditional authentication protocols fall short when there is no common central entity or when access to the central authentication server is not available or desirable. Looks Good To Me (LGTM) is an authentication protocol that leverages the unique hardware and context provided with augmented reality headsets to bring innate human trust mechanisms into the digital world to solve authentication in a usable and secure way. LGTM works over point to point wireless communication so users can authenticate one another in a variety of circumstances and is designed with usability at its core, requiring users to perform only two actions: one to initiate and one to confirm. Users intuitively authenticate one another, using seemingly only each other's faces, but under the hood LGTM uses a combination of facial recognition and wireless localization to bootstrap trust from a wireless signal, to a location, to a face, for secure and usable authentication.

This work studies applications and generalizations of a simple estimation technique that provides exponential concentration under heavy-tailed distributions, assuming only bounded low-order moments. We show that the technique can be used for approximate minimization of smooth and strongly convex losses, and specifically for least squares linear regression. For instance, our d-dimensional estimator requires just O(d log(1/δ)) random samples to obtain a constant factor approximation to the optimal least squares loss with probability 1-δ, without requiring the covariates or noise to be bounded or subgaussian. We provide further applications to sparse linear regression and low-rank covariance matrix estimation with similar allowances on the noise and covariate distributions. The core technique is a generalization of the median-of-means estimator to arbitrary metric spaces.

Current-mode Analog-to-Digital Converter (ADC) has drawn many attentions due to its high operating speed, power and ground noise immunity, and etc. However, 2n – 1 comparators are required in traditional n-bit current-mode ADC design, leading to inevitable high power consumption and large chip area. In this work, we propose a low power and compact current mode Multi-Threshold Comparator (MTC) based on giant Spin Hall Effect (SHE). The two threshold currents of the proposed SHE-MTC are 200μA and 250μA with 1ns switching time, respectively. The proposed current-mode hybrid spin-CMOS flash ADC based on SHE-MTC reduces the number of comparators almost by half (2n-1), thus correspondingly reducing the required current mirror branches, total power consumption and chip area. Moreover, due to the non-volatility of SHE-MTC, the front-end analog circuits can be switched off when it is not required to further increase power efficiency. The device dynamics of SHE-MTC is simulated using a numerical device model based on Landau-Lifshitz-Gilbert (LLG) equation with Spin-Transfer Torque (STT) term and SHE term. The device-circuit co-simulation in SPICE (45nm CMOS technology) have shown that the average power dissipation of proposed ADC is 1.9mW, operating at 500MS/s with 1.2 V power supply. The INL and DNL are in the range of 0.23LSB and 0.32LSB, respectively.

Wearable personal health monitoring systems can offer a cost effective solution for human healthcare. These systems must provide both highly accurate, secured and quick processing and delivery of vast amount of data. In addition, wearable biomedical devices are used in inpatient, outpatient, and at home e-Patient care that must constantly monitor the patient's biomedical and physiological signals 24/7. These biomedical applications require sampling and processing multiple streams of physiological signals with strict power and area footprint. The processing typically consists of feature extraction, data fusion, and classification stages that require a large number of digital signal processing and machine learning kernels. In response to these requirements, in this paper, a low-power, domain-specific many-core accelerator named Power Efficient Nano Clusters (PENC) is proposed to map and execute the kernels of these applications. Experimental results show that the manycore is able to reduce energy consumption by up to 80% and 14% for DSP and machine learning kernels, respectively, when optimally parallelized. The performance of the proposed PENC manycore when acting as a coprocessor to an Intel Atom processor is compared with existing commercial off-the-shelf embedded processing platforms including Intel Atom, Xilinx Artix-7 FPGA, and NVIDIA TK1 ARM-A15 with GPU SoC. The results show that the PENC manycore architecture reduces the energy by as much as 10X while outperforming all off-the-shelf embedded processing platforms across all studied machine learning classifiers.