Biblio

Searchable Symmetric Encryption aims at making possible searching over an encrypted database stored on an untrusted server while keeping privacy of both the queries and the data, by allowing some small controlled leakage to the server. Recent work shows that dynamic schemes – in which the data is efficiently updatable – leaking some information on updated keywords are subject to devastating adaptative attacks breaking the privacy of the queries. The only way to thwart this attack is to design forward private schemes whose update procedure does not leak if a newly inserted element matches previous search queries. This work proposes Sophos as a forward private SSE scheme with performance similar to existing less secure schemes, and that is conceptually simpler (and also more efficient) than previous forward private constructions. In particular, it only relies on trapdoor permutations and does not use an ORAM-like construction. We also explain why Sophos is an optimal point of the security/performance tradeoff for SSE. Finally, an implementation and evaluation results demonstrate its practical efficiency.

We evaluate the use of LEDs intended for illumination as low-cost filtered optical detectors. An optical wireless system that is exclusively based on commercial off-the-shelf 5-mm R/G/B LEDs is experimentally demonstrated for Gb/s close-proximity transmission.

With the wide application of Internet technology in the industrial control field, industrial control networks are getting larger and larger, and the industrial data generated by industrial control systems are increasing dramatically, and the performance requirements of the acquisition and storage systems are getting higher and higher. The collection and analysis of industrial equipment work logs and industrial timing data can realize comprehensive management and continuous monitoring of industrial control system work status, as well as intrusion detection and energy efficiency analysis in terms of traffic and data. In the face of increasingly large realtime industrial data, existing log collection systems and timing data gateways, such as packet loss and other phenomena [1], can not be more complete preservation of industrial control network thermal data. The emergence of software-defined networking provides a new solution to realize massive thermal data collection in industrial control networks. This paper proposes a 10-gigabit industrial thermal data acquisition and storage scheme based on software-defined networking, which uses software-defined networking technology to solve the problem of insufficient performance of existing gateways.

This paper reports electroplated CoPt permanent magnets samples yielding thicknesses up to 100 μm, deposition rates up to 35 μm/h, coercivities up to 1000 kA/m (1.25 T), remanences up to 0.8 T, and energy products up to 77 kJ/m3. The impact of electroplating bath temperature and glycine additives are systematically studied. Compared to prior work, these microfabricated magnets not only exhibit up to 10X increase in thickness without sacrificing magnetic performance, but also improve the areal magnetic energy density by 2X. Using a thick removeable SU-8 mold, these high-performing thick-film magnets are intended for magnetic microactuators, magnetic field sensors, energy conversion devices, and more.

One important goal of black-box complexity theory is the development of complexity models allowing to derive meaningful lower bounds for whole classes of randomized search heuristics. Complementing classical runtime analysis, black-box models help us understand how algorithmic choices such as the population size, the variation operators, or the selection rules influence the optimization time. One example for such a result is the Ω(n log n) lower bound for unary unbiased algorithms on functions with a unique global optimum [Lehre/Witt, GECCO 2010], which tells us that higher arity operators or biased sampling strategies are needed when trying to beat this bound. In lack of analyzing techniques, almost no non-trivial bounds are known for other restricted models. Proving such bounds therefore remains to be one of the main challenges in black-box complexity theory. With this paper we contribute to our technical toolbox for lower bound computations by proposing a new type of information-theoretic argument. We regard the permutation- and bit-invariant version of LeadingOnes and prove that its (1+1) elitist black-box complexity is Ω(n2), a bound that is matched by (1+1)-type evolutionary algorithms. The (1+1) elitist complexity of LeadingOnes is thus considerably larger than its unrestricted one, which is known to be of order n log log n [Afshani et al., 2013].

This paper presents a 28nm SoC with a programmable FC-DNN accelerator design that demonstrates: (1) HW support to exploit data sparsity by eliding unnecessary computations (4× energy reduction); (2) improved algorithmic error tolerance using sign-magnitude number format for weights and datapath computation; (3) improved circuit-level timing violation tolerance in datapath logic via timeborrowing; (4) combined circuit and algorithmic resilience with Razor timing violation detection to reduce energy via VDD scaling or increase throughput via FCLK scaling; and (5) high classification accuracy (98.36% for MNIST test set) while tolerating aggregate timing violation rates \textbackslashtextgreater10-1. The accelerator achieves a minimum energy of 0.36μJ/pred at 667MHz, maximum throughput at 1.2GHz and 0.57μJ/pred, or a 10%-margined operating point at 1GHz and 0.58μJ/pred.

Game theory serves as a powerful tool for distributed optimization in multiagent systems in different applications. In this paper we consider multiagent systems that can be modeled as a potential game whose potential function coincides with a global objective function to be maximized. This approach renders the agents the strategic decision makers and the corresponding optimization problem the problem of learning an optimal equilibruim point in the designed game. In distinction from the existing works on the topic of payoff-based learning, we deal here with the systems where agents have neither memory nor ability for communication, and they base their decision only on the currently played action and the experienced payoff. Because of these restrictions, we use the methods of reinforcement learning, stochastic approximation, and learning automata extensively reviewed and analyzed in [3], [9]. These methods allow us to set up the agent dynamics that moves the game out of inefficient Nash equilibria and leads it close to an optimal one in both cases of discrete and continuous action sets.

A security breach often makes companies react by changing their attitude and approach to security within the organization. This paper presents an in-depth case study of post-breach security changes made by a company and the consequences of those changes. We employ the principles of participatory action research and humble inquiry to conduct a long-term study with employee interviews while embedded in the organization's security division. Despite an extremely high level of financial investment in security, and consistent attention and involvement from the board, the interviews indicate a significant level of friction between employees and security. In the main themes that emerged from our data analysis, a number of factors shed light on the friction: fear of another breach leading to zero risk appetite, impossible security controls making non-compliance a norm, security theatre underminining the purpose of security policies, employees often trading-off security with productivity, and as such being treated as children in detention rather than employees trying to finish their paid jobs. This paper shows that post-breach security changes can be complex and sometimes risky due to emotions often being involved. Without an approach considerate of how humans and security interact, even with high financial investment, attempts to change an organization's security behaviour may be ineffective.

Attacks infiltrating the integrity of vehicular control systems and medical devices have brought to sharp focus the urgency of securing cyber-physical systems. There is a broader discussion about the role of principled
security -aware design and analysis in the development of both modern engineering systems such as the Smartgrid as well as in future systems that use advanced AI and machine learning in safety critical settings.
Although there has been a growing interest in these security in the CPSWeek commun ity (increasing number of security related papers in ICCPS, HSCC, RTAS, HyCons), this body of research remains largely disconnected from the mainstream systems security research (USENIX, Oakland, CCS, NDSS). The CPS community has developed analysis and synthesis algorithms, verification tools, notions of observability and controllability, and have been in the forefront of research on emerging applications. The connections between this body of work and systems security research remain unexplored. 

The goal of this workshop is to advance the science of security in cyberphysical systems by helping bridge this. We plan to bring together the leaders from these two communities in a full day workshop of invited
sessions and panel discussions. Instead of unstructured technical presentations, the speakers and participants will put their research in the context of some broad topics that will help us bridge this gap. Topics of interest include:

• Identify hard open problems for academic research in CPS security
• Data and testbeds in security research amenable to CPS methods
• Success and fails in designing for resiliency
• Identify CPS tools and techniques (e.g., verification, synthesis) that can advance systems security research
• How to make an impact with CPS security research (where most systems are closed, design cycles are long, and methodologies are slower to change than in cyber systems)
• Metrics for CPS security

This paper proposes the implementation of progressive authentication service in smart android mobile phone. In this digital era, massive amount of work can be done in the digital form using the smart devices like smart phone , laptop, Tablets, etc. The number of smartphone users approx. reach to 299.24 million, as per the recent survey report [1] in 2019 this count will reach 2.7 billion and after 3 years, this count will increase up to 442.5 million. This article includes a cluster based progressive smart lock with a dependent combination that is short and more secure in nature. Android provides smart lock facilities with the combination of 9 dot, 6dot, 5dot, 4dot and 1-9 number. By using this mobile phone user will be able to generate pattern lock or number password for authentication. This is a single authentication system, this research paper includes a more secured multiple cluster based pattern match system.

FastChain is a simulator built in NS-3 which simulates the networked battlefield scenario with military applications, connecting tankers, soldiers and drones to form Internet-of-Battlefield-Things (IoBT). Computing, storage and communication resources in IoBT are limited during certain situations in IoBT. Under these circumstances, these resources should be carefully combined to handle the task to accomplish the mission. FastChain simulator uses Sharding approach to provide an efficient solution to combine resources of IoBT devices by identifying the correct and the best set of IoBT devices for a given scenario. Then, the set of IoBT devices for a given scenario collaborate together for sharding enabled Blockchain technology. Interested researchers, policy makers and developers can download and use the FastChain simulator to design, develop and evaluate blockchain enabled IoBT scenarios that helps make robust and trustworthy informed decisions in mission-critical IoBT environment.

Cryptocurrencies are the digital currencies designed to replace the regular cash money while taking place in our daily lives especially for the last couple of years. Mining cryptocurrencies are one of the popular ways to have them and make a profit due to unstable values in the market. This attracts attackers to utilize malware on internet users' computer resources, also known as cryptojacking, to mine cryptocurrencies. Cryptojacking started to be a major issue in the internet world. In this case, we developed MiNo, a web browser add-on application to detect these malicious mining activities running without the user's permission or knowledge. This add-on provides security and efficiency for the computer resources of the internet users. MiNo designed and developed with double-layer protection which makes it ahead of its competitors in the market.

There is expected to be a surge in deepfakes during the 2020 presidential campaigns. According to experts, little has been done to prepare for fake videos in which candidates are depicted unfavorably in order to sway public perception.

Governments needs reliable data on crime in order to both devise adequate policies, and allocate the correct revenues so that the measures are cost-effective, i.e., The money spent in prevention, detection, and handling of security incidents is balanced with a decrease in losses from offences. The analysis of the actual scenario of government actions in cyber security shows that the availability of multiple contrasting figures on the impact of cyber-attacks is holding back the adoption of policies for cyber space as their cost-effectiveness cannot be clearly assessed. The most relevant literature on the topic is reviewed to highlight the research gaps and to determine the related future research issues that need addressing to provide a solid ground for future legislative and regulatory actions at national and international levels.

Ransomwares have become a growing threat since 2012, and the situation continues to worsen until now. The lack of security mechanisms and security awareness are pushing the systems into mire of ransomware attacks. In this paper, a new framework called 2entFOX' is proposed in order to detect high survivable ransomwares (HSR). To our knowledge this framework can be considered as one of the first frameworks in ransomware detection because of little publicly-available research in this field. We analyzed Windows ransomwares' behaviour and we tried to find appropriate features which are particular useful in detecting this type of malwares with high detection accuracy and low false positive rate. After hard experimental analysis we extracted 20 effective features which due to two highly efficient ones we could achieve an appropriate set for HSRs detection. After proposing architecture based on Bayesian belief network, the final evaluation is done on some known ransomware samples and unknown ones based on six different scenarios. The result of this evaluations shows the high accuracy of 2entFox in detection of HSRs.

Voice controlled interfaces have vastly improved the usability of many devices (e.g., headless IoT systems). Unfortunately, the lack of authentication for these interfaces has also introduced command injection vulnerabilities - whether via compromised IoT devices, television ads or simply malicious nearby neighbors, causing such devices to perform unauthenticated sensitive commands is relatively easy. We address these weaknesses with Two Microphone Authentication (2MA), which takes advantage of the presence of multiple ambient and personal devices operating in the same area. We develop an embodiment of 2MA that combines approximate localization through Direction of Arrival (DOA) techniques with Robust Audio Hashes (RSHs). Our results show that our 2MA system can localize a source to within a narrow physical cone (\$\textbackslashtextless30ˆ\textbackslashtextbackslashcirc \$) with zero false positives, eliminate replay attacks and prevent the injection of inaudible/hidden commands. As such, we dramatically increase the difficulty for an adversary to carry out such attacks and demonstrate that 2MA is an effective means of authenticating and localizing voice commands.

Radio Frequency Identification (RFID) improves the efficiency of managing assets in supply chain applications throughout an entire life cycle or while in transport. Transfer of ownership of RFID-tagged items involves replacing information authorizing the old owner with information authorizing the new owner. In this work, we present a two-party, multiple tag, single-owner protocol for ownership transfer: 2P-mtOTP. This two-party protocol depends only on the communication among the two owners and the tags. Further, 2P-mtOTP is robust to attacks on its security, and it preserves the privacy of the owners and tags. We analyze our work in comparison to recent ownership transfer protocols in terms of security, privacy, and efficiency.

A power-efficient programmable-gain control function embedded Delta-Sigma (ΔΣ) analog-to-digital converter (ADC) for various smart sensor applications is presented. It consists of a programmable-gain switched-capacitor ΔΣ modulator followed by a digital decimation filter for down-sampling. The programmable function is realized with programmable coefficients of a loop filter using a capacitor array. The coefficient control is accomplished with keeping the location of poles of a noise transfer function, so the stability of a designed closed-loop transfer function can be assured. The proposed gain control method helps ADC to optimize its performance with varying input signal magnitude. The gain controllability requires negligible additional energy consuming or area occupying block. The power efficient programmable-gain ADC (PGADC) is well-suited for sensor devices. The gain amplification can be optimized from 0 to 18 dB with a 6 dB step. Measurements show that the PGADC achieves 15.2-bit resolution and 12.4-bit noise free resolution with 99.9 % reliability. The chip operates with a 3.3 V analog supply and a 1.8 V digital supply, while consuming only 97 μA analog current and 37 μA digital current. The analog core area is 0.064 mm2 in a standard 0.18-μm CMOS process.