Conventional and machine learning approaches as countermeasures against hardware trojan attacks

Every year, the rate at which technology is applied on areas of our everyday life is increasing at a steady pace. This rapid development drives the technology companies to design and fabricate their integrated circuits (ICs) in non-trustworthy outsourcing foundries to reduce the cost, thus, leaving space for a synchronous form of virus, known as Hardware Trojan (HT), to be developed. HTs leak encrypted information, degrade device performance or lead to total destruction. To reduce the risks associated with these viruses, various approaches have been developed aiming to prevent and detect them, based on conventional or machine learning methods. Ideally, any undesired modification made to an IC should be detectable by pre-silicon verification/simulation and post-silicon testing. The infected circuit can be inserted in different stages of the manufacturing process, rendering the detection of HTs a complicated procedure. In this paper, we present a comprehensive review of research dedicated to countermeasures against HTs embedded into ICs. The literature is grouped in four main categories; (a) conventional HT detection approaches, (b) machine learning for HT countermeasures, (c) design for security and (d) runtime monitor.     

Optimal resilient power grid operation during the course of a progressing wildfire

We study a two-stage stochastic and nonlinear optimization model for operating a power grid exposed to a natural disaster. Although this approach can be generalized to any natural hazard of continuous (and not instantaneous) nature, our focus is on wildfires. We assume that an approaching wildfire impacts the power grid by reducing the transmission capacity of its overhead lines. At the time when proactive decisions have to be taken, the severity of the wildfire is not known. This introduces uncertainty. In this paper, we extend previous work by more realistically capturing this uncertainty and by strengthening the mathematical programming formulation through standard reformulation techniques. With these reformulation techniques, the resulting two-stage, convex mixed-integer quadratically constrained programming formulation can be efficiently solved using commercial quadratic programming solvers as demonstrated on a case study on a modified version of the IEEE 123-bus test system with 100 scenarios. We also quantify the uncertainties through a second case study using the following three standard metrics of two-stage stochastic optimization: the expected value of perfect information, the expected result of using the expected value solution and the value of the stochastic solution.     

EPMDA: an efficient privacy-preserving multi-dimensional data aggregation scheme for edge computing-based IoT system

In order to perform multi-dimensional data aggregation operations efficiently in edge computing-based Internet of things (IoT) systems, a new efficient privacy-preserving multi-dimensional data aggregation (EPMDA) scheme is proposed in this paper. EPMDA scheme is characterized by employing the homomorphic Paillier encryption and SM9 signature algorithm. To improve the computation efficiency of the Paillier encryption operation, EPMDA scheme generates a pre-computed modular exponentiation table of each dimensional data,and the Paillier encryption operation can be implemented by using only several modular multiplications. For the multi-dimensional data, the scheme concatenates zeros between two adjacent dimensional data to avoid data overflow in the sum operation of ciphertexts. To enhance security, EPMDA scheme sets random number at the high address of the exponent. Moreover, the scheme utilizes SM9 signature scheme to guarantee device authentication and data integrity. The performance evaluation and comparison show that EPMDA scheme is more efficient than the existing multi-dimensional data aggregation schemes.     

A novel intelligent approach for yaw position forecasting in wind energy systems

Yaw control systems orientate the rotor of a wind turbine into the wind direction, optimize the wind power generated by wind turbines and alleviate the mechanical stresses on a wind turbine. Regarding the advantages of yaw control systems, a k-nearest neighbor classifier (k-NN) has been developed in order to forecast the yaw position parameter at 10-min intervals in this study. Air temperature, atmosphere pressure, wind direction, wind speed, rotor speed and wind power parameters are used in 2, 3, 4, 5 and 6-dimensional input spaces. The forecasting model using Manhattan distance metric for k = 3 uncovered the most accurate performance for atmosphere pressure, wind direction, wind speed and rotor speed inputs. However, the forecasting model using Euclidean distance metric for k = 1 brought out the most inconsistent results for atmosphere pressure and wind speed inputs. As a result of multi-tupled analyses, many feasible inferences were achieved for yaw position control systems. In addition, the yaw position forecasting model developed was compared with the persistence model and it surpassed the persistence model significantly in terms of the improvement percent.     

Energy-optimal base station density in cellular access networks with sleep modes

Sleep modes are widely accepted as an effective technique for energy-efficient networking: by adequately putting to sleep and waking up network resources according to traffic demands, a proportionality between energy consumption and network utilization can be approached, with important reductions in energy consumption. Previous studies have investigated and evaluated sleep modes for wireless access networks, computing variable percentages of energy savings. In this paper we characterize the maximum energy saving that can be achieved in a cellular wireless access network under a given performance constraint. In particular, our approach allows the derivation of realistic estimates of the energy-optimal density of base stations corresponding to a given user density, under a fixed performance constraint. Our results allow different sleep mode proposals to be measured against the maximum theoretically achievable improvement. We show, through numerical evaluation, the possible energy savings in today’s networks, and we further demonstrate that even with the development of highly energy-efficient hardware, a holistic approach incorporating system level techniques is essential to achieving maximum energy efficiency.     

High-temperature stability of Mg(Al,Cr)2O4 spinel co-existing with calcium aluminates in air

Cr₂O₃ is a well-known corrosion resistant oxide used in refractory applications. However, it can oxidize into toxic and water-soluble Cr(VI) compounds upon reaction with calcium aluminate cement phases in the presence of oxygen, which subsequently causes disposal problems after use. This study describes the extent to which chromium in the spinel Mg(Al,Cr)₂O₄ phase can be oxidized to Cr(VI) when it reacts with the calcium aluminate cement phases C₁₂A₇, CA, CA₂ and free CaO at 1300 °C in air, using XRD, XPS and leaching tests (TRGS 613 standard) as analytical tools. On reaction with CaO, the Mg(Al,Cr^III)₂O₄ spinel mainly transformed into hauyne (Ca₄Al₆Cr^VIO₁₆) and Ca₅Cr₃O₁₂ which contains both Cr(IV) and Cr(VI). The reaction of C₁₂A₇ and CA with the spinel phase also resulted in the formation of Ca₄Al₆CrO₁₆. Conversely, the reaction of Mg(Al,Cr^III)₂O₄ spinel with CA₂ resulted in the formation of only a trace amount of Cr(VI). Water-soluble Cr(VI) leached in large quantities (>100 mg/L) from samples where the Mg(Al,Cr^III)₂O₄ reacted with either C₁₂A₇ or CA. Almost no Cr(VI) leached from the sample when Mg(Al,Cr^III)₂O₄ reacted with CaO, using the standard TRGS 613 leach test, but a significant amount of Cr(VI) was released into solution when leached with a HCl solution for 12 h. Both Cr(IV) and Cr(VI) present in the Ca₅Cr₃O₁₂ dissolved into acidic solution. Only a small amount of Cr(VI) leached from the sample that resulted when spinel was reacted with CA₂, even after a prolonged HCl leach. Cr(III) in spinel Mg(Al,Cr)₂O₄ is very stable and does not leach in either distilled water or acidic solution.     

Continuation methods for approximate large scale object sequencing

Machine Learning - We propose a set of highly scalable algorithms for the combinatorial data analysis problem of seriating similarity matrices. Seriation consists of finding a permutation of data...     

基于微博用户兴趣话题的相似用户挖掘

相似用户挖掘是提高社交网络服务质量的重要途径，在面向大数据的社交网络时代，准确的相似用户挖掘对于用户和互联网企业等都有重要的意义，而根据用户自己的兴趣话题挖掘的相似用户更符合相似用户的要求。提出了一种基于用户兴趣话题进行相似用户挖掘的方法。该方法首先使用TextRank话题提取方法对用户进行兴趣话题提取，再对用户发表内容进行训练，计算出所有词之间的相似度。提出CP（Corresponding Position similarity）、CPW（Corresponding Position Weighted similarity）、AP（All Position similarity）、APW（All Position Weighted similarity）四种用户兴趣话题词相似度计算方法，通过用户和相似用户间关注、粉丝重合率验证相似用户挖掘效果，APW similarity的相似用户的关注/粉丝重合百分比为1.687%，优于提出的其他三种算法，分别提高了26.3%、2.8%、12.4%，并且比传统的文本相似度方法Jaccard相似度、编辑距离算法、余弦相似度分别提高了20.4%、21.2%、45.0%。因此APW方法可以更加有效地挖掘出用户的相似用户。     

Revisiting the Parallel Strategy for DOACROSS Loops

Journal of Computer Science and Technology - DOACROSS loops are significant parts in many important scientific and engineering applications, which are generally exploited pipeline/wave-front...