Publications are essential for scientific communication. Access to publications is provided by conventional libraries, digital libraries operated by learned societies or commercial publishers, and a huge number of web sites maintained by the scientists themselves or their institutions. Comprehensive meta-indices for this increasing number of information sources are missing for most areas of science. The DBLP Computer Science Bibliography of the University of Trier has grown from a very specialized small collection of bibliographic information to a major part of the infrastructure used by thousands of computer scientists. This short paper first reports the history of DBLP and sketches the very simple software behind the service. The most time-consuming task for the maintainers of DBLP may be viewed as a special instance of the authority control problem; how to normalize different spellings of person names. The third section of the paper discusses some details of this problem which might be an interesting research issue for the information retrieval community.

The DBLP Computer Science bibliography: Evolution, research issues, perspectives

Survey of machine learning techniques for malware analysis

The rise of machine learning for detection and classification of malware: Research developments, trends and challenges

Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows. The proposed methodologies are not adequate while evolutionary and complex nature of malware is changing quickly and therefore turn out to be harder to recognize. This paper presents a systematic and detailed survey of the malware detection mechanisms using data mining techniques. In addition, it classifies the malware detection approaches in two main categories including signature-based methods and behavior-based detection. The main contributions of this paper are: (1) providing a summary of the current challenges related to the malware detection approaches in data mining, (2) presenting a systematic and categorized overview of the current approaches to machine learning mechanisms, (3) exploring the structure of the significant methods in the malware detection approach and (4) discussing the important factors of classification malware approaches in the data mining. The detection approaches have been compared with each other according to their importance factors. The advantages and disadvantages of them were discussed in terms of data mining models, their evaluation method and their proficiency. This survey helps researchers to have a general comprehension of the malware detection field and for specialists to do consequent examinations.

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A state-of-the-art survey of malware detection approaches using data mining techniques

https://cyberleninka.org/article/n/693122.pdf

Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications

In this paper, a downlink macro-diversity precoding-aided spatial modulation (MD-PSM) scheme is proposed, in which two base stations (BSs) communicate simultaneously with a single mobile station (MS). As such, the proposed scheme achieved twice the spectral efficiency of the conventional PSM scheme. To render the demodulation possible, the two signal constellation sets used at the two BSs should be disjoint. Also, since the two BSs use the same spatial dimension, i.e., indices of receive antennas, the Minkowski sum of the two constellation sets should include unrepeated symbols. This is achieved through rotating the constellation set used by the second BS, where the error rate is also minimized. After obtaining the optimal rotation angles for several scenarios, a reduced complexity maximum-likelihood receiver is introduced. For an equal number of transmit and receive antennas of 4 and at a target bit error rate (BER) of 10−4, the simulation results show that the proposed MD-PSM scheme outperforms the conventional PSM by about 17.3 dB and 12.4 dB while achieving the same and double the spectral efficiency, respectively. Also, due to the distributed nature of MD-PSM, it is shown that the diversity order of the novel MD-PSM scheme is twice that of the conventional PSM.

Downlink macro-diversity precoding-aided spatial modulation

In spatial modulation (SM), a single signal symbol is transmitted from a given physical antenna, where both the signal symbol and the antenna index carry information. SM with multiple active antennas (MA-SM) transmits several signal symbols from a combination of antennas at each channel use, thereby increasing the spectral efficiency. MA-SM is proposed in combination with a new 3-dimensional constellation, where signal symbols transmitted from a given antenna combination are rotated before transmission. In this paper, we derived an upper-bound on the error probability of the MA-SM as a function of the rotation angles. The search for the optimal rotation angles is modeled as a multi-objective optimization problem. We concluded based on both analytical and simulation results that the 3-dimensional constellation with the optimal angles achieved negligible improvement. Therefore, we do not recommend using the 3-dimensional constellation with the MA-SM system.

On the Performance of the Multiple Active Antenna Spatial Modulation with 3-Dimensional Constellation

Interference alignment (IA) is a technique used to reduce the dimension of the interference, where consequently the multiplexing rate is increased In the 2-user X channel, combining IA with space-time block codes increases the diversity gain These gains are achieved with the cost of leaked information at unintended receivers, where this leaked information can be used to decode other receiver’s signals In this paper, we consider each of the two two-antenna receivers as an eavesdropper with 1 or 2 additional eavesdropping antennas As such, we suggest receiver structures to answer the question: “Is the leaked information sufficient to properly decode the unintended signals?” besides quantifying the leaked information in terms of secrecy sum rates (SSR) Interestingly, we show that the SSR is negative, indicating that the quality of the eavesdropped signals is superior to that of the intended signals To assure confidentiality, we propose an interleaved multiple rotation-based transformation scheme that neutralizes any a priori knowledge about the structure of the eavesdropped information and rotates the transmitted symbols using orthogonal matrices, preserving both the power and the distance between symbols

/pdf/aligned-space-time-block-codes-for-the-2-user-x-channel-with-1koqphz50a.pdf

Aligned Space Time Block Codes for the 2-User X Channel with Secrecy Constraints

Using runtime execution artifacts to identify malware and its associated family is an established technique in the security domain. Many papers in the literature rely on explicit features derived from network, file system, or registry interaction. While effective, the use of these fine-granularity data points makes these techniques computationally expensive. Moreover, the signatures and heuristics are often circumvented by subsequent malware authors. In this work, we propose Chatter, a system that is concerned only with the order in which high-level system events take place. Individual events are mapped onto an alphabet and execution traces are captured via terse concatenations of those letters. Then, leveraging an analyst labeled corpus of malware, n-gram document classification techniques are applied to produce a classifier predicting malware family. This paper describes that technique and its proof-of-concept evaluation. In its prototype form, only network events are considered and eleven malware families are used. We show the technique achieves 83%-94% accuracy in isolation and makes non-trivial performance improvements when integrated with a baseline classifier of combined order features to reach an accuracy of up to 98.8%.

/pdf/network-based-analysis-and-classification-of-malware-using-45ayaz00fq.pdf

Network-based Analysis and Classification of Malware using Behavioral Artifacts Ordering.

In this paper, we propose two schemes to reduce the complexity of fixed-complexity sphere decoder (FSD) algorithm in the ordering and tree-search stages, respectively, while achieving quasi-ML performance. In the ordering stage, we propose a QR-decomposition-based FSD signal ordering based on the zero-forcing criterion (FSD-ZF-SQRD) that requires only a few number of additional complex flops compared to the unsorted QRD. Also, the proposed ordering algorithm is extended using the minimum mean square error (MMSE) criterion to achieve better performance. In the tree-search stage, we introduce a threshold-based complexity reduction approach for the FSD depending on the reliability of the signal with the largest noise amplification. Numerical results show that in 8 ? 8 MIMO system, the proposed FSD-ZF-SQRD and FSD-MMSE-SQRD only require 19.5% and 26.3% of the computational efforts required by Hassibi’s scheme, respectively. Moreover, a third threshold vector is outlined which can be used for high order modulation schemes. In 4 ? 4 MIMO system using 16-QAM and 64-QAM, simulation results show that when the proposed threshold-based approach is employed, FSD requires only 62.86% and 53.67% of its full complexity, respectively.

Manar Mohaisen

Papers

Downlink macro-diversity precoding-aided spatial modulation

On the Performance of the Multiple Active Antenna Spatial Modulation with 3-Dimensional Constellation

Aligned Space Time Block Codes for the 2-User X Channel with Secrecy Constraints

Network-based Analysis and Classification of Malware using Behavioral Artifacts Ordering.

Efficiency Improvement of the Fixed-complexity Sphere Decoder