Biblio

Acoustic wave (AW) synthesis methodologies have become popular among AW filter designers because they provide a fast and precise seed to start with the design of AW devices. Nowadays, with the increasing complexity of carrier aggregation, there is a strong necessity to develop synthesis methods more focused on multiport filtering schemes. However, when dealing with multiport filtering functions, numerical accuracy plays an important role to succeed with the synthesis process since polynomial degrees are much higher as compared to the standalone filter case. In addition to polynomial degree, the number set of polynomial coefficients is also an important source of error during the extraction of the circuital elements of the filter. Nonetheless, in this paper is demonstrated that coupling matrix approaches are the best choice when the objective is to synthesize filtering functions with complex roots in their characteristic polynomials, which is the case of the channel polynomials of the multiport device.

The algorithm of causal anomaly detection in industrial control physics is proposed to determine the normal cloud line of industrial control system so as to accurately detect the anomaly. In this paper, The causal modeling algorithm combining Maximum Information Coefficient and Transfer Entropy was used to construct the causal network among nodes in the system. Then, the abnormal nodes and the propagation path of the anomaly are deduced from the structural changes of the causal network before and after the attack. Finally, an anomaly detection algorithm based on hybrid differential cumulative is used to identify the specific anomaly data in the anomaly node. The stability of causality mining algorithm and the validity of locating causality anomalies are verified by using the data of classical chemical process. Experimental results show that the anomaly detection algorithm is better than the comparison algorithm in accuracy, false negative rate and recall rate, and the anomaly location strategy makes the anomaly source traceable.

Recently a huge trend on the internet of things (IoT) and an exponential increase in automated tools are helping malware producers to target IoT devices. The traditional security solutions against malware are infeasible due to low computing power for large-scale data in IoT environment. The number of malware and their variants are increasing due to continuous malware attacks. Consequently, the performance improvement in malware analysis is critical requirement to stop rapid expansion of malicious attacks in IoT environment. To solve this problem, the paper proposed a novel framework for classifying malware in IoT environment. To achieve flne-grained malware classification in suggested framework, the malware image classification system (MICS) is designed for representing malware image globally and locally. MICS first converts the suspicious program into the gray-scale image and then captures hybrid local and global malware features to perform malware family classification. Preliminary experimental outcomes of MICS are quite promising with 97.4% classification accuracy on 9342 windows suspicious programs of 25 families. The experimental results indicate that proposed framework is quite capable to process large-scale IoT malware.

During impact analysis on object-oriented code, statically extracting dependencies is often complicated by subclassing, programming to interfaces, aliasing, and collections, among others. When a tool recommends a large number of types or does not rank its recommendations, it may lead developers to explore more irrelevant code. We propose to mine and rank dependencies based on a global, hierarchical points-to graph that is extracted using abstract interpretation. A previous whole-program static analysis interprets a program enriched with annotations that express hierarchy, and over-approximates all the objects that may be created at runtime and how they may communicate. In this paper, an analysis mines the hierarchy and the edges in the graph to extract and rank dependencies such as the most important classes related to a class, or the most important classes behind an interface. An evaluation using two case studies on two systems totaling 10,000 lines of code and five completed code modification tasks shows that following dependencies based on abstract interpretation achieves higher effectiveness compared to following dependencies extracted from the abstract syntax tree. As a result, developers explore less irrelevant code.

The lack of qualification of a common operating picture (COP) directly impacts the situational awareness of military Command and Control (C2). Since a commander is reliant on situational awareness information in order to make decisions regarding military operations, the COP needs to be trustworthy and provide accurate information for the commander to base decisions on the resultant information. If the COP's integrity is questioned, there is no definite way of defining its integrity. This paper looks into the integrity of the COP and how it can impact situational awareness. It discusses a potential solution to this problem on which future research can be based.