DExIE - An IoT-Class Hardware Monitor for Real-Time Fine-Grained Control-Flow Integrity

The Dynamic Execution Integrity Engine (DExIE) is a lightweight hardware monitor that can be flexibly attached to many IoT-class processor pipelines. It is guaranteed to catch both inter- and intra-function illegal control flows in time to prevent any illegal instructions from touching memory. The performance impact of attaching DExIE to a core depends on the concrete pipeline structure. In some especially suitable cases, extending a processor with DExIE will have no performance penalty. DExIE is real-time capable, as it causes no or only up to 10.4 % additional and then predictable pipeline stalls. Depending on the monitored processor’s size and structure, DExIE is faster than software-based monitoring and often smaller than a separate guard processor. We present not just the hardware architecture, but also the automated programming flow, and discuss compact adaptable storage formats to hold fine-grained control flow information.

Design and Realisation of Scalable Business Process Management Systems for Deployment in the Cloud

Business Process Management Systems ( BPMSs ) provide automated support for the execution of business processes in modern organisations. With the emergence of cloud computing, BPMS deployment considerations are shifting from traditional on-premise models to the Software-as-a-Service ( SaaS ) paradigm, aiming at delivering Business Process Automation as a Service. However, scaling up a traditional BPMS to cope with simultaneous demand from multiple organisations in the cloud is challenging, since its underlying system architecture has been designed to serve a single organisation with a single process engine. Moreover, the complexity in addressing both the dynamic execution environment and the elasticity requirements of users impose further challenges to deploying a traditional BPMS in the cloud. A typical SaaS often deploys multiple instances of its core applications and distributes workload to these application instances via load balancing. But, for stateful and often long-running process instances, standard stateless load balancing strategies are inadequate. In this article, we propose a conceptual design of BPMS capable of addressing dynamically varying demands of end users in the cloud, and present a prototypical implementation using an open source traditional BPMS platform. Both the design and system realisation offer focused strategies on achieving scalability and demonstrates the system capabilities for supporting both upscaling, to address large volumes of user demand or workload, and downscaling, to release underutilised computing resources, in a cloud environment.

Structure for Synonymous Flow Label (SFL)

Because of the very high loss rate in general activity, enhancements in connection and transmission innovations have made it more difficult to evaluate packet loss utilizing dynamic execution estimation techniques with plotted traffic. That, along with seriously requesting administration level necessities, implies that network administrators currently should have the option to quantify the loss of the actual user data traffic utilizing inactive execution estimation strategies. Multiprotocol Label Switching (MPLS) strategy portrays the prerequisite for presenting stream characters inside the MPLS architecture. This paper depicts a strategy for achieving this by utilizing a method called Synonymous Flow Label (SFL) in which names that imitate the conduct of different labels give the recognizable proof assistance. These identifiers can be utilized to trigger per stream procedure on the packet at the receiving label switching router.

Autonomic Management Framework for Cloud-Native Applications

In order to meet the rapidly changing requirements of the Cloud-native dynamic execution environment, without human support and without the need to continually improve one’s skills, autonomic features need to be added. Embracing automation at every layer of performance management enables us to reduce costs while improving outcomes. The main contribution of this paper is the definition of autonomic management requirements of Cloud-native applications. We propose that the automation is achieved via high-level policies. In turn autonomy features are accomplished via the rule engine support. First, the paper presents the engineering perspective of building a framework for Autonomic Management of Cloud-Native Applications, namely AMoCNA, in accordance with Model Driven Architecture (MDA) concepts. AMoCNA has many desirable features whose main goal is to reduce the complexity of managing Cloud-native applications. The presented models are, in fact, meta-models, being technology agnostic. Secondly, the paper demonstrates one possibility of implementing the aforementioned design procedures. The presented AMoCNA implementation is also evaluated to identify the potential overhead introduced by the framework.

Software Crucial Functions Ranking and Detection in Dynamic Execution Sequence Patterns

Because of the sequence and number of calls of functions, software network cannot reflect the real execution of software. Thus, to detect crucial functions (DCF) based on software network is controversial. To address this issue, from the viewpoint of software dynamic execution, a novel approach to DCF is proposed in this paper. It firstly models, the dynamic execution process as an execution sequence by taking functions as nodes and tracing the stack changes occurring. Second, an algorithm for deleting repetitive patterns is designed to simplify execution sequence and construct software sequence pattern sets. Third, the crucial function detection algorithm is presented to identify the distribution law of the numbers of patterns at different levels and rank those functions so as to generate a decision-function-ranking-list (DFRL) by occurrence times. Finally, top-k discriminative functions in DFRL are chosen as crucial functions, and similarity the index of decision function sets is set up. Comparing with the results from Degree Centrality Ranking and Betweenness Centrality Ranking approaches, our approach can increase the node coverage to 80%, which is proven to be an effective and accurate one by combining advantages of the two classic algorithms in the experiments of different test cases on four open source software. The monitoring and protection on crucial functions can help increase the efficiency of software testing, strength software reliability and reduce software costs.

A Novel Malware Classification Method Based on Crucial Behavior

Recently, some graph-based methods have been proposed for malware detection. However, current malware is generally characterized by sophisticated behaviors, which makes graph-based malware detection extremely challenging. To address this issue, we propose a graph repartition algorithm by transforming API call graphs into fragment behaviors based on programs’ dynamic execution traces. The proposed algorithm relies on the N-order subgraph (NSG) for constructing the appropriate fragment behavior. Moreover, we improve the term frequency-inverse document frequency- (TF-IDF-) like measure and information gain (IG) to extract the crucial N-order subgraph (CNSG). This novel behavioral representation and improved extraction method can accurately represent crucial behaviors of malware. Experiments on 4,400 samples demonstrate that the proposed method achieves a high accuracy of 99.75% in malware detection and promising performance of 95.27% in malware classification.

A study on Influence of Strategic Human Resource Management on Performance of Organizations

The Execution of association is getting progressively basic, especially in a market with increasingly noticeable contention and dynamic. Execution of association is estimated through different pointers. It guarantees the movement of the relationship to be engaged in an overall business focus. Customarily, the execution of Performance markers achieved through HR. HR is the key for keeping the relationship in the market so engaged. These HR ought to be directed effectively to achieve the necessary execution of the affiliation. It is imperative to manage intentionally the HR and to modify at its strategy with legitimate framework. The purpose of this audit is fixated on the impact of the indispensable organization of human resource in achieving progressive execution. This survey was coordinated in perspective on basic and discretionary sources. What measure of affiliations appears to be engaged in the market through achieving the execution pointers? How basic is the organization of HR in achieving legitimate execution? Along these lines, through the aptitudes, practices and mindsets would be depended upon by HR to achieve the necessary execution in the affiliation.

Execution Plan Control in Dynamic Coalition of Robots with Smart Contracts and Blockchain

The paper presents an approach of the blockchain and smart contracts utilization for dynamic robot coalition creation. The coalition is forming for solving complex tasks in industry applications that requires sequential united actions from the several robots. The main idea is that the process is split into two stages: scheduling and dynamic execution. On the scheduling stage, the coalition is defined based on the correlation of existing tasks and robot equipment, and the execution plan is formed and stored in smart contracts. The second stage is the plan execution. During this stage, smart contract controls how each robot solves its sub-task and whether it solves the sub-task due to the planned moment of time. In case of any deviation from the plan, smart contacts will provide a solution for returning to the plan or for changing the coalition composition with new robots and an execution plan. The prototype for execution control system has been developed based on the Hyperledger Fabric platform.