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Multiple attacks have shown that in-vehicle networks have vulnerabilities which can be exploited. Securing the Controller Area Network (CAN) for modern vehicles has become a necessary task for car manufacturers. Some attacks inject potentially large amount of fake messages into the CAN network; however, such attacks are relatively easy to detect. In more sophisticated attacks, the original messages are modified, making the detection a more complex problem. In this paper, we present a novel machine learning based intrusion detection method for CAN networks. We focus on detecting message modification attacks, which do not change the timing patterns of communications. Our proposed temporal convolutional network-based solution can learn the normal behavior of CAN signals and differentiate them from malicious ones. The method is evaluated on multiple CAN-bus message IDs from two public datasets including different types of attacks. Performance results show that our lightweight approach compares favorably to the state-of-the-art unsupervised learning approach, achieving similar or better accuracy for a wide range of scenarios with a significantly lower false positive rate.

In this paper, we introduce a novel approach to optimizing the control of systems that can be modeled as Markov decision processes (MDPs) with a threshold-based optimal policy. Our method is based on a specific type of genetic program known as symbolic regression (SR). We present how the performance of this program can be greatly improved by taking into account the corresponding MDP framework in which we apply it. The proposed method has two main advantages: (1) it results in near-optimal decision policies, and (2) in contrast to other algorithms, it generates closed-form approximations. Obtaining an explicit expression for the decision policy gives the opportunity to conduct sensitivity analysis, and allows instant calculation of a new threshold function for any change in the parameters. We emphasize that the introduced technique is highly general and applicable to MDPs that have a threshold-based policy. Extensive experimentation demonstrates the usefulness of the method.

Various types of systems across a broad range of disciplines contain tandem queues with nested sessions. Strong dependence between the servers has proved to make such networks complicated and difficult to study. Exact analysis is in most of the cases intractable. Moreover, even when performance metrics such as the saturation throughput and the utilization rates of the servers are known, determining the limiting factor of such a network can be far from trivial. In our work, we present a simple, tractable and nevertheless relatively accurate method for approximating the above mentioned performance measurements for any server in a given network. In addition, we propose an extension to the intuitive “slowest server rule” for identification of the bottleneck, and show through extensive numerical experiments that this method works very well.

In service function chaining, data flows from a particular application or user travel along a pre-defined sequence of network functions. Appropriate service function chaining resource allocation is required to comply with the service level required by the application. In this paper, we introduce a dynamic priority assignment for flows that compete for service using a particular network function in a chain. Using the recent results of the performance metrics of transient birth-death processes, we analyse this priority assignment and develop an optimal strategy for selecting a (cheap) low-or (expensive) high-priority service, given the flow's service level agreement requirements. A decision table can, thus, be created to facilitate the fast, online priority scheduling of newly arriving flows requesting service. © 2019 IEEE.

The advent of renewable energy sources has huge implications for the design and control of power grids. On the engineering side, reliability is currently ensured by strict con- straints on current, voltage and temperature. However, with growing supply-side uncertainty induced by renewables, these will need to be replaced by probabilistic guarantees, allowing constraints on a given line to be violated with a low probability, e.g., several minutes per year. In the present note we illustrate, using large deviations techniques, how replacing (probabilistic) current constraints by temperature constraints can lead to capacity gains in power grids.

Various canonical forms of general resource allocation problemsarise naturally across a broad spectrum of computer systems andcommunication networks. As the complexities of these systemsand networks continue to grow, together with ubiquitous advancesin technology, new approaches and methods are required to effec-tively and efficiently solve these problems. Such environments of-ten consist of different types of resources that are allocated in com-bination to serve demand whose behavior over time is characterizedby different types of uncertainty and variability. Each type of re-source has a different reward and cost structure that ranges from thebest of a set of primary resource allocation options, having the high-est reward, highest cost and highest net-benefit, to a secondary re-source allocation option, having the lowest reward, lowest cost andlowest net-benefit. Each type of resource also has different struc-tures for the flexibility and cost of making changes to the allocationcapacity. The resource management optimization problem we con-sider consists of adaptively determining the primary and secondaryresource allocation capacities that serve the uncertain demand andthat maximize the expected net-benefit over a time horizon of in-terest based on the foregoing reward, cost and flexibility structuralproperties of the different types of resources.The general class of resource allocation problems studied in thispaper arises in a wide variety of application domains such as cloudcomputing and data center environments, computer and communi-cation networks, and energy-aware and smart power grid environ-ments, among many others. Across these and many other domain-specific resource allocation problems, there is a common need forthe dynamic adjustment of allocations among multiple types of re-sources, each with different structural properties, to satisfy time-varying and uncertain demand. Taking a financial mathematicsapproach that hedges against future risks associated with resourceallocation decisions and uncertain demand, we consider the under-lying fundamental stochastic optimal control problem where thedynamic control policy that allocates primary resource capacitiesto serve uncertain demand is a variational stochastic process withconditions on its derivative, which in turn determines the secondaryresource allocation capacity. The objective is to maximize the ex-pected discounted net-benefit over time based on the structural prop

Osteopontin (OPN) is a glycophosphoprotein with multiple intracellular and extracellular functions. In vitro, OPN enhances migration of mouse neutrophils and macrophages. In cancer, extracellular OPN facilitates migration of cancer cells via its RGD sequence. The present study was designed to investigate whether osteopontin is responsible for neutrophil and macrophage infiltration in human cancer and in particular in glioblastoma. We found that in vitro mouse neutrophil migration was RGD-dependent. In silico, we found that the OPN gene was one of the 5% most highly expressed genes in 20 out of 35 cancer microarray data sets in comparison with normal tissue in at least 30% of cancer patients. In some types of cancer, such as ovarian cancer, lung cancer and melanoma, the OPN gene was one of those with the highest expression levels in at least 90% of cancer patients. In glioblastoma, the most invasive type of brain tumours/glioma, but not in lower grades of glioma it was one of the 5% highest expressed genes in 90% of patients. In situ, we found increased protein levels of OPN in human glioblastoma versus normal human brain confirming in silico results. OPN protein expression was co-localized with neutrophils and macrophages. In conclusion, OPN in tumours not only induces migration of cancer cells but also of leucocytes.

The concurrent use of networks provides a powerful means to boost performance in areas covered by multiple networks where only limited bandwidth is available. However, despite its enormous potential for performance improvement only little is known about how to effectively exploit the potential for performance improvement in practical deployments. This raises the need for traffic-splitting-and-reassembly algorithms that are effective, yet simple and easy-to-deploy. Motivated by this, we first propose a simple analytic flow-level model, called the Concurrent Access Network (CAN) model, that optimally splits traffic in the idealized situation where there is full state information at infinitely fine-grained time granularity, leading to zero synchronization delay during the reassembly phase. Next, we present a new splitting algorithm for TCP-based networks that uses a simple score function to make on-the-fly decisions on the routing of individual TCP segments, based on the measured per-connection RTT, transmission-buffer content and throughput. Then, we use the CAN-model as a benchmark to evaluate the effectiveness and practical usefulness of the score-function based algorithm on real TCP networks in a test-lab environment. Extensive lab experimentation demonstrates that this score-function based splitting of TCP traffic is extremely efficient, leads to close-to-optimal response-time performance and is easily deployable.

In this paper we investigate sequential decision mechanisms for composite web services. After executing each sub-service within a sequential workflow, decisions are made whether to terminate or continue the execution of the workflow. These decisions are based on observed response times, expected rewards, and typical Service Level Agreement parameters such as costs, penalties, and agreed response–time objectives. We propose a model for the sequential decision–making process within which we explore a couple of decision algorithms. We benchmarked these algorithms against the profit made when executing the workflow without decision–making. We show that algorithm based on backward recursion principle of dynamic programming is optimal with respect to profit. Next, we analyse the structure of erroneous decisions for both algorithms and show that significant profit gains can be obtained by sequential decision making.