Runtime verification is critical for detecting whether programmable networks behave as intended during operation. However, many existing runtime verification mechanisms instantiate executable verification logic around requirements specified before deployment, making it difficult to change checks at runtime. This paper presents Chameleon, a runtime-pluggable verification mechanism for programmable networks. Chameleon separates a stable verification substrate from concrete verification requirements: the verification substrate is embedded into the data plane before deployment, while requirements are represented as runtime-manageable VERIFY objects and translated into P4Runtime table entries, allowing the operator to add, modify, or delete supported verification types without recompiling the data-plane program. Experimental results show that Chameleon can augment P4 programs with small one-time preprocessing and compilation overheads, and supports millisecond-level runtime configuration operations for verification requirements.

As an important part of intrusion detection, feature selection plays a significant role in improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient swarm intelligence algorithm with excellent performance in data mining. To solve the problem of low efficiency and high false positive rate in intrusion detection caused by increasing high-dimensional data, an improved krill swarm algorithm based on linear nearest neighbor lasso step (LNNLS-KH) is proposed for feature selection of network intrusion detection. The number of selected features and classification accuracy are introduced into fitness evaluation function of LNNLS-KH algorithm, and the physical diffusion motion of the krill individuals is transformed by a nonlinear method. Meanwhile, the linear nearest neighbor lasso step optimization is performed on the updated krill herd position in order to derive the global optimal solution. Experiments show that the LNNLS-KH algorithm retains 7 features in NSL-KDD dataset and 10.2 features in CICIDS2017 dataset on average, which effectively eliminates redundant features while ensuring high detection accuracy. Compared with the CMPSO, ACO, KH, and IKH algorithms, it reduces features by 44%, 42.86%, 34.88%, and 24.32% in NSL-KDD dataset, and 57.85%, 52.34%, 27.14%, and 25% in CICIDS2017 dataset, respectively. The classification accuracy increased by 10.03% and 5.39%, and the detection rate increased by 8.63% and 5.45%. Time of intrusion detection decreased by 12.41% and 4.03% on average. Furthermore, LNNLS-KH algorithm quickly jumps out of the local optimal solution and shows good performance in the optimal fitness iteration curve, convergence speed, and false positive rate of detection.

The new medium access method of IEEE 802.11ah, called restricted access window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this article, we present a surrogate model that can accurately predict RAW performance under a given RAW configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 10(17) possible data points. This is too big to achieve feasible training convergence. In this article, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 10(5) data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks.

The Restricted Access Window (RAW) mechanism proposed by IEEE 802.11ah promises to address one of the major problems of the Internet of Things (IoT): high channel contention in large-scale densely deployed sensor networks. The RAW feature allows the Access Point (AP) to divide stations into different groups, with only the stations in the same group being allowed to access the channel simultaneously. Existing station grouping strategies only support homogeneous scenarios, where all sensor stations have the same fixed data transmission interval, modulation and coding scheme (MCS) and packet size. In this paper, we present two contributions to address this issue. First, a surrogate model that predicts RAW performance given specific network conditions and RAW configuration parameters. It is fast to train and can be solved in real-time. Second, the Model-Based RAW Optimization Algorithm (MoROA), which uses the surrogate model to determine the optimal RAW configuration in real-time, for heterogeneous stations and dynamic traffic. We compare the accuracy of our surrogate model to simulation results. Performance of MoROA is compared to existing RAW optimization algorithms and traditional 802.11 channel access methods. The results shows that the trained surrogate model can accurately predict RAW performance with a relative error less than 7% and 10% for 95% and 98% of the RAW configurations respectively. MoROA achieves a throughput up to twice as high as traditional 802.11 channel access functions in dense heterogeneous networks.

IEEE 802.11ah is the new sub-1 GHz Wi-Fi standard, targeting large-scale and dense deployments of low-power stations. One of its major improvements compared to previous 802.11 standards, is its ability to scale to thousands of stations per access point. Cost-effective evaluation at such a scale is only possible using simulation, which requires realistic path loss models and hardware parameters. In this paper, we evaluate seven path loss models, based on a large scale sub-urban measurement campaign, including macro line-of- sight (LoS), pico LoS, and pico non-LoS with different as well as equal antenna height deployments. For each of the four resulting scenarios, the most accurate model is determined and used in combination with radio transceiver parameters obtained from actual 802.11ah station hardware to determine MAC-layer throughput and packet loss as a function of distance. The standard promises a range of up to 1 km at 150 kbps. Our results paint a less optimistic picture. When using realistic hardware parameters ranges up to 450 and 130 m can be achieved for a near LoS macro and pico deployment scenario respectively. For the non-LoS pico scenario ranges of 80 and 150 m can be achieved for transmitter at height 12 m and transmitter at heights 1.5 m respectively. With an ideal hardware configuration that operates at the maximum allowed transmission power, this could ideally be increased to 1700, 490, 300 and 550 m respectively.

IEEE 802.11ah is a new Wi-Fi standard operating on unlicensed sub-GHz frequencies. It aims to provide long-range connectivity to Internet of Things (IoT) devices. The IEEE 802.11ah restricted access window (RAW) mechanism promises to increase throughput and energy efficiency in dense deployments by dividing stations into different RAW groups and allowing only one group to access the channel at a time. In this demo, we demonstrate the ability of the RAW mechanism to support a large number of densely deployed IoT stations with heterogeneous traffic requirements. Differentiated Quality of Service (QoS) is offered to a small set of high-throughput wireless cameras that coexist with thousands of best-effort sensor monitoring stations. The results are visualized in near real-time using our own developed IEEE 802.11ah visualizer running on top of the ns-3 event-based network simulator.

IEEE 802.11ah is a new Wi-Fi draft for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things (IoT): connectivity among a large number of power-constrained stations deployed over a wide area. The new Restricted Access Window (RAW) mechanism promises to increase throughput and energy efficiency by dividing stations into different RAW groups. Only the stations in the same group can access the channel simultaneously, which reduces collision probability in dense scenarios. However, the draft does not specify any RAW grouping algorithms, while the grouping strategy is expected to severely impact RAW performance. To study the impact of parameters such as traffic load, number of stations and RAW group duration on optimal number of RAW groups, we implemented a sub-1Ghz PHY model and the 802.11ah MAC protocol in ns-3 to evaluate its transmission range, throughput, latency and energy efficiency in dense IoT network scenarios. The simulation shows that, with appropriate grouping, the RAW mechanism substantially improves throughput, latency and energy efficiency. Furthermore, the results suggest that the optimal grouping strategy depends on many parameters, and intelligent RAW group adaptation is necessary to maximize performance under dynamic conditions. This paper provides a major leap towards such a strategy.

Despite the critical role that middleboxes play in introducing new network functionality, management and innovation of them are still severe challenges for network operators, since traditional middleboxes based on hardware lack service flexibility and scalability. Recently, though new networking technologies, such as network function virtualization (NFV) and software-defined networking (SDN), are considered as very promising drivers to design cost-efficient middlebox service architectures, how to guarantee transmission efficiency has drawn little attention under the condition of adding virtual service process for traffic. Therefore, we focus on the service deployment problem to reduce the transport delay in the network with a combination of NFV and SDN. First, a framework is designed for service placement decision, and an integer linear programming model is proposed to resolve the service placement and minimize the network transport delay. Then a heuristic solution is designed based on the improved quantum genetic algorithm. Experimental results show that our proposed method can calculate automatically the optimal placement schemes. Our scheme can achieve lower overall transport delay for a network compared with other schemes and reduce 30% of the average traffic transport delay compared with the random placement scheme.