Network intrusion and fault detection: a statistical anomaly approach

With the advent and explosive growth of the global Internet and electronic commerce environments, adaptive/automatic network/service intrusion and anomaly detection in wide area data networks and e-commerce infrastructures is fast gaining critical research and practical importance. We present and demonstrate the use of a general-purpose hierarchical multitier multiwindow statistical anomaly detection technology and system that operates automatically, adaptively, and proactively, and can be applied to various networking technologies, including both wired and wireless ad hoc networks. Our method uses statistical models and multivariate classifiers to detect anomalous network conditions. Some numerical results are also presented that demonstrate that our proposed methodology can reliably detect attacks with traffic anomaly intensity as low as 3-5 percent of the typical background traffic intensity, thus promising to generate an effective early warning.     

SiGe HMODFET "KAIST" micropower model and amplifier realization

The recently published small-signal KAIST model is used successfully to fit the measured RF characteristics of a novel SiGe n-HMODFET device operating at micropower levels and extracted small-signal model parameters for this device under micropower operation are presented here for the first time. This model is then used to predict the performance of a simple micropower amplifier (sub 300-/spl mu/W total power consumption), realized in SiGe technology, and a comparison of modeled versus measured data is included.     

11B NMR Study of Pure and Lightly Carbon-Doped MgB2 Superconductors

We report a¹¹B NMR line shape and spin-lattice relaxation rate (1/(T ₁ T)) study of pure and lightly carbon-doped MgB_2−x C _x forx=0, 0.02, and 0.04, in the vortex state and in magnetic field of 23.5 kOe. We show that while pure MgB₂ exhibits the magnetic field distribution from superposition of the normal and the Abrikosov state, slight replacement of boron with carbon unveils the magnetic field distribution of the pure Abrikosov state. This indicates a considerable increase ofH _c ^2/c with carbon doping with respect to pure MgB₂. The spin-lattice relaxation rate 1/(T ₁ T) demonstrates clearly the presence of a coherence peak right belowT _c in pure MgB₂, followed by a typical BCS decrease on cooling. However, at temperatures lower than ≈10 K strong deviation from the BCS behavior is observed, probably from residual contribution of the vortex dynamics. In the carbon-doped systems both the coherence peak and the BCS temperature dependence of 1/(T ₁ T) weaken, an effect attributed to the gradual shrinking of the σ hole cylinders of the Fermi surface with electron doping.     

11B NMR Study of Pure and Lightly Carbon-Doped MgB2 Superconductors

We report a ¹¹B NMR line shape and spin-lattice relaxation rate (1/(T₁T))(1/(T_1T)) study of pure and lightly carbon-doped MgB_2-xC_x_{2-x}{\rm C}_x for x = 0,0.02x = 0,0.02, and 0.04, in the vortex state and in magnetic field of 23.5 kOe. We show that while pure MgB₂_2 exhibits the magnetic field distribution from superposition of the normal and the Abrikosov state, slight replacement of boron with carbon unveils the magnetic field distribution of the pure Abrikosov state. This indicates a considerable increase of H^c_c2H^c_{c2} with carbon doping with respect to pure MgB₂_2. The spin-lattice relaxation rate 1/(T₁T)1/(T_1T) demonstrates clearly the presence of a coherence peak right below T_cT_{\rm c} in pure MgB₂_2, followed by a typical BCS decrease on cooling. However, at temperatures lower than ?\approx 10 K strong deviation from the BCS behavior is observed, probably from residual contribution of the vortex dynamics. In the carbon-doped systems both the coherence peak and the BCS temperature dependence of 1/(T₁T)1/(T_1T) weaken, an effect attributed to the gradual shrinking of the σ hole cylinders of the Fermi surface with electron doping.     

Adaptive Anomaly Detection in Transaction-Oriented Networks

Adaptive algorithms for real-time and proactive detection of network/service anomalies, i.e., soft performance degradations, in transaction-oriented wide area networks (WANs) have been developed. These algorithms (i) adaptively sample and aggregate raw transaction records to compute service-class based traffic intensities, in which potential network anomalies are highlighted; (ii) construct dynamic and service-class based performance thresholds for detecting network and service anomalies; and (iii) perform service-class based and real-time network anomaly detection. These anomaly detection algorithms are implemented as a real-time software system called TRISTAN ( ansaction n antaneous nomaly otification), which is deployed in the AT&T Transaction Access Services (TAS) network. The TAS network is a commercially important, high volume (millions of transactions per day), multiple service classes (tens), hybrid telecom and data WAN that services transaction traffic such as credit card transactions in the US and neighboring countries. TRISTAN is demonstrated to be capable of automatically and adaptively detecting network/service anomalies and correctly identifying the corresponding "guilty" service classes in TAS. TRISTAN can detect network/service faults that elude detection by the traditional alarm-based network monitoring systems.     

Universality in ant behaviour

Prediction for social systems is a major challenge. Universality at the social level has inspired a unified theory for urban living but individual variation makes predicting relationships within societies difficult. Here, we show that in ant societies individual average speed is higher when event duration is longer. Expressed as a single scaling function, this relationship is universal because for any event duration an ant, on average, moves at the corresponding average speed except for a short acceleration and deceleration at the beginning and end. This establishes cause and effect within a social system and may inform engineering and control of artificial ones.     

Diagnosing Anomalies and Identifying Faulty Nodes in Sensor Networks

In this paper, an anomaly detection approach that fuses data gathered from different nodes in a distributed sensor network is proposed and evaluated. The emphasis of this work is placed on the data integrity and accuracy problem caused by compromised or malfunctioning nodes. The proposed approach utilizes and applies Principal Component Analysis simultaneously on multiple metrics received from various sensors. One of the key features of the proposed approach is that it provides an integrated methodology of taking into consideration and combining effectively correlated sensor data, in a distributed fashion, in order to reveal anomalies that span through a number of neighboring sensors. Furthermore, it allows the integration of results from neighboring network areas to detect correlated anomalies/attacks that involve multiple groups of nodes. The efficiency and effectiveness of the proposed approach is demonstrated for a real use case that utilizes meteorological data collected from a distributed set of sensor nodes     

Evaluation of ageing in asphalt cores using low-field nuclear magnetic resonance

This paper introduces an innovative methodology for estimating the ageing of asphalt concrete cores without extracting the binder. Asphalt concrete samples at different ageing stages (unaged, 3-month and 6-month aged) and with different percent air voids (4%, 7% and 10%) were analysed with low-field nuclear magnetic resonance (NMR). The transverse relaxation time T₂ and relative hydrogen index (RHI) obtained from NMR measurements were related to the viscosity of the asphalt binder. The samples were analysed during cooling from 70°C to room temperature, showing increase in viscosity with decreasing temperature. There was a clear trend indicating higher viscosities in samples that were aged for a longer period and samples with higher percent air voids. The RHI and T₂ values obtained from low-field NMR measurements and the viscosity data calculated from measurements using a dynamic shear rheometer were correlated to develop a model that relates viscosity with RHI.     

Immunotherapy as a Therapeutic Strategy for Gastrointestinal Cancer—Current Treatment Options and Future Perspectives

Gastrointestinal (GI) cancer constitutes a highly lethal entity among malignancies in the last decades and is still a major challenge for cancer therapeutic options. Despite the current combinational treatment strategies, including chemotherapy, surgery, radiotherapy, and targeted therapies, the survival rates remain notably low for patients with advanced disease. A better knowledge of the molecular mechanisms that influence tumor progression and the development of optimal therapeutic strategies for GI malignancies are urgently needed. Currently, the development and the assessment of the efficacy of immunotherapeutic agents in GI cancer are in the spotlight of several clinical trials. Thus, several new modalities and combinational treatments with other anti-neoplastic agents have been identified and evaluated for their efficiency in cancer management, including immune checkpoint inhibitors, adoptive cell transfer, chimeric antigen receptor (CAR)-T cell therapy, cancer vaccines, and/or combinations thereof. Understanding the interrelation among the tumor microenvironment, cancer progression, and immune resistance is pivotal for the optimal therapeutic management of all gastrointestinal solid tumors. This review will shed light on the recent advances and future directions of immunotherapy for malignant tumors of the GI system.     

Game theoretic distributed uplink power control for CDMA networks with real-time services

In this paper, the issue of efficient power allocation in the uplink of CDMA wireless networks supporting real-time services with various QoS constraints, is addressed. Within the proposed framework, utility functions are adopted to reflect a user’s degree of satisfaction with respect to its actual throughput requirements satisfaction and respective power consumption. The corresponding problem is formulated as a non-cooperative game where users aim selfishly at maximizing their utility-based performance under the imposed limitations. The existence and uniqueness of a Nash equilibrium point of the proposed Uplink Power Control (UPC) game is proven, at which all users have attained a targeted SINR threshold value or transmit with their maximum power, leading essentially to an SINR-balanced system. Moreover, a distributed iterative algorithm for reaching UPC game’s equilibrium is provided. The properties of equilibrium in a pure optimization theoretical framework are studied, and the tradeoffs between users’ overall throughput performance and real-time services’ QoS requirements satisfaction, in channel aware resource allocation processes, are revealed and quantified. Through modeling and simulation the efficacy of the introduced framework and proposed UPC algorithm are demonstrated and evaluated.