13,034 research outputs found
Statistical analysis driven optimized deep learning system for intrusion detection
Attackers have developed ever more sophisticated and intelligent ways to hack
information and communication technology systems. The extent of damage an
individual hacker can carry out upon infiltrating a system is well understood.
A potentially catastrophic scenario can be envisaged where a nation-state
intercepting encrypted financial data gets hacked. Thus, intelligent
cybersecurity systems have become inevitably important for improved protection
against malicious threats. However, as malware attacks continue to dramatically
increase in volume and complexity, it has become ever more challenging for
traditional analytic tools to detect and mitigate threat. Furthermore, a huge
amount of data produced by large networks has made the recognition task even
more complicated and challenging. In this work, we propose an innovative
statistical analysis driven optimized deep learning system for intrusion
detection. The proposed intrusion detection system (IDS) extracts optimized and
more correlated features using big data visualization and statistical analysis
methods (human-in-the-loop), followed by a deep autoencoder for potential
threat detection. Specifically, a pre-processing module eliminates the outliers
and converts categorical variables into one-hot-encoded vectors. The feature
extraction module discard features with null values and selects the most
significant features as input to the deep autoencoder model (trained in a
greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for
Cybersecurity is used as a benchmark to evaluate the feasibility and
effectiveness of the proposed architecture. Simulation results demonstrate the
potential of our proposed system and its outperformance as compared to existing
state-of-the-art methods and recently published novel approaches. Ongoing work
includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired
Cognitive Systems (BICS 2018
Progress towards quantum simulating the classical O(2) model
We connect explicitly the classical model in 1+1 dimensions, a model
sharing important features with lattice gauge theory, to physical models
potentially implementable on optical lattices and evolving at physical time.
Using the tensor renormalization group formulation, we take the time continuum
limit and check that finite dimensional projections used in recent proposals
for quantum simulators provide controllable approximations of the original
model. We propose two-species Bose-Hubbard models corresponding to these finite
dimensional projections at strong coupling and discuss their possible
implementations on optical lattices using a Rb and K Bose-Bose
mixture.Comment: 7 pages, 6 figures, uses revtex, new material and one author added,
as to appear in Phys. Rev.
A Simple Linear Ranking Algorithm Using Query Dependent Intercept Variables
The LETOR website contains three information retrieval datasets used as a
benchmark for testing machine learning ideas for ranking. Algorithms
participating in the challenge are required to assign score values to search
results for a collection of queries, and are measured using standard IR ranking
measures (NDCG, precision, MAP) that depend only the relative score-induced
order of the results. Similarly to many of the ideas proposed in the
participating algorithms, we train a linear classifier. In contrast with other
participating algorithms, we define an additional free variable (intercept, or
benchmark) for each query. This allows expressing the fact that results for
different queries are incomparable for the purpose of determining relevance.
The cost of this idea is the addition of relatively few nuisance parameters.
Our approach is simple, and we used a standard logistic regression library to
test it. The results beat the reported participating algorithms. Hence, it
seems promising to combine our approach with other more complex ideas.Comment: 5 page
Two semi-Lagrangian fast methods for Hamilton-Jacobi-Bellman equations
In this paper we apply the Fast Iterative Method (FIM) for solving general
Hamilton-Jacobi-Bellman (HJB) equations and we compare the results with an
accelerated version of the Fast Sweeping Method (FSM). We find that FIM can be
indeed used to solve HJB equations with no relevant modifications with respect
to the original algorithm proposed for the eikonal equation, and that it
overcomes FSM in many cases. Observing the evolution of the active list of
nodes for FIM, we recover another numerical validation of the arguments
recently discussed in [Cacace et al., SISC 36 (2014), A570-A587] about the
impossibility of creating local single-pass methods for HJB equations
Betel quid chewing as a risk factor for hepatocellular carcinoma: a case-control study
The role of betel quid chewing in the aetiology of hepatocellular carcinoma (HCC) was evaluated in a case–control study including 263 pairs of age- and sex-matched HCC patients and healthy controls. Serum hepatitis B surface antigen (HBsAg), and antibodies to hepatitis C virus (anti-HCV) were determined, and standardized personal interview conducted using a structured questionnaire. Multivariate analysis indicated that betel quid chewing (odds ratio (OR), 3.49; 95% confidence interval (CI), 1.74–6.96), HBsAg (OR, 16.69; 95% CI, 9.92–28.07), anti-HCV (OR, 38.57; 95% CI, 18.15–81.96), and educational duration of less than 10 years (OR, 1.71; 95% CI, 1.05–2.78) are independent risk factors of HCC. In addition, there was an additive interaction between betel quid chewing and chronic infection with either hepatitis B virus (synergy index, 5.37) or hepatitis C virus (synergy index, 1.66). Moreover, risk on HCC increased as duration of betel quid chewing increased, or amount of betel quid consumed (each P for trend < 0.0001). © 2001 Cancer Research Campaign http://www.bjcancer.co
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