Phenomenology of SUSY with intermediate scale physics

The presence of fields at an intermediate scale between the Electroweak and the Grand Unification scale modifies the evolution of the gauge couplings and consequently the running of other parameters of the Minimal Supersymmetric Standard Model, such as gauginos and scalar masses. The net effect is a modification of the low energy spectrum which affects both the collider phenomenology and the dark matter relic density.Comment: 6 pages, 12 figures, contribution to Moriond EW 2012 proceeding

Low energy processes to distinguish among seesaw models

We consider the three basic seesaw scenarios (with fermionic singlets, scalar triplets or fermionic triplets) and discuss their phenomenology, aside from neutrino masses. We use the effective field theory approach and compare the dimension-six operators characteristic of these models. We discuss the possibility of having large dimension-six operators and small dimension-five (small neutrino masses) without any fine-tuning, if the lepton number is violated at a low energy scale. Finally, we discuss some peculiarities of the phenomenology of the fermionic triplet seesaw model.Comment: 3 pages, to appear in the proceedings of IFAE08, Bologna, Ital

Higgs-gauge unification without tadpoles

In orbifold gauge theories localized tadpoles can be radiatively generated at the fixed points where U(1) subgroups are conserved. If the Standard Model Higgs fields are identified with internal components of the bulk gauge fields (Higgs-gauge unification) in the presence of these tadpoles the Higgs mass becomes sensitive to the UV cutoff and electroweak symmetry breaking is spoiled. We find the general conditions, based on symmetry arguments, for the absence/presence of localized tadpoles in models with an arbitrary number of dimensions D. We show that in the class of orbifold compactifications based on T^{D-4}/Z_N (D even, N>2) tadpoles are always allowed, while on T^{D-4}/\mathbb Z_2 (arbitrary D) with fermions in arbitrary representations of the bulk gauge group tadpoles can only appear in D=6 dimensions. We explicitly check this with one- and two-loops calculationsComment: 19 pages, 3 figures, axodraw.sty. v2: version to appear in Nucl. Phys.

Tadpoles and Symmetries in Higgs-Gauge Unification Theories

In theories with extra dimensions the Standard Model Higgs fields can be identified with internal components of bulk gauge fields (Higgs-gauge unification). The bulk gauge symmetry protects the Higgs mass from quadratic divergences, but at the fixed points localized tadpoles can be radiatively generated if U(1) subgroups are conserved, making the Higgs mass UV sensitive. We show that a global symmetry, remnant of the internal rotation group after orbifold projection, can prevent the generation of such tadpoles. In particular we consider the classes of orbifold compactifications T^d/Z_N (d even, N>2) and T^d/Z_2 (arbitrary d) and show that in the first case tadpoles are always allowed, while in the second they can appear only for d=2 (six dimensions).Comment: 10 pages, based on talks given by M.Q. at String Phenomenology 2004, University of Michigan, Ann Arbor, August 1-6, 2004 and 10th International Symposium on Particles, Strings and Cosmology (PASCOS'04 and Nath Fest), Northeastern University, Boston, August 16-22, 200

On Security and Sparsity of Linear Classifiers for Adversarial Settings

Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to adversarial attacks, including the deliberate manipulation of data at test time to evade detection. In this work, we focus on the vulnerability of linear classifiers to evasion attacks. This can be considered a relevant problem, as linear classifiers have been increasingly used in embedded systems and mobile devices for their low processing time and memory requirements. We exploit recent findings in robust optimization to investigate the link between regularization and security of linear classifiers, depending on the type of attack. We also analyze the relationship between the sparsity of feature weights, which is desirable for reducing processing cost, and the security of linear classifiers. We further propose a novel octagonal regularizer that allows us to achieve a proper trade-off between them. Finally, we empirically show how this regularizer can improve classifier security and sparsity in real-world application examples including spam and malware detection

Unitarity of the Leptonic Mixing Matrix

We determine the elements of the leptonic mixing matrix, without assuming unitarity, combining data from neutrino oscillation experiments and weak decays. To that end, we first develop a formalism for studying neutrino oscillations in vacuum and matter when the leptonic mixing matrix is not unitary. To be conservative, only three light neutrino species are considered, whose propagation is generically affected by non-unitary effects. Precision improvements within future facilities are discussed as well.Comment: Standard Model radiative corrections to the invisible Z width included. Some numerical results modified at the percent level. Updated with latest bounds on the rare tau decay. Physical conculsions unchange

General bounds on non-standard neutrino interactions

We derive model-independent bounds on production and detection non-standard neutrino interactions (NSI). We find that the constraints for NSI parameters are around O(10^{-2}) to O(10^{-1}). Furthermore, we review and update the constraints on matter NSI. We conclude that the bounds on production and detection NSI are generally one order of magnitude stronger than their matter counterparts.Comment: 18 pages, revtex4, 1 axodraw figure. Minor changes, matches published versio

Neutrino masses from higher than d=5 effective operators

We discuss the generation of small neutrino masses from effective operators higher than dimension five, which open new possibilities for low scale see-saw mechanisms. In order to forbid the radiative generation of neutrino mass by lower dimensional operators, extra fields are required, which are charged under a new symmetry. We discuss this mechanism in the framework of a two Higgs doublet model. We demonstrate that the tree level generation of neutrino mass from higher dimensional operators often leads to inverse see-saw scenarios in which small lepton number violating terms are naturally suppressed by the new physics scale. Furthermore, we systematically discuss tree level generalizations of the standard see-saw scenarios from higher dimensional operators. Finally, we point out that higher dimensional operators can also be generated at the loop level. In this case, we obtain the TeV scale as new physics scale even with order one couplings.Comment: 22 pages, 3 figures, 2 tables. Some references adde

Security Evaluation of Support Vector Machines in Adversarial Environments

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion), or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector Machine Applications

Is Feature Selection Secure against Training Data Poisoning?

Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation of data-driven technologies. Feature selection has been widely used in machine learning for security applications to improve generalization and computational efficiency, although it is not clear whether its use may be beneficial or even counterproductive when training data are poisoned by intelligent attackers. In this work, we shed light on this issue by providing a framework to investigate the robustness of popular feature selection methods, including LASSO, ridge regression and the elastic net. Our results on malware detection show that feature selection methods can be significantly compromised under attack (we can reduce LASSO to almost random choices of feature sets by careful insertion of less than 5% poisoned training samples), highlighting the need for specific countermeasures