2D2N: A Dynamic Degenerative Neural Network for Classification of Images of Live Network Data

© 2019 IEEE. The detection of new, novel attacks on organizational networks is a problem of ever-increasing relevance in today's society. Research in the area is focused on the detection of 'Zero-Day' and 'Black Swan' events through the use of machine learning technologies. Where previous technologies needed a known example of malicious behavior to detect a similar event, recent advances in anomaly detection on network activity has shown promise of detecting novel attacks. In a real word environment however, novel behavior occurs relatively frequently as users utilize new software applications and new standards in networking. Changes such as these, while of notable importance to network security technicians, may not present themselves as an imminent threat to a network. This paper proposes a novel method for the detection and classification of changes in networking behavior. Through the use of a Dynamic Degenerative Neural Network (2D2N), changes in recognizable user activity are dynamically classified and stored for future reference. Through the use of a time-based entropy function, infrequent activity can be analyzed and given precedence over frequent activity. This aids in the classification of abnormal activity for fast, efficient assessment by the relevant persons in an organization. The proposed method enables the detection, classification and scoring of any and all user activity on a network. Evaluation of the proposed method is based upon live data gathered from a large, multinational organization

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Multi-resource predictive workload consolidation approach in virtualized environments

The revolution of virtualization technologies and Cloud computing solutions has emphasized the need for energy-efficient and Service level agreement (SLA)-aware resource management techniques in cloud data centers. Workload consolidation in Infrastructure-as-a-Service (IaaS) providers allows for efficient utilization of hardware resources and reduced energy consumption by consolidating workloads onto fewer physical servers. To ensure successful workload consolidation, it is crucial for IaaS providers to carefully estimate the host state and identify overloaded and underloaded hosts, thereby avoiding overly aggressive consolidation. Existing proposals determine the host state depending on its current resource utilization or a single anticipated resource utilization value, and often consider only a single resource type of the host, such as CPU. These limitations may lead to unreliable host state estimations, resulting in excessive and needless service migrations between physical machines (PMs). This, in turn, can lead to extra delays in service execution, degraded performance, increased power consumption, and SLA violations. To address these challenges, we propose a workload consolidation approach that leverages a multi-resource and multi-step resource utilization prediction model. Based on this model, our overload and underload decision-making algorithms consider the forecasted future trend (sequence of future value) of each host resource's utilization, including CPU, memory, and bandwidth. Through extensive experimentations conducted with two real-world datasets, we demonstrate that our approach can significantly reduce power consumption, SLA violation rate, and the number of migrations compared to existing benchmarks

New Chaotic Permutation Methods for Image Encryption

International audienceSince two decades, and in order to reach higher performance, more and more studies look to combine the conventional encryption methods and the complex behaviour of chaotic signals. To quantify the expected improvement induced by such a mix, this paper aims to compare the performance of two well known chaotic maps, namely, Logistic and piecewise linear chaotic map with their performance when they are perturbed by a new technique. These four chaotic maps are then used to control three bit permutation methods: Grp, Cross and Socek, known to have good inherent cryptographic properties. When applied to images, the common measures like NPCR, UACI, intra and inter-components correlation coefficients, histogram and distribution of two adjacent pixels lead to two main results. First, Socek permutation method remains better than Grp and Cross whatever the used chaotic map. Second, the proposed chaotic permutation methods controlled by the perturbed maps present higher performance and is more secure and suitable for chaotic image encryption scheme

Algorithmes de chiffrement / déchiffrement basé chaos pour la sécurité des images transmises

La mondialisation des échanges (Internet, messagerie électronique, commerce électronique, ), grâce à l émergence des nouvelles technologies de l information et de la communication, pose le problème de la sécurité de l information transmise à travers les canaux publics non sécurisés. L utilisation du chaos dans des crypto-systèmes, apporte de l amélioration (temps de chiffrement, sécurité) par rapport aux méthodes standards de la cryptographie (DES, IDEA, AES), ceci grâce aux caractéristiques des signaux chaotiques tels que: bonnes propriétés cryptographiques, reproductibilité à l identique (de terministes), et l hyper sensibilité à la clé secrète. Dans cette thèse, une nouvelle structure d algorithmes CBCSTI de chiffrement/déchiffrement basés chaos, supportant quatre versions différentes selon l association des méthodes de permutation adoptées au niveau des bits et au niveau des pixels est réalisée. Chaque version comporte quatre modes opératoires: OFB, CBC, CFB, CTR et la structure est conforme au standard de NIST vis-à-vis de la propagation des erreurs de transmission. La carte chaotique PWLCM utilisée inclut une technique de perturbation permettant d améliorer les dynamiques chaotiques et ainsi d augmenter et de contrôler la longueur des séquences chaotiques générées. L analyse de la sécurité, utilisant les tests standards les plus significatifs: sensibilité (à la clé secrète en émission et en réception, et à l image claire), tests statistiques (histogramme, densité de probabilité, corrélation des pixels adjacents) sur l image claire et l image chiffrée, série des tests statistiques de NIST sur des images chiffrées montre l intérêt de la structure proposée.Recently, a large amount of work using digital chaotic systems to construct cryptosystems has been studied and has attracted more and more attention in the last years. Because of the chaotic signal characteristics, such as: good cryptographic properties, reproducibility with identical (deterministic), and the hyper sensitivity to secret key, the chaos based cryptosystems are more rapid and secure then the standard encryption algorithms (DES, IDEA, AES). In this thesis, a novel chaos-based cryptosystem for secure transmitted images (CBCSTI) is proposed. This cryptosystem contains four different versions according to adopted association of bits and pixels permutation methods. Each version comprises four cryptographic modes: OFB, CBC, CFB, CTR, each of them is in conformity with the standard of NIST with respect to the error propagation. A perturbed PWLCM chaotic map was used to control the different operations (substitutions and permutations) in the algorithm. The proposed disturbance technique allow us to improve the dynamic chaotic and thus to increase and control the length of the generated chaotic sequences. The security analysis, key sensitivity at the emission and the reception, the statistical tests applied on the original images and the ciphered ones like: histogram, probability density, correlation of adjacent pixels and NIST tests on the encrypted images prove the efficacy of the proposed cryptosystem.NANTES-BU Sciences (441092104) / SudocNANTES-BU Technologie (441092105) / SudocSudocFranceF

Vehicular cloud networks: Architecture, applications and security issues

Vehicular Ad Hoc Networks (VANET) are the largest real life application of ad-hoc networks where nodes are represented via fast moving vehicles. This paper introduces the future emerging technology, i.e., Vehicular Cloud Networking (VCN) where vehicles and adjacent infrastructure merge with traditional internet clouds to offer different applications ranging from low sized applications to very complex applications. VCN is composed of three types of clouds: Vehicular cloud, Infrastructure cloud and traditional Back-End (IT) cloud. We introduced these clouds via a three tier architecture along with their operations and characteristics. We have proposed use cases of each cloud tier that explain how it is practically created and utilised while taking the vehicular mobility in consideration. Moreover, it is critical to ensure security, privacy and trust of VCN network and its assets. Therefore, to describe the security of VCN, we have provided an in-depth analysis of different threats related to each tier of VCN. The threats related to vehicular cloud and infrastructure cloud are categorized according to their assets, i.e., vehicles, adjacent infrastructure, wireless communication, vehicular messages, and vehicular cloud threats. Similarly, the Back-End cloud threats are categorized into data and network threats. The possible implications of these threats and their effects on various components of VCN are also explained in detail

Chaos-based cryptosystem for secure transmitted images

International audienc