Formal-Guided Fuzz Testing: Targeting Security Assurance from Specification to Implementation for 5G and Beyond

Softwarization and virtualization in 5G and beyond necessitate thorough testing to ensure the security of critical infrastructure and networks, requiring the identification of vulnerabilities and unintended emergent behaviors from protocol designs to their software stack implementation. To provide an efficient and comprehensive solution, we propose a novel and first-of-its-kind approach that connects the strengths and coverage of formal and fuzzing methods to efficiently detect vulnerabilities across protocol logic and implementation stacks in a hierarchical manner. We design and implement formal verification to detect attack traces in critical protocols, which are used to guide subsequent fuzz testing and incorporate feedback from fuzz testing to broaden the scope of formal verification. This innovative approach significantly improves efficiency and enables the auto-discovery of vulnerabilities and unintended emergent behaviors from the 3GPP protocols to software stacks. Following this approach, we discover one identifier leakage model, one DoS attack model, and two eavesdrop attack models due to the absence of rudimentary MITM protection within the protocol, despite the existence of a Transport Layer Security (TLS) solution to this issue for over a decade. More remarkably, guided by the identified formal analysis and attack models, we exploit 61 vulnerabilities using fuzz testing demonstrated on srsRAN platforms. These identified vulnerabilities contribute to fortifying protocol-level assumptions and refining the search space. Compared to state-of-the-art fuzz testing, our united formal and fuzzing methodology enables auto-assurance by systematically discovering vulnerabilities. It significantly reduces computational complexity, transforming the non-practical exponential growth in computational cost into linear growth

Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks

In this paper, we present a novel distributed UAVs beam reforming approach to dynamically form and reform a space-selective beam path in addressing the coexistence with satellite and terrestrial communications. Despite the unique advantage to support wider coverage in UAV-enabled cellular communications, the challenges reside in the array responses' sensitivity to random rotational motion and the hovering nature of the UAVs. A model-free reinforcement learning (RL) based unified UAV beam selection and tracking approach is presented to effectively realize the dynamic distributed and collaborative beamforming. The combined impact of the UAVs' hovering and rotational motions is considered while addressing the impairment due to the interference from the orbiting satellites and neighboring networks. The main objectives of this work are two-fold: first, to acquire the channel awareness to uncover its impairments; second, to overcome the beam distortion to meet the quality of service (QoS) requirements. To overcome the impact of the interference and to maximize the beamforming gain, we define and apply a new optimal UAV selection algorithm based on the brute force criteria. Results demonstrate that the detrimental effects of the channel fading and the interference from the orbiting satellites and neighboring networks can be overcome using the proposed approach. Subsequently, an RL algorithm based on Deep Q-Network (DQN) is developed for real-time beam tracking. By augmenting the system with the impairments due to hovering and rotational motion, we show that the proposed DQN algorithm can reform the beam in real-time with negligible error. It is demonstrated that the proposed DQN algorithm attains an exceptional performance improvement. We show that it requires a few iterations only for fine-tuning its parameters without observing any plateaus irrespective of the hovering tolerance

New Frontier in the Treatment of Diabetes

Diabetes mellitus is a group of metabolic diseases recognized by chronic hyperglycemia resulting from defects in secretion in insulin, insulin action or both. There are different types of diabetes like Type 1, type 2, gestational diabetes, secondary diabetes, wolfram syndrome and autoimmune polyglandular syndrome. Type 1 and type 2 diabetes are most common type of diabetes. Polydipsia, polyuria, polyphagia, weight loss slow wound healing, etc. are common symptoms of Diabetes. Diabetes can be genetic; autoimmune, medical related or even diet related. In this article causes and treatment of diabetes is discussed in detail. It includes glimpse of novel technologies like patches, pump and pens, etc. It also includes momentary of other treatment like oral and Injectable hypoglycemic drug and surgical treatments. A glance of latest innovation for measuring glucose level in body with help of sweat, breath and saliva are explained. Keywords: Diabetes; Type 2 Diabetes Mellitus (TY2DM), Polydipsia, polyuria, polyphagia, clicksoft microinjection, insulin pen, v-g

Optimum Cooperative Spectrum Sensing Technique for Multiuser Ultraviolet Wireless Communications

In this paper, we present a novel optimum cooperative spectrum sensing technique to mitigate multiuser interference for multiuser ultraviolet wireless communications over Málaga distributed turbulence channel. We consider the distributed decision fusion for the cooperative sensing. Based on the derived Málaga distribution, a mathematically-tractable expression for the average probability of detection is presented. An optimal voting rule is derived to minimize the average error rate. To verify this optimum voting rule, we use the energy detection technique. It is found that the formulated voting rule produces one optimal value only, which indeed confirms its optimum performance

Efficacy of a monovalent human-bovine (116E) rotavirus vaccine in Indian children in the second year of life

Rotavirus gastroenteritis is one of the leading causes of diarrhea in Indian children less than 2 years of age. The 116E rotavirus strain was developed as part of the Indo-US Vaccine Action Program and has undergone efficacy trials. This paper reports the efficacy and additional safety data in children up to 2 years of age. In a double-blind placebo controlled multicenter trial, 6799 infants aged 6-7 weeks were randomized to receive three doses of an oral human-bovine natural reassortant vaccine (116E) or placebo at ages 6, 10, and 14 weeks. The primary outcome was severe (≥11 on the Vesikari scale) rotavirus gastroenteritis. Efficacy outcomes and adverse events were ascertained through active surveillance. We randomly assigned 4532 and 2267 subjects to receive vaccine and placebo, respectively, with over 96% subjects receiving all three doses of the vaccine or placebo. The per protocol analyses included 4354 subjects in the vaccine and 2187 subjects in the placebo group. The overall incidence of severe RVGE per 100 person years was 1.3 in the vaccine group and 2.9 in the placebo recipients. Vaccine efficacy against severe rotavirus gastroenteritis in children up to 2 years of age was 55.1% (95% CI 39.9 to 66.4; p<0.0001); vaccine efficacy in the second year of life of 48.9% (95% CI 17.4 to 68.4; p=0.0056) was only marginally less than in the first year of life [56.3% (95% CI 36.7 to 69.9; p<0.0001)]. The number of infants needed to be immunized to prevent one episode of severe RVGE in the first 2 years of life was 40 (95% CI 28.0 to 63.0) and for RVGE of any severity, it was 21 (95% CI 16.0 to 32.0). Serious adverse events were observed at the same rates in the two groups. None of the eight intussusception events occurred within 30 days of a vaccine dose and all were reported only after the third dose. The sustained efficacy of the 116E in the second year of life is reassuring

An Efficient Likelihood Based Automatic Modulation Classification For SISO And MIMO Wireless Communication Systems

Automatic modulation classification (AMC) identifies the type of the modulation of the received signal so that it can be demodulated correctly. It is the intermediate step between the signal detection and its demodulation process. AMC is a branch of the non-cooperative wireless communication system, which brings together some features of the traditional cooperative communication theory, which includes channel estimation and tracking, and signal detection. AMC has wide applications domain in military and civilian scenarios. In the military application, modulation can be considered as an additional variant of encryption, inhibiting the receivers from extracting the information from the signal received without having knowledge of the modulation type. Other than this, automatic modulation classification is also pivotal in identifying the transmission unit for generation of corresponding jamming signals with matching modulation technique. In today’s modern civilian applications, AMC find its application in intelligent communication systems such as software defined radio and cognitive radio (CR). AMC is nowadays an essential part of the adaptive modulation communication system for improved spectrum efficiency, throughput, link reliability and lowering the bit error rate. In adaptive modulation, the transmitter dynamically varies the modulation parameters or the constellation size of the transmitted waveform according to the channel condition and system specifications. In this research, we improved the classification accuracy of the existing likelihood-based (LB) modulation classifier for SISO system. The classification process is assisted by the wireless sensor network (WSN) to enhance the classification accuracy. The time complexity of the proposed approach is also presented. The proposed method is shown to have high classification accuracy and improved robustness over the existing methods. But the cost paid for this is high computational complexity. In the subsequent chapter, we presented the LB modulation classification for MIMO communication systems. The impact of spatial correlation and antenna mutual coupling is also presented. The uniform linear array (ULA) consisting of the dipole antennas is considered for performance analysis

Microscopic optical fields in diamond and germanium: molecular-orbital approach

Using the bonding and antibonding orbitals for the valence and conduction bands in germanium and diamond, the nonresonant part of the microscopic dielectric matrix, the local dielectric function, and the microscopic fields induced by a transverse optical field are calculated self-consistently. It is found that the secondary fields are appreciable in both germanium and diamond

Microscopic optical fields and mixing coefficients of X-ray and optical frequencies in solids

Simple approximation schemes are developed to calculate induced optical fields and local field corrections to the linear optical dielectric function in metals like aluminium and in insulators like germanium. In these calculations, the unperturbed electronic states in Ge are described within the framework of the bonding orbital approach, whereas the nearly-free-electron approximation is used for Al. As expected, explicit numerical calculations show that the contribution to secondary longitudinal induced fields is more appreciable in Ge. The second order susceptibility describing the non-linear mixing of an optical frequency with an x-ray frequency, which depends upon the magnitude of the microscopic induced optical charge density, is also calculated for these solids. For most relevant wavevectors of secondary optical fields, it is found to be of the order of 10-12 esu in Ge and 10-14 esu in Al

Tight-binding calculations of microscopic screening of ion-ion interaction and phonon dispersion in germanium

A set of extreme tight-binding states for the valence and conduction bands in germanium, consistent with its observable electronic dielectric properties, is used to calculate the effective ion-ion interaction and phonon dispersion relations in the material. The agreement with experimental dispersion curves is comparable with other elaborate calculations