Machine Learning Threatens 5G Security

Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of mobile networks. However, ML will also open the network to several serious cybersecurity vulnerabilities. Most of the learning in ML happens through data gathered from the environment. Un-scrutinized data will have serious consequences on machines absorbing the data to produce actionable intelligence for the network. Scrutinizing the data, on the other hand, opens privacy challenges. Unfortunately, most of the ML systems are borrowed from other disciplines that provide excellent results in small closed environments. The resulting deployment of such ML systems in 5G can inadvertently open the network to serious security challenges such as unfair use of resources, denial of service, as well as leakage of private and confidential information. Therefore, in this article we dig into the weaknesses of the most prominent ML systems that are currently vigorously researched for deployment in 5G. We further classify and survey solutions for avoiding such pitfalls of ML in 5G systems

Face Recognition Using Morphological Analysis of Images

Face recognition from still and motion image has been an active and emerging research area in the field of image processing, pattern recognition and so on in the recent years . The challenges associated with discriminant face recognition can be attributed to the following factors such as pose, facial expression, occlusion, image orientation, image condition, presence or absence of structural component and many more. In this paper, we have tried to emphasize on the morphological analysis of images based on the behavior of the intensity value. Firstly images with various situations of a person are selected as training images. Based on the min, max and average characteristics of images, the training model has been built. Morphological analysis like binary image processing, erosion and dilation play the important role to identify the facial portion of an image from the whole one. Finally face recognition has been made for input images based on their intensity value measurement. The training images collected from various database such as YALE, ORL, and UMIST and others. The algorithm performed well and showed 80 percent accuracy on face predictio

A good fort has a gap

You are not (and can’t be) absolutely certain that the principal defences (of a system or a structure) you have set up are impenetrable. In other words, you cannot out rule the possibility of a successful break-in, since the attacker might be able to circumvent or confuse the provided access control points by some clever means. This might be due to the breadth and complexity of the system or structure, or to some other inherent weakness.

A good fort has a gap

You are not (and can’t be) absolutely certain that the principal defences (of a system or a structure) you have set up are impenetrable. In other words, you cannot out rule the possibility of a successful break-in, since the attacker might be able to circumvent or confuse the provided access control points by some clever means. This might be due to the breadth and complexity of the system or structure, or to some other inherent weakness.