AbstractNumerous types of threats could become vulnerable to Wi-Fi networks. In terms of preventing and reducing their effect on the networks, it has become an imperative activity of any user to understand the threats. Even after thoroughly encrypting them, the route between the attacker’s device and the victim’s device may even be vulnerable to security attacks on Wi-Fi networks. It has also been noted that there are current shortcomings on Wi-Fi security protocols and hardware modules that are available in the market. Any device connected to the network could be a possible primary interface for attackers. Wi-Fi networks that are available in the transmission range are vulnerable to threats. For instance, if an Access Point (AP) has no encrypted traffic while it is attached to a Wi-Fi network, an intruder may run a background check to launch the attack. And then, attackers could launch more possible attacks in the targeted network, in which the Evil Twin attack have become the most prominent. This Evil Twin attack in a Wi-Fi network is a unique outbreak mostly used by attackers to make intrusion or to establish an infection where the users are exploited to connect with a victim’s network through a nearby access point. So, there are more chance to get user’s credentials by the perpetrators. An intruder wisely introduces a fake access point that is equivalent to something looks like an original access point near the network premises in this case. So, an attacker is capable of compromising the network when a user unconsciously enters by using this fake access point. Attackers could also intercept the traffic and even the login credentials used after breaching insecure networks. This could enable monitoring the users and perhaps even manipulating the behavior patterns of an authorized network user smoother for attackers. The key consideration of this research paper is the identification and avoidance of the Evil Twin attack over any Wi-Fi networks. It is named as DPETAs to address the strategies that intruders use to extract identities and what users need to do to keep them out of the networks.KeywordsManagement framesFake access points802.11 standardsWi-Fi attacksMan in the middle (MITM) attackEvil twin AP attacks 

DPETAs: Detection and Prevention of Evil Twin Attacks on Wi-Fi Networks

Evaluating the public data from person-to-person communication destinations through the social network could create invigorating outcomes and bits of knowledge on the general assessment of practically any product, administration, or conduct. One of the best and precise public notion pointers is through information mining from social networks, as numerous clients seem to state their viewpoints on the social networks. The innovation in the Internet technologies figured out how to expand action in contributing to a blog, labeling, posting, and online informal communication. Therefore, individuals are beginning to develop keen on mining these immense information assets to evaluate the viewpoints. The Social Network Analysis (SNA) is the way toward researching social designs using graph hypothesis and networks. It integrates an assortment of procedures for analyzing the design of informal organizations, in addition with the hypotheses that target describing the hidden elements and the patterns in this framework. It is an intrinsically integrative field, which initially emerged from the sectors of graph hypothesis, statistics, and sociopsychology. This chapter will cover the hypothesis of SNA, with a short prologue to graph hypothesis and data spread. Then discuss the role of Python in SNA, followed up by building and suggesting informal communities from genuine Pandas and text-based data sets. 

Overview of Social Network Analysis and Different Graph File Formats

Dementia is a neurological illness that causes diversion from a variety of important cognitive activities. Common examples include memory, reasoning, orientation, understanding, computation, verbal communication, and decision making. Alzheimer's disease (AD) is one of the most common dementias affecting the elderly. It was projected that more than 47 million people globally will be affected by dementia in 2015; these predictions were verified, and forecasts for 2050 are much more concerning, with 131 million people living with dementia. The basic objective of AI is to improve human decision-making and automate operations that are too time-consuming or resource-intensive for people to accomplish. AI can operate as a fast, accurate, and in the long run, cost-effective method to assist human experience and intuition through predictive analytics. AI is an effective technique for AD detection as these methods are employed as a computer-aided diagnosis (CAD) system in clinical practices and play a crucial role in identifying variations in the brain images to detect AD. 

Artificial Intelligence in the Detection of Alzheimer's Disease

Vehicles communicate with nearby vehicles to share high routing and traffic information in Vehicular Ad hoc Networks (VANETs) environment. Congestion and Delay in the transmission may occur due to the density of the nodes in the network. Traffic condition depends on the vehicles in Rural and Urban environment. Increase or Decrease in vehicle’s speed makes significant network changes when compared to the MANET environment. Road Side Terminals (RSTs) plays a major role in bridging the connection between the sender and the receiver nodes. The traditional AODV algorithm performs better when there are shortest path and link lifetime between the nodes in VANET. Giving 3 Level Preference to the nodes as High Preference (HP), Average Preference (AP) and Less Preference (LP) gives chances to nodes that have High Preference when compared to Less Preference. CPAODV model is proposed by implementing Classifying and giving preference to the RREQ to mitigate latency to the nodes. RST sends RREQ wisely based on the early model of Route Discovery stage itself. NS2 Simulator is used to analyze the strength of the proposed algorithm using QoS metrics like Throughput, Packet Delivery Ratio and End to End delay. This proposed CPAODV method performs better when compared to traditional AODV and CBAODV algorithm.

Mohammed Gouse Galety

Papers

CPAODV: Classifying and Assigning 3 Level Preference to the Nodes in VANET Using AODV Based CBAODV Algorithm