Background: Every organization generally uses a VPN service individually to bypass the filters that hide the actual communication. Such communication filtration is not allowed by the organizational monitoring network. But these institutes are not in a position to spend a considerable amount of funds on a secure sockets layer to monitor traffic flow over their computer networks. Objective: Our work suggests a simple technique to block or detect annoying VPN clients inside the network activities. This method does not require the network to decrypt or even decode any network communication. Methods: The proposed solution selects two machine learning techniques Feature Tree and K-means as classification techniques that work on time-related features. First, the DNS mapping with the ordinary characteristic of the transmission control protocol / Internet protocol computer the network stack is identified, and it is not to be considered as a regular traffic flow if the domain name information is not available. The process not only examines non-standard utilization of hypertext transfer protocol security but also conceals such communication from hypertext transfer protocol security dependent filters in the firewall to detect as an anomaly in large. Results: We define the traffic flow as normal traffic flow and VPN traffic flow. These two flows are characterized by taking two machine learning techniques, Feature Tree and K-means. We executed each experiment 4 times. As a result, eight types of regular traffics and eight types of VPN traffics were represented. Conclusion: Once the traffic flow is identified, it is classified and studied by machine learning techniques. Using time-related features, the traffic flow is defined as normal flow or VPN traffic flow.

Ordinary defence components like rule-based firewalls and mark based detection are not staying aware of the always expanding intricacy and frequency of cyber security dangers. The reason for this work is to explore the way that deep reinforcement learning (DRL), a subfield of artificial intelligence famous for its viability in handling testing decision-production situations, may be utilized to improve cyber security conventions. To mimic and balance threatening cyber-attacks, we present a system that utilizes deep reinforcement learning (DRL). We propose a specialist based model that can learn and adjust ceaselessly in powerful network security situations. In light of the present status of the network and the rewards it gets for its decisions, the specialist concludes what the best game-plans are. Specifically, we utilize the policy gradient (PG)- based double deep Q-network (DDQN) model and trial on three different datasets: NSL-KDD, CIC-IDS, and AWID. Our review demonstrates the way that DRL can really further develop the detection after-effects of cyber-attacks. Utilizing the policy gradient DDQN model on different datasets, we find prominent upgrades in cyber security conventions. Specific boundary modifications upgrade the viability of our philosophy much more, displaying empowering results on different datasets. This exploration features the potential of deep reinforcement learning (DRL) as a successful instrument in the field of cyber security. Our examination progresses detection techniques and gives a versatile arrangement that can be applied to an assortment of cyber security worries by giving areas of strength for a to demonstrating and relieving cyber dangers.

Agriculture is an important sector of Indian agronomy for human livelihood. All areas are affected by the effects of environmental toxic farms, which makes managing various difficult situations more challenging. Agriculture must adopt new technology in accordance with daily environmental changes if it is going to benefit from a crop from the perspectives of farmers and end users. Farmers will benefit from early detection of agricultural diseases rather than risking their lives in dangerous circumstances. Computer technology will be very helpful in maintaining sustainable and healthy crops for the objective of identifying crop diseases in addition to the farmer's close observation. Deep learning (DL) techniques are very influential among various computing technologies. In this work, we explore several current approaches to precision agriculture, such as artificial intelligence (AI), DL, and machine learning (ML). The findings of the study make clear modern methods, their drawbacks, and the knowledge lacking that needs to be addressed to explore precision agriculture fully.