Network security analysis By Big Data

Many security problems on the Internet arise from botnets, which can be controlled by a competitor as a network of malware-infected devices .It is difficult to analyze the gigantic dataset with ordinary computers. To overcome this challenge, it was suggested to use a scalable threat detection framework. Networks are considered a target for intruders who would try to infiltrate them. Multi-pronged attacks may spread over network subnets To discover and predict such attacks, the authors of [8] proposed a system named Big-distributed Intrusion Detection System (B-did) that relies on two components ; HAMR and An analytics engine. The survey on wireless device fingerprinting methods in wireless networks was proposed. Device fingerprinting can be defined as the process of generating device-specific signatures by gathering device information.The device fingerprinting process is broken into three main steps. The authors reviewed the existing algorithms , their role of algorithm is limited to detect the presence and the likely culprit involved in the attack while failing to pinpoint the malicious devices in an exact manner. Studies have shown that researchers have worked more in the area of wireless networks and showed more interest Several applications are used for analyzing large network design data. Collecting of data network by optimal network design can lead to fast secure network

کلیدواژه ها:

Big Data ، Botnet ، Network Security ، Map Reduce ، Machine Learning ، B-did ، IDS