IoT Based Intruder Prevention using Fogger

Anamoly detection in videos plays an important role in various real-life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Nowadays, there has been a rise in the amount of disruptive and offensive activities that have been happening. Due to this, security has been given principal significance. Public places like shopping centers, avenues, banks, etc. are increasingly being equipped with CCTVs to guarantee the security of individuals. Subsequently, this inconvenience is making a need to computerize this system with high accuracy. Since constant observation of these surveillance cameras by humans is a near-impossible task. It requires workforces and their constant attention to judge if the captured activities are anomalous or suspicious. Hence, this drawback is creating a need to automate this process with high accuracy. Moreover, there is a need to display which frame and which parts of the recording contain the uncommon activity which helps the quicker judgment of that unordinary action being unusual or suspicious. Therefore, to reduce the wastage of time and labour, we are utilizing deep learning algorithms for Automating Threat Recognition System. Its goal is to automatically identify signs of aggression and violence in real-time, which filters out irregularities from normal patterns. We intend to utilize different Deep Learning models (CNN and RNN) to identify and classify levels of high movement in the frame. From there, we can raise a detection alert for the situation of a threat, indicating the suspicious activities at an instance of time and spray the smoke spray

An Insight into Sybil Attacks – A Bibliometric Assessment

Sybil attack poses a significant security concern in both centralized and distributed network environments, wherein malicious adversary sabotage the network by impersonating itself as several nodes, called Sybil nodes. A Sybil attacker creates different identities for a single physical device to deceive other benign nodes, as well as uses these fake identities to hide from the detection process, thereby introducing a lack of accountability in the network. In this paper, we have thoroughly discussed the Sybil attack including its types, attack mechanisms, mitigation techniques that are in use today for the detection and prevention of such attacks. Subsequently, we have discussed the impact of the Sybil attack in various application domains and performed a bibliometric assessment in the top four scholarly databases. This will help the research community to quantitatively analyze the recent trends to determine the future research direction for the detection and prevention of such attacks

Support Vector Machine (SVM) Based Sybil Attack Detection in Vehicular Networks

International audienc

Support Vector Machine (SVM) Based Sybil Attack Detection in Vehicular Networks

International audienc