Radio Frequency Identification System and IoT based Tech Genix Lock

In this paper a Door lock system using RFID and IOT Technologies. Using smart phone with Blynk App the Admin or owner can remotely monitor, control (ON & OFF the access) and can get Blynk notifications from the door lock when someone try to access it or if any suspicious activity happens (someone try to break the it). The registered User will receive Gmail’s when he tries to unlock the door. Other features are the admin or owner can remotely unlock and lock the door from any place, and he can disable the switch which is used for getting out from the room, for blocking the unknown persons who went with theft RFID card, it is done by seeing the notifications send to the user when door is unlocked with his card through Admin. For authorized person the lock will be opened and closed automatically after a predetermined delay. If it is an unauthorized person tries it alerts the admin and neighbors through a Blynk notification and buzzer sound. Thus, once implemented, the system will prove An Effective security with minimum cost and increase in comfort for system users and also more efficient compared to existing cost ineffective systems.

SUSPICIOUS ACTIVITY DETECTION USING CCTV SURVEILLANCE VIDEO

Security has recently been given the highest priority with the rise in the number of antisocial activations taking place. To continuously track individuals and their interactions, CCTVs have been built in several ways. Every person is recorded on an image on average 30 times a day in a developed world with a community of 1.6 billion. The resolution of 710*570 captured at knitting will approximate 20 GB per day. Constant monitoring of human data makes it hard to judge whether the incident is an irregular one, and it is an almost uphill struggle when a population and its full support are needed. In this paper, we make a system for the detection of suspicious activity using CCTV surveillance video. There seems to be a need to demonstrate in which frame the behavior is located as well as which section of it allows the faster judgment of the suspicious activity is unusual. This is done by converting the video into frames and analyzing the persons and their activates from the processed frames. We have accepted wide support from Machine learning and Deep Learning Algorithms to make it possible. To automate that process, first, we need to build a training model using a large number of images (all possible images which describe features of suspicious activities) and a “Convolution Neural Network‟ using the Tensor Flow Python module. We can then upload any video into the application, and it will extract frames from the uploaded video and then that frame will be applied on a training model to predict its class such as suspicious or normal.

Network Attack Classification in IoT Using Support Vector Machines

Machine learning (ML) techniques learn a system by observing it. Events and occurrences in the network define what is expected of the network’s operation. It is for this reason that ML techniques are used in the computer network security field to detect unauthorized intervention. In the event of suspicious activity, the result of the ML analysis deviates from the definition of expected normal network activity and the suspicious activity becomes apparent. Support vector machines (SVM) are ML techniques that have been used to profile normal network activity and classify it as normal or abnormal. They are trained to configure an optimal hyperplane that classifies unknown input vectors’ values based on their positioning on the plane. We propose to use SVM models to detect malicious behavior within low-power, low-rate and short range networks, such as those used in the Internet of Things (IoT). We evaluated two SVM approaches, the C-SVM and the OC-SVM, where the former requires two classes of vector values (one for the normal and one for the abnormal activity) and the latter observes only normal behavior activity. Both approaches were used as part of an intrusion detection system (IDS) that monitors and detects abnormal activity within the smart node device. Actual network traffic with specific network-layer attacks implemented by us was used to create and evaluate the SVM detection models. It is shown that the C-SVM achieves up to 100% classification accuracy when evaluated with unknown data taken from the same network topology it was trained with and 81% accuracy when operating in an unknown topology. The OC-SVM that is created using benign activity achieves at most 58% accuracy.

Unusual Crowd Activity Detection Using Open CV and Motion Influence Map

Suspicious behavior is dangerous in public areas that may cause heavy causalities. There are various systems developed on the basis of video frame acquisition where motion or pedestrian detection occur but those systems are not intelligent enough to identify the unusual activities even at real time. It is required to recognized scamper situation at real time from video surveillance for quick and immediate management before any casualties. Proposed system focuses on recognizing suspicious activities and target to achieve a technique which is able to detect suspicious activity automatically using computer vision. Here system uses OpenCV library for classifying different kind of actions at real time.

Finanšu līdzekļu arests tiesību uz īpašumu aizstāvības kontekstā

The aim of this work is to analyse the problematic aspects of seizure of criminal assets. The paper examines 30 decisions of investigating judges to seize property and higher court decisions, as well as claims and other decisions related to seizure. The article mainly analyses the application of legal norms and general legal principles, as well as the inconsistency of certain legal norms with the constitution (Satversme). The most significant problems are the following: (1) the court does not assess the evidence on the merits; (2) there is no right to request the revocation of the asset seizure in court; (3) the time limits for asset seizure are disproportionately long; (4) the presumption of criminal origin is not restricted in practice to criminals or related parties; (5) suspicious activity is the only evidence of criminal origin. The problems manifest themselves as a violation of fundamental rights, including the right to a fair trial, equality before the law and the right to property.

Detecting Criminal Activities of Surveillance Videos using Deep Learning

Videos generated by surveillance cameras inside the ATM were very long. In case, any robbery had taken place inside the ATM; it became time consuming to watch the entire long video. Hence, there was a need to process these surveillance videos by extracting the priority frames from it in which suspicious activities like robbery, murder, kidnap, etc. had taken place. The objective of this paper was to propose algorithm that would generate a detect the suspicious frames from that long surveillance video for the authorities which would consists of priority information. In this paper a novel approach dealing with Convolutional Neural Networks using Deep Learning was used to sample the priority information from the surveillance videos. The priority information was the suspicious activities like robbery, murder, etc. which take place inside the ATM. The results of the CNN model effectively were able to extract suspicious activity frames from a long video and thus extract suspicious frames and create a video from it.