Obstacle Avoiding Wireless Surveillance Bot
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1 Volume 118 No , ISSN: (on-line version) url: Obstacle Avoiding Wireless Surveillance Bot ijpam.eu Aman Aryan, Aakanksha Mishra, Raashi Pradeep Shetty Department of Computer Science and Engineering SRM Institute of Science and Technology, Kattankulathur Tamil Nadu, Mrs. A. Meena Priyadharsini Assistant Professor & Faculty Advisor Department of Computer Science and Engineering SRM Institute of Science and Technology, Kattankulathur Abstract When it comes to the suite of wireless networks (Wi-Fi), there are several design flaws which makes several attacks on existing networks possible. Even in a basic setup, any domestic or corporate network consists of an Access Point (AP) and connected devices (Clients). Any activity which either prevents the normal operation (uninterrupted communication between the AP Client pair) or poses a security risk can be tagged as malicious and as a potential attack on the network. These involve, but are not limited to the cracking of the handshake to figure out the password needed to connect to the AP, de-authenticating legitimate users off the network, flooding with Beacon Packets to confuse the end user or possible cause a Denial of Service (DOS) and Evil Twin attack which aims on having an identical AP with the intention of having a user connect to the malicious AP which later can either escalate to a Man in The Middle (MiTM) attack or a Phishing attack. This project focuses on building an obstacle avoiding Autonomous Robot that demonstrates these attacks. II. PROPOSED MODEL In the proposed system, we build an Obstacle Avoiding Wireless Security Assessment and Threat Detection Autonomous Bot that uses a Raspberry Pi Camera Module and an Ultrasonic Sensor module to avoid collision with obstacles. Fig. 1: Block Diagram of Object Detection and Avoidance I. INTRODUCTION The objective of this project is to detect any active attacks on the network and notify the network administrator in case any attacks are detected with the help of an Auto Guided Vehicle, the base of our model, to provide mobility to the model. The model is an Arduino based autonomous robot. Arduino Mega board is used for this purpose. It is a microcontroller that acts as a brain to the bot. Differential steering is used to control the Bot, i.e., it uses DC motors to provide movement to the robot individually to avoid sliding and to provide stability. The Robot uses A Raspberry Pi Camera module and an Ultrasound Sensor module for autonomous navigation. A Camera module is used to implement Object Detection algorithm on a Raspberry Pi and an Ultrasonic Sensor module together with the camera provides autonomous navigation to the bot. The Object Detection algorithm runs on a Raspberry Pi which takes continuous video frames using a Raspberry Pi Camera Module, and detects the largest object in the frame based on its colour. It gives the extreme points (leftmost and rightmost) along the object s contour and gives the relative position of the object in the frame represented by a boolean type array. The video frame can be considered to be divided into 11 equal-sized segments, and the array shows the position of the object by assigning the value 1 wherever in the frame the object lies. Thus, the bot is steered in the appropriate direction accordingly. 4309
2 Ultrasonic Sensors are used to detect objects and avoid collision by determining the distance of the obstacle from the sensor. The Sensor used in the project is HC- SR04. Obstacles are avoided on the basis of signals from the ultrasonic sensors. When the Robot is powered on and starts running, Arduino measures the distances of objects, in front of it, by using Ultrasonic Sensor Module and stores in a variable. Then Arduino compares this value with predefined values and take decisions accordingly to move the Robot Left, Right, Forward, or backward. Fig. 2: The Leftmost point of the detected object labelled as Red and Rightmost with Green Dot. Here in this project, 20cm distance is selected for taking any decision. Whenever Arduino gets less than the 15cm distance from any object, then Arduino stops the robot and moves it back and then turns it left or right. Now before moving it forward again, Arduino again checks whether any obstacle is present within the range of 15 cm distance, if yes then again repeats the previous process, else move the robot forward until it will detect any obstacle or object again. Fig. 4: Working of an Ultrasonic Sensor Module to detect an object III. WIRELESS ATTACKS The following are the possible attacks on a network that are demonstrated on the AGV. Beacon Flood Attack: A beacon flood attack consist of sending random (or targeted) probes in the air in enormous quantities resulting in creating confusion & chaos and Denial of Service condition in the worst case. Fig. 3: Figure shows The Leftmost and Rightmost coordinates along with the Boolean array representing the position of the object. 4310
3 Deauthentication frame format is as shown below. It is subtype 12 (0x0c) management frame (type 0). Fig. 7: Deauthentication Frame Format Fig. 5: A Beacon Flood Attack floods the Wifi Menu De-Authentication Attack: A De-Authentication attack consisting of continuously sending a special packet in the air which pretends to be coming from the Access Point (AP) asking the Client to disconnect from the Access Point. This prevents the Client to connect to the Access Point (AP) even after knowing the password to connect to the AP. Frame body of Deauthentication frame contains following: 1. Reason Code (2 byte) 2. Vendor Specific Information (one or more) w (MFP) info Fig. 8: Common Reason Codes The same property (the frame subtype being 12/ 0xC) can be used to detect a de-authentication attack. Fig. 6: The device is not able to connect to the Access Point Anatomy of a De-Authentication Packet: Station or AP can send a Deauthentication Frame when all communications are terminated. Setting a filter for wlan.fc.type_subtype == 0xc, and raising an alarm when the count of the deauthentication packets become high. Evil Twin Attack: An Evil Twin attack consists of having a duplicate Access Point (AP) with same name, if performed with a powerful wireless card can lead to the duplicate AP being listed over the authentic one in the list. This can increase the rate of success of the attack. 4311
4 Once the Client connects to the fake AP, all its data goes through the attacker s network setup, and the attacker can intercept and modify the data at his will, extending this attack to a phishing attack. This attack becomes specially easy with Access Points (APs) which have open authentication ( For example Cafe, Hotels, etc). However, this method can only be used in cases where the BSSID of the Evil Access point is not same as the legitimate AP. Cracking the Access Point s (AP) password When the client enters the password for connecting to the Access point, the handshake can be captured using a wireless card in monitor mode and then the handshake transaction can be brute forced with a list of passwords. If the AP s password is weak or common then it can be cracked with ease on a modern system. Fig. 9: Two Different Access Points with the same name can confuse the User Detecting Evil Twin attacks An evil twin is a fraudulent Wi-Fi access point that appears to be legitimate, set up to eavesdrop on wireless communications. The evil twin is the wireless LAN equivalent of the phishing scam. Because of the nature of the attack, the only way to detect this attack is whitelisting. In the whitelisting approach where we already save the SSID (service set identifier) and BSSID (MAC address of the AP's radio for that service set ) of the Access Points available in the vicinity so that we can compare the current state of the air ( in terms of the SSID BSSID pairs present ). Fig. 10: The handshake capture when ran against common password gave the AP s password (secret123). IV. LIMITATIONS 1. Short-circuiting between Arduino Mega Board and L298 motor board can occur which can lead to the downfall of the entire system. 2. Since rechargeable batteries are not used, the batteries used in our model can only last for up to five minutes. 3. If even one of the jumper wires are damaged, the connection of the Arduino to the L298 motor and then to the wheels, to make it rotate, fails. 4. Object detection solely on the basis of output from Camera is not possible for smooth navigation of the Bot. In cases of a video frame with object reflection and glare, the camera is not able to detect objects properly. 5. Because the flaws are in the design of , attacks can only be detected and not prevented. 4312
5 6. Since is a short range communication, the attacker has to be in vicinity and once the attack is detected the issue is reduced to a physical security issue. V. RESULTS In this paper, an autonomous robot is designed that is used to demonstrate the various possible attacks on the network. The attacks are detected and the same is notified to the network administrator. The robot can be used for surveillance purpose as it is autonomous, and hence can be used where human intervention is not required. VI. LITERATURE SURVEY [1] Object tracking robot using Raspberry PI with open Computer Vision: Focuses on generating algorithms using Linux OS with Python. [2] Object detection and tracking using image processing: This paper helped us with tacking based on colour via video streaming with the help of a webcam interfaced to the Raspberry pie. It moves the camera by tracking the object via Arduino. [3] Object Tracking Robot on Raspberry Pi using OpenCV: This helped us detect single objects at a time via image detection and placing those images in a virtual grid. The model in this paper had an arm that picked up the stationary object into its respective colour container with a predefined position. [4] Modelling simulation and control of a differential steering type mobile robot: This paper explained the entire Detection and De- Authentication DoS attacks in Wi-Fi Networks: Helped us focus on the De-Authentication attacks on Wire free network. This experiment was performed by sending spoofed De-Authentication frames which in turn disconnects their network. [5] The security analysis of WPA encryption in wireless network: Explained to us the encryption methods in a wire free network and analyse a method to crack it using BackTrack5. [6] The cryptanalysis of WPA encryption in wireless network: Described about the ways a wireless LAN can be protected, but the algorithm is complex and uses dictionary files. [7] Security analysis and implementation of a simple method for prevention and detection against Evil Twin Attack: This gave us with a solution for prevention and detection of evil twin attack by making minimal altercations to the Operating system. [8] User-side Wi-Fi evil twin attack detection using random wireless channel monitoring: Provided us with the solution to an Evil Twin Attack and detect whether the attack is an RAP or LAP. [9] Wireless hacking a Wi-Fi hack by cracking WEP: This gave us a larger view on the common threats related to wire free network and provided us with an insight of the entire process of cracking WEP. REFERENCES [1] Robot-Using-Ultrasonic-Sensor/ [2] e works [3] content/uploads/2014/12/ object- DETECTION-AND-TRACKING-USING- IMAGE-PROCESSING.pdf [4] [5] 35/number-4/IJETT-V35P235.pdf [6] our_features.html [7] [8] [9] [10]
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