Implementation of Human Detection on Raspberry Pi for Smart Surveillance

Zero conf protocols date from 1999. They provide plug and play mechanisms to set up networks without having to conFigure DNS or DHCP servers. Almost every device (PCs, printers, scanners, etc.) nowadays “speaks” one of these protocols, sometimes without its owner being even aware of it. The booming IoT ecosystem, in particular, relies heavily on them. Unfortunately, these protocols offer a number of different ways to run, so called, man in the middle attacks (MITM). Some previous publications have mentioned and have taken advantage of one or another of these design flaws. In this paper, we provide a deep dive into the various issues at hand and show the extent of the problem. We consider that the growing reliance of networks on these protocols represent an underestimated and ill covered threat. We have run a number of experiments (300) to test various implementations and discuss our results. We also propose means to detect these attacks thanks to Zeek (aka Bro). We make the attack code as well as the Zeek scripts available to the research community in a format that makes replication of our results possible by researchers while not easy to use by script kiddies.

Cyber security of distribution power systems is of an increasing and pressing importance due to the fast modernisation of current systems. Cyber attacks on distribution power systems may aim to operate the system inefficiently, steal private smart meter data or cause intentional false tripping of few or all feeders. In this paper, a Man in The Middle (MiTM) attack on a power factor correction unit is implemented and demonstrated to overload a distribution feeder and cause an intentional false tripping of the entire feeder causing regional blackout. Experimental implementation of the attack is carried out in a laboratory-scale setup using commercial power equipment under different loading conditions to demonstrate the effectiveness of this attack.

ABSTRAK Penelitian ini bertujuan untuk mengevaluasi tingkat keamanan jaringan WLAN yang telah diterapkan di SMK Kristen Getsemani Manado dengan menggunakan metode Penetration Testing. Dalam Analisa keamanan jaringan WLAN dilakukan dengan menggunakan metode Penetration Testing dengan mensimulasikan bentuk serangan terhadap jaringan menggunakan sistem operasi yang biasa digunakan dalam penetration yaitu Kali Linux. Berdasarkan hasil pengujian ditemukan bahwa jaringan WLAN di SMK Kristen Getsemani Manado masih memiliki kerentanan yang dapat dimanfaatkan oleh pihak yang tidak bertanggung jawab.Dua jenis serangan yang berhasil dilakukan dalam penelitian ini adalah Analisis Lalu lintas Jaringan(Traffic Network) dan Man In The Middle Attack. Dari temuan tersebut disimpulkan bahwa Jaringan WLAN di SMK Kristen Getsemani Manado belum memberikan perilndungan yang optimal bagi pengguna yang terhubung, sehingga masih memungkinkan terjadinya gangguan saat mengakses layanan internet. ABSTRACT This study aims to broadcast the level of WLAN network security that has been implemented at SMK Kristen Getsemani Manado using the Penetration Testing method. In the WLAN network security analysis is carried out using the Penetration Testing method with a copy of the form of attack on the network using the operating system commonly used in penetration, namely Kali Linux. Based on the test results, it was found that the WLAN network at SMK Kristen Getsemani Manado still has vulnerabilities that can be exploited by irresponsible parties. Two types of attacks that were successfully carried out in this study were Network Traffic Analysis and Man In The Middle Attack. From these findings, it is concluded that the WLAN Network at SMK Kristen Getsemani Manado has not provided optimal protection for connected users, so it is still possible for disruptions to occur when accessing internet services.

The open stack communication protocol of the internet of things always in security threats. The vulnerability of protocol faces a problem of man in middle attacks and loss of integrity of communication. Cryptography plays a vital role in security authentication on IoT based enable communication system. IoT devices' limited resource constraints cannot offer any security mechanism on the physical level and network level. The key generation methods uplift the security provision in IoT enables communication model. The process of key generation applied three types of cryptography algorithm, symmetric, asymmetric and hybrid algorithm. The complexity of key generation methods needs more computational time and memory; this is the limitation of IoT devices. So various authors proposed a lightweight key generation algorithm for security and authentication. This paper analyzed the performance of key generation methods based on different cryptography approaches and measured their security strength. For the analysis process, use MATLAB software and sample text data file for the communication of two parties. Some ADHOC methods apply to measure protocol weakness during the communication network compromised with a man in the middle attack. The variable key size also reflects the security strength of IoTs enable devices.

In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, etc.). Furthermore, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes. The Edge-IIoTset dataset can be publicly accessed from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://ieee-dataport.org/8939</uri> .

The roll out of the deployment of the 5G technology has been ongoing globally. The deployment of the technologies associated with 5G has seen mixed reaction as regards its prospects to improve communication services in all spares of life amid its security concerns. The security concerns of 5G network lies in its architecture and other technologies that optimize the performance of its architecture. There are many fractions of 5G security architecture in the literature, a holistic security architectural structure will go a long way in tackling the security challenges. In this paper, the review of the security challenges of the 5G technology based on its architecture is presented along with their proposed solutions. This review was carried out with some keywords relating to 5G securities and architecture; this was used to retrieve appropriate literature for fitness of purpose. The 5G security architectures are mojorly centered around the seven network security layers; thereby making each of the layers a source of security concern on the 5G network. Many of the 5G security challenges are related to authentication and authorization such as denial-of-service attacks, man in the middle attack and eavesdropping. Different methods both hardware (Unmanned Aerial Vehicles, field programmable logic arrays) and software (Artificial intelligence, Machine learning, Blockchain, Statistical Process Control) has been proposed for mitigating the threats. Other technologies applicable to 5G security concerns includes: Multi-radio access technology, smart-grid network and light fidelity. The implementation of these solutions should be reviewed on a timely basis because of the dynamic nature of threats which will greatly reduce the occurrence of security attacks on the 5G network.

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

Secure authentication scheme to thwart RT MITM, CR MITM and malicious browser extension based phishing attacks

Evaluating the Impact of Cyberattacks on PLC Performance: A Systematic Implementation and Empirical Investigation

Short Messaging Service (SMS) has removed difference between near and far. Because of guaranteed delivery, people use it on daily basis for connectivity. Not only humans but Information Systems have also utilized it to connect, known as automated messaging. Although automated messaging holds prominent SMS market share but full potential was not reached because of the fact that SMS has no security at all. Any SMS sent or received can be intercepted and manipulated by Man In The Middle (MITM). To resist MITM in next generation smartphones we have proposed a new framework which can be used to secure both human and automated messaging. This automated messaging. This new framework use HTTPS for secure key exchange, ECC, RSA as encryption algorithm and GSM network to send and receive encrypted messages.

The integration of Internet of Things (IoT) into energy management systems has been changed energy grids and energy technologies; with technological advancement increase in cybersecurity concerns has been introduced significantly. Observation and manipulation of sensor data has become easier particularly through cyber-attacks such as man in the middle attack (MITM), Due to this cyber-attacks credibility of sensor data is been affected which can result into drastic disruption in energy grid and energy technologies operations. Through this paper challenges in IoT system used in energy management and energy monitoring will be observed. A testbed is set up using a PLC (Programmable Logic Controller) is established using Raspberry Pi as hardware and Open PLC Library as software to run ladder logic. A sensor DHT11 record temperature value of heating element which is controlled by PLC, using machine learning algorithms model has been trained in such a way that any abrupt change in reading or manipulation can be detected. To validate this ML model Ettercap tool has been used for manipulation of sensor reading. To increase the accuracy of model camera feed is also included to check the status of heating element. Overall model gives successful implementation of ML model and detection of manipulation in sensor reading when system is compromised in Modbus TCP/IP communication.

Relay attacks are passive man in the middle attacks, aiming to extend the physical distance of devices involved in a transaction beyond their operating environment, within the restricted time-frame. In the field of smartphones, proposals have been put forward suggesting sensing the natural ambient environment as an effective Proximity and Relay Attack Detection (PRAD) mechanism. However, these proposals are not in compliance with industry imposed constraints (e.g. EMV and ITSO) mandating that transactions should complete within a certain time-frame (e.g. 500ms for EMV contactless transactions). The generation of an artificial ambient environment (AAE) using peripherals of the transaction devices has shown positive results when using infrared light as an AAE actuator. In this paper we propose the use of vibration as an alternative AAE actuator. We empirically evaluated the effectiveness of the proposed solution as a PRAD mechanism on an experimental test-bed that we deployed. A total of 36,000 genuine and relay attack transaction pairs were analysed using well-known machine learning algorithms. The results of our analysis indicate that the proposed solution is highly effective.

In a present-day world there are different types of attacks being launched on computing devices. World is experiencing more and more cyber-attacks and types of attacks are also increasing. For example, an IoT device in a home network can act as a botnet attacking other devices or there could be man in the middle attack. As time goes by more and more devices are being connected within any given network. All these devices will be vulnerable to attacks if any one of the devices is compromised within the network. This complicates Intrusion Detection in any given network. Manual detection and intervention is nearly impossible. Hence it is quintessential to detect different types of attacks with more confidence with less computation complexity and time. A lot of research work has already been done in this area where the attacks have been studied separately. In this paper we focus on detecting intrusions including IoT botnet attacks and other types of network attacks. To achieve this, we build a multiclass classification using supervised learning models along with the dimensionality reduction technique. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD dataset. In this study we used a new dataset, IoT network Intrusion Detection dataset.

Our modern technologies are increasingly moving towards cryptography to secure the data transmission via a wireless medium. This is because transmission via wireless media is convenient to the modern world, but there still exists a major drawback cryptography in the name of Security. The Vigenere cipher belongs to the family of ciphers in which the cipher text is the ‘sum’ of the plaintext and some keystream. The Vigenere cipher is the encryption technique used in most of the communications, particularly in SMS encryption and decryption. The Vigenere key stream is not random, and therefore the cipher is vulnerable to attack. The plaintext attack and dictionary attack are the most commonly occurring attack leading to man in the middle attack. To overcome these two common attacks in vigenere cipher, we propose a noble approach that uses numbers instead of English alphabets to generate the key.