Biblio

Roads are the backbone of our country, they play an important role for human progress. Roads seem to be dangerous and harmful for human beings on hills, near rivers, lakes and small ridges. It's possible with the help of IoT (Internet of things) to incorporate all the things made efficiently and effectively. IoT in combination with roads make daily life smart and excellent. This paper shows IoT technology will be the beginning of smart cities and it will reduce road accidents and collisions. If all vehicles are IoT based and connected with the internet, then an efficient method to guide, it performs urgent action, when less time is available. Internet and antenna technology in combination with IoT perform fully automation in our day-to-day life. It will provide excellent service as well as accuracy and precision.

Unmanned aerial vehicles (UAVs) and various network entities deployed on the ground can communicate with each other over the Internet of Drones (IoD), a network architecture designed expressly to allow communications between heterogenous entities. Drone technology has a wide range of uses, including on-demand package delivery, traffic and wild life surveillance, inspection of infrastructure and search, rescue and agriculture. However, IoD systems are vulnerable to numerous attacks, The main goal is to develop an all-encompassing security model that can be used to analyze security concerns in various UAV-based systems. With exceptional flexibility and increasing efficiency, trust management is a promising alternative to traditional detection methods. In a heterogeneous environment, it is also compatible with other security mechanisms. In this article, we present a fuzzy logic as an Insider Detection technique which calculate sensor data trust and assessing node behavior. To build confidence throughout the entire IoD, our proposal divides trust into two parts: Data trust and Node trust. This is in contrast to earlier models. Experimental results show that our solution is effective in terms of False positive ratio and Average of end-to-end delay.

Technological innovations are expanding rapidly in the public safety sector providing opportunities for more targeted and comprehensive urban crime deterrence and detection. Yet, the spatial dispersion of crimes may vary over time. Therefore, it is unclear whether and how sensors can optimally impact crime rates. We developed a Smart City Digital Twin-based method to dynamically place license plate reader (LPR) sensors and improve their detection and deterrence performance. Utilizing continuously updated crime records, the convolutional long short-term memory algorithm predicted areas crimes were most likely to occur. Then, a Monte Carlo traffic simulation simulated suspect vehicle movements to determine the most likely routes to flee crime scenes. Dynamic LPR placement predictions were made weekly, capturing the spatiotemporal variation in crimes and enhancing LPR performance relative to static placement. We tested the proposed method in Warner Robins, GA, and results support the method's promise in detecting and deterring crime.

Internet service providers (ISP) rely on network traffic classifiers to provide secure and reliable connectivity for their users. Encrypted traffic introduces a challenge as attacks are no longer viable using classic Deep Packet Inspection (DPI) techniques. Distinguishing encrypted from non-encrypted traffic is the first step in addressing this challenge. Several attempts have been conducted to identify encrypted traffic. In this work, we compare the detection performance of DPI, traffic pattern, and randomness tests to identify encrypted traffic in different levels of granularity. In an experimental study, we evaluate these candidates and show that a traffic pattern-based classifier outperforms others for encryption detection.

A distributed denial-of-service (DDoS) is a malicious attempt by attackers to disrupt the normal traffic of a targeted server, service or network. This is done by overwhelming the target and its surrounding infrastructure with a flood of Internet traffic. The multiple compromised computer systems (bots or zombies) then act as sources of attack traffic. Exploited machines can include computers and other network resources such as IoT devices. The attack results in either degraded network performance or a total service outage of critical infrastructure. This can lead to heavy financial losses and reputational damage. These attacks maximise effectiveness by controlling the affected systems remotely and establishing a network of bots called bot networks. It is very difficult to separate the attack traffic from normal traffic. Early detection is essential for successful mitigation of the attack, which gives rise to a very important role in cybersecurity to detect the attacks and mitigate the effects. This can be done by deploying machine learning or deep learning models to monitor the traffic data. We propose using various machine learning and deep learning algorithms to analyse the traffic patterns and separate malicious traffic from normal traffic. Two suitable datasets have been identified (DDoS attack SDN dataset and CICDDoS2019 dataset). All essential preprocessing is performed on both datasets. Feature selection is also performed before detection techniques are applied. 8 different Neural Networks/ Ensemble/ Machine Learning models are chosen and the datasets are analysed. The best model is chosen based on the performance metrics (DEEP NEURAL NETWORK MODEL). An alternative is also suggested (Next best - Hypermodel). Optimisation by Hyperparameter tuning further enhances the accuracy. Based on the nature of the attack and the intended target, suitable mitigation procedures can then be deployed.

Wireless sensor networks are used in many areas such as war field surveillance, monitoring of patient, controlling traffic, environmental and building surveillance. Wireless technology, on the other hand, brings a load of new threats with it. Because WSNs communicate across radio frequencies, they are more susceptible to interference than wired networks. The authors of this research look at the goals of WSNs in terms of security as well as DDOS attacks. The majority of techniques are available for detecting DDOS attacks in WSNs. These alternatives, on the other hand, stop the assault after it has begun, resulting in data loss and wasting limited sensor node resources. The study finishes with a new method for detecting the UDP Reflection Amplification Attack in WSN, as well as instructions on how to use it and how to deal with the case.

With the increasing complexity of the driving environment, more and more attention has been paid to the research on improving the intelligentization of traffic control. Among them, the digital twin-based internet of vehicle can establish a mirror system on the cloud to improve the efficiency of communication between vehicles, provide warning and safety instructions for drivers, avoid driving potential dangers. To ensure the security and effectiveness of data sharing in traffic control, this paper proposes a secure and privacy-preserving scheme for digital twin-based traffic control. Specifically, in the data uploading phase, we employ a group signature with a time-bound keys technique to realize data source authentication with efficient members revocation and privacy protection, which can ensure that data can be securely stored on cloud service providers after it synchronizes to its twin. In the data sharing stage, we employ the secure and efficient attribute-based access control technique to provide flexible and efficient data sharing, in which the parameters of a specific sub-policy can be stored during the first decryption and reused in subsequent data access containing the same sub-policy, thus reducing the computing complexity. Finally, we analyze the security and efficiency of the scheme theoretically.

Cybersecurity is without doubt becoming a societal challenge. It even starts to affect sectors that were not considered to be at risk in the past because of their relative isolation. One of these sectors is aviation in general, and specifically air traffic management. Nowadays, the cyber security is one of the essential issues of current Air Traffic Systems. Compliance with the basic principles of cyber security is mandated by European Union law as well as the national law. Therefore, EUROCONTROL as the provider of several tools or services (ARTAS, EAD, SDDS, etc.), is regularly conducting various activities, such as the cyber-security assessments, penetration testing, supply chain risk assessment, in order to maintain and improve persistence of the products against the cyber-attacks.

Aim: Object Detection is one of the latest topics in today’s world for detection of real time objects using Deep Belief Networks. Methods & Materials: Real-Time Object Detection is performed using Deep Belief Networks (N=24) over Convolutional Neural Networks (N=24) with the split size of training and testing dataset 70% and 30% respectively. Results: Deep Belief Networks has significantly better accuracy (81.2%) compared to Convolutional Neural Networks (47.7%) and attained significance value of p = 0.083. Conclusion: Deep Belief Networks achieved significantly better object detection than Convolutional Neural Networks for identifying real-time objects in traffic surveillance.

Protecting an identity of IPv6 packet against Denial-of-Service (DoS) attack, depend on the proposed methods of cryptography and steganography. Reliable communication using the security aspect is the most visible issue, particularly in IPv6 network applications. Problems such as DoS attacks, IP spoofing and other kinds of passive attacks are common. This paper suggests an approach based on generating a randomly unique identities for every node. The generated identity is encrypted and hided in the transmitted packets of the sender side. In the receiver side, the received packet verified to identify the source before processed. Also, the paper involves implementing nine experiments that are used to test the proposed scheme. The scheme is based on creating the address of IPv6, then passing it to the logistics map then encrypted by RSA and authenticated by SHA2. In addition, network performance is computed by OPNET modular. The results showed better computation power consumption in case of lost packet, average events, memory and time, and the better results as total memory is 35,523 KB, average events/sec is 250,52, traffic sent is 30,324 packets/sec, traffic received is 27,227 packets/sec, and lose packets is 3,097 packets/sec.

Software-Defined Networking (SDN) technique is presented in this paper to manage the Naval Supervisory Control and Data Acquisition (SCADA) network for equipping the network with the function of reconfiguration and scalability. The programmable nature of SDN enables a programmable Modular Topology Generator (MTG), which provides an extensive control over the network’s internal connectivity and traffic control. Specifically, two functions of MTG are developed and examined in this paper, namely linkHosts and linkSwitches. These functions are able to place the network into three different states, i.e., fully connected, fully disconnected, and partially connected. Therefore, it provides extensive security benefits and allows network administrators to dynamically reconfigure the network and adjust settings according to the network’s needs. Extensive tests on Mininet have demonstrated the effectiveness of SDN for enabling the reconfigurable and scalable Naval SCADA network. Therefore, it provides a potent tool to enhance the resiliency/survivability, scalability/compatibility, and security of naval SCADA networks.

Volumetric Distributed Denial of Service attacks forcefully disrupt the availability of online services by congesting network links with arbitrary high-volume traffic. This brute force approach has collateral impact on the upstream network infrastructure, making early attack traffic removal a key objective. To reduce infrastructure load and maintain service availability, we introduce ReCEIF, a topology-independent mitigation strategy for early, rule-based ingress filtering leveraging deep reinforcement learning. ReCEIF utilizes hierarchical heavy hitters to monitor traffic distribution and detect subnets that are sending high-volume traffic. Deep reinforcement learning subsequently serves to refine hierarchical heavy hitters into effective filter rules that can be propagated upstream to discard traffic originating from attacking systems. Evaluating all filter rules requires only a single clock cycle when utilizing fast ternary content-addressable memory, which is commonly available in software defined networks. To outline the effectiveness of our approach, we conduct a comparative evaluation to reinforcement learning-based router throttling.

Smart Security Solutions are in high demand with the ever-increasing vulnerabilities within the IT domain. Adjusting to a Work-From-Home (WFH) culture has become mandatory by maintaining required core security principles. Therefore, implementing and maintaining a secure Smart Home System has become even more challenging. ARGUS provides an overall network security coverage for both incoming and outgoing traffic, a firewall and an adaptive bandwidth management system and a sophisticated CCTV surveillance capability. ARGUS is such a system that is implemented into an existing router incorporating cloud and Machine Learning (ML) technology to ensure seamless connectivity across multiple devices, including IoT devices at a low migration cost for the customer. The aggregation of the above features makes ARGUS an ideal solution for existing Smart Home System service providers and users where hardware and infrastructure is also allocated. ARGUS was tested on a small-scale smart home environment with a Raspberry Pi 4 Model B controller. Its intrusion detection system identified an intrusion with 96% accuracy while the physical surveillance system predicts the user with 81% accuracy.

Automatic Identification System (AIS) plays a leading role in maritime navigation, traffic control, local and global maritime situational awareness. Today, the reliable and secure AIS operation is threatened by probable cyber attacks such as imitation of ghost vessels, false distress or security messages, or fake virtual aids-to-navigation. We propose a method for ensuring the authentication and integrity of AIS messages based on the use of the Message Authentication Code scheme and digital watermarking (WM) technology to organize an additional tag transmission channel. The method provides full compatibility with the existing AIS functionality.

Industrial Control System (ICS) transmits control and monitoring data between devices in an industrial environment that includes smart grids, water and gas distribution, or traffic control. Unlike traditional internet communication, ICS traffic is stable, periodical, and with regular communication patterns that can be described using statistical modeling. By observing selected features of ICS transmission, e.g., packet direction and inter-arrival times, we can create a statistical profile of the communication based on distribution of features learned from the normal ICS traffic. This paper demonstrates that using statistical modeling, we can detect various anomalies caused by irregular transmissions, device or link failures, and also cyber attacks like packet injection, scanning, or denial of service (DoS). The paper shows how a statistical model is automatically created from a training dataset. We present two types of statistical profiles: the master-oriented profile for one-to-many communication and the peer-to-peer profile that describes traffic between two ICS devices. The proposed approach is fast and easy to implement as a part of an intrusion detection system (IDS) or an anomaly detection (AD) module. The proof-of-concept is demonstrated on two industrial protocols: IEC 60870-5-104 (aka IEC 104) and IEC 61850 (Goose).

Industrial Control System (ICS) communication transmits monitoring and control data between industrial processes and the control station. ICS systems cover various domains of critical infrastructure such as the power plants, water and gas distribution, or aerospace traffic control. Security of ICS systems is usually implemented on the perimeter of the network using ICS enabled firewalls or Intrusion Detection Systems (IDSs). These techniques are helpful against external attacks, however, they are not able to effectively detect internal threats originating from a compromised device with malicious software. In order to mitigate or eliminate internal threats against the ICS system, we need to monitor ICS traffic and detect suspicious data transmissions that differ from common operational communication. In our research, we obtain ICS monitoring data using standardized IPFIX flows extended with meta data extracted from ICS protocol headers. Unlike other anomaly detection approaches, we focus on modelling the semantics of ICS communication obtained from the IPFIX flows that describes typical conversational patterns. This paper presents a technique for modelling ICS conversations using frequency prefix trees and Deterministic Probabilistic Automata (DPA). As demonstrated on the attack scenarios, these models are efficient to detect common cyber attacks like the command injection, packet manipulation, network scanning, or lost connection. An important advantage of our approach is that the proposed technique can be easily integrated into common security information and event management (SIEM) systems with Netflow/IPFIX support. Our experiments are performed on IEC 60870-5-104 (aka IEC 104) control communication that is widely used for the substation control in smart grids.

Bitcoin is a virtual encrypted digital currency based on a peer-to-peer network. In recent years, for higher anonymity, more and more Bitcoin users try to use Tor hidden services for identity and location hiding. However, previous studies have shown that Tor are vulnerable to traffic fingerprinting attack, which can identify different websites by identifying traffic patterns using statistical features of traffic. Our work shows that traffic fingerprinting attack is also effective for the Bitcoin hidden nodes detection. In this paper, we proposed a novel lightweight Bitcoin hidden service traffic fingerprinting, using a random decision forest classifier with features from TLS packet size and direction. We test our attack on a novel dataset, including a foreground set of Bitcoin hidden node traffic and a background set of different hidden service websites and various Tor applications traffic. We can detect Bitcoin hidden node from different Tor clients and website hidden services with a precision of 0.989 and a recall of 0.987, which is higher than the previous model.

Though intrusion detection systems provide actionable alerts based on signature-based or anomaly-based traffic patterns, the majority of systems still rely on human analysts to identify and contain the root cause of security incidents. This process is naturally susceptible to human error and is time-consuming, which may allow for further enumeration and pivoting within a compromised environment. Through this paper, we have augmented traditional signature-based network intrusion detection systems with a trust framework whose reduction and redemption values are a function of the severity of the incident, the degree of connectivity of nodes and the time elapsed. A lightweight implementation on the nodes coupled with a multithreaded approach on the central trust server has shown the capability to scale to larger networks with high traffic volumes and a varying proportion of suspicious traffic patterns.

Today's global network is filled with attackers both live and automated seeking to identify and compromise vulnerable devices, with initial scanning and attack activity occurring within minutes or even seconds of being connected to the Internet. To better understand these events, honeypots can be deployed to monitor and log activity by simulating actual Internet facing services such as SSH, Telnet, HTTP, or FTP, and malicious activity can be logged as attempts are made to compromise them. In this study six multi-service honeypots are deployed in locations around the globe to collect and catalog traffic over a period of several months between March and December, 2020. Analysis is performed on various characteristics including source and destination IP addresses and port numbers, usernames and passwords utilized, commands executed, and types of files downloaded. In addition, Cowrie log data is restructured to observe individual attacker sessions, study command sequences, and monitor tunneling activity. This data is then correlated across honeypots to compare attack and traffic patterns with the goal of learning more about the tactics being employed. By gathering data gathered from geographically separate zones over a long period of time a greater understanding can be developed regarding attacker intent and methodology, can aid in the development of effective approaches to identifying malicious behavior and attack sources, and can serve as a cyber-threat intelligence feed.

In this study, we present the abstracted pedestrian behavior representation and abnormal detection method based on user feedback for pedestrian video surveillance system. Video surveillance data is large in size and difficult to process in real time. To solve this problem, we suggested a method of expressing the pedestrian behavior with abbreviated map. In the video surveillance system, false detection of an abnormal situation becomes a big problem. If surveillance user can guide the false detection case as human in the loop, the surveillance system can learn the case and reduce the false detection error in the future. We suggested user feedback based abnormal pedestrian detection method. By the suggested user feedback algorithm, the false detection can be reduced to less than 0.5%.

Video analytics has many applications in traffic control, security monitoring, action/event analysis, etc. With the adoption of deep neural networks, the accuracy of video analytics in video streams has been greatly improved. However, deep neural networks for performing video analytics are compute-intensive. In order to reduce processing time, many systems switch to the lower frame rate or resolution. State-of-the-art switching approaches adjust configurations by profiling video clips on a large configuration space. Multiple configurations are tested periodically and the cheapest one with a desired accuracy is adopted. In this paper, we propose a method that adapts the configuration by analyzing past video analytics results instead of profiling candidate configurations. Our method adopts a lower/higher resolution or frame rate when objects move slow/fast. We train a model that automatically selects the best configuration. We evaluate our method with two real-world video analytics applications: traffic tracking and pose estimation. Compared to the periodic profiling method, our method achieves 3%-12% higher accuracy with the same resource cost and 8-17x faster with comparable accuracy.

Nowadays, video surveillance cameras are widely used in many smart city applications for ensuring road safety. We can use video data from them to solve such tasks as traffic management, driving control, environmental monitoring, etc. Most of these applications are based on object recognition and tracking algorithms. However, the video image quality is not always meet the requirements of such algorithms due to the influence of different external factors. A variety of adverse weather conditions produce noise on the images, which often makes it difficult to detect objects correctly. Lately, deep learning methods show good results in image processing, including denoising tasks. This work is devoted to the study of using these methods for image quality enhancement in difficult weather conditions such as snow, rain, fog. Different deep learning techniques were evaluated in terms of their impact on the quality of object detection/recognition. Finally, the system for automatic image denoising was developed.

CPS traffic characteristics is one of key techniques of Cyber-Physical Systems (CPS). A deep research of CPS network traffic characteristics can help to better plan and design CPS networks. A brief overview of the key concepts of CPS is firstly presented. Then CPS application scenarios are analyzed in details and classified. The characteristics of CPS traffic is analyzed theoretically for different CPS application scenarios. At last, the characteristics of CPS traffic is verified using NS-2 simulation.

This work investigates entropy-based prevention of Distributed Denial-of-Service (DDoS) attacks for Software Defined Networks (SDN). The experiments are conducted on a virtual SDN testbed setup within Mininet, a Linux-based network emulator. An arms race iterates on the SDN testbed between offense, launching botnet-based DDoS attacks with progressive sophistications, and defense who is deploying SDN controls with emerging technologies from other faucets of cyber engineering. The investigation focuses on the transmission control protocol’s synchronize flood attack that exploits vulnerabilities in the three-way TCP handshake protocol, to lock up a host from serving new users.The defensive strategy starts with a common packet filtering-based design from the literature to mitigate attacks. Utilizing machine learning algorithms, SDNs actively monitor all possible traffic as a collective dataset to detect DDoS attacks in real time. A constant upgrade to a stronger defense is necessary, as cyber/network security is an ongoing front where attackers always have the element of surprise. The defense further invests on entropy methods to improve early detection of DDoS attacks within the testbed environment. Entropy allows SDNs to learn the expected normal traffic patterns for a network as a whole using real time mathematical calculations, so that the SDN controllers can sense the distributed attack vectors building up before they overwhelm the network.This work reveals the vulnerabilities of SDNs to stealthy DDoS attacks and demonstrates the effectiveness of deploying entropy in SDN controllers for detection and mitigation purposes. Future work includes provisions to use these entropy detection methods, as part of a larger system, to redirect traffic and protect networks dynamically in real time. Other types of DoS, such as ransomware, will also be considered.

With the rapid growth of the Internet of Things (IoT) applications in smart regions/cities, for example, smart healthcare, smart homes/offices, there is an increase in security threats and risks. The IoT devices solve real-world problems by providing real-time connections, data and information. Besides this, the attackers can tamper with sensors, add or remove them physically or remotely. In this study, we address the IoT security sensor tampering issue in an office environment. We collect data from real-life settings and apply machine learning to detect sensor tampering using two methods. First, a real-time view of the traffic patterns is considered to train our isolation forest-based unsupervised machine learning method for anomaly detection. Second, based on traffic patterns, labels are created, and the decision tree supervised method is used, within our novel Anomaly Detection using Machine Learning (AD-ML) system. The accuracy of the two proposed models is presented. We found 84% with silhouette metric accuracy of isolation forest. Moreover, the result based on 10 cross-validations for decision trees on the supervised machine learning model returned the highest classification accuracy of 91.62% with the lowest false positive rate.