Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety

Rapid advances in technology and shifting tastes among motorists have reworked the contemporary automobile production sector. Driving is now much safer and more convenient than ever before thanks to a plethora of new technology and apps. Millions of people are hurt every year despite the fact that automobiles are networked and have several sensors and radars for collision avoidance. Each year, many of them are injured in car accidents and need emergency care, and sadly, the fatality rate is growing. Vehicle and pedestrian collisions are still a serious problem, making it imperative to advance methods that prevent them. This paper refines our previous efficient VANET-based pedestrian safety system based on two-way communication between smart cars and the cell phones of vulnerable road users. We implemented the scheme using C and NS3 to simulate different traffic scenarios. Our objective is to measure the additional overhead to protect vulnerable road users. We prove that our proposed scheme adds just a little amount of additional overhead and successfully satisfies the stringent criteria of safety applications

Vehicle to Pedestrian Systems: Survey, Challenges and Recent Trends

The accelerated rise of new technologies has reshaped the manufacturing industry of contemporary vehicles. Numerous technologies and applications have completely revolutionized the driving experience in terms of both safety and convenience. Although vehicles are now connected and equipped with a multitude of sensors and radars for collision avoidance, millions of people suffer serious accidents on the road, and unfortunately, the death rate is still on the rise. Collisions are still a dire reality for vehicles and pedestrians alike, which is why the improvement of collision prevention mechanisms is an ongoing necessity. Collision prevention mechanisms have evolved from vision-based systems like radars to systems that transcend the driver’s line of sight. These latter systems depend on vehicular ad hoc networks (VANETs) to employ bidirectional communication between vehicles and other vehicles (V2V) as well as between vehicles and road infrastructure (V2I). Recently, research has expanded to include a new communication system between vehicles and pedestrians (V2P) through the latter’s smartphones. In this paper, we provide an extensive survey of existing V2P projects, categorize different parameters that influence V2P system design, compare different communication technologies used in V2P systems and present an overview of recent trends that solve problems in V2P systems like network congestion, pedestrian localization, and context information exchange. The main contribution of our work is to pave the road for future research by providing a comprehensive view of projects, challenges and recent trends in the emerging and rapidly growing field of V2P system design

Privacy Preservation Using Machine Learning in the Internet of Things

The internet of things (IoT) has prepared the way for a highly linked world, in which everything is interconnected, and information exchange has become more easily accessible via the internet, making it feasible for various applications that enrich the quality of human life. Despite such a potential vision, users’ privacy on these IoT devices is a significant concern. IoT devices are subject to threats from hackers and malware due to the explosive expansion of IoT and its use in commerce and critical infrastructures. Malware poses a severe danger to the availability and reliability of IoT devices. If left uncontrolled, it can have profound implications, as IoT devices and smart services can collect personally identifiable information (PII) without the user’s knowledge or consent. These devices often transfer their data into the cloud, where they are stored and processed to provide the end users with specific services. However, many IoT devices do not meet the same security criteria as non-IoT devices; most used schemes do not provide privacy and anonymity to legitimate users. Because there are so many IoT devices, so much malware is produced every day, and IoT nodes have so little CPU power, so antivirus cannot shield these networks from infection. Because of this, establishing a secure and private environment can greatly benefit from having a system for detecting malware in IoT devices. In this paper, we will analyze studies that have used ML as an approach to solve IoT privacy challenges, and also investigate the advantages and drawbacks of leveraging data in ML-based IoT privacy approaches. Our focus is on using ML models for detecting malware in IoT devices, specifically spyware, ransomware, and Trojan horse malware. We propose using ML techniques as a solution for privacy attack detection and test pattern generation in the IoT. The ML model can be trained to predict behavioral architecture. We discuss our experiments and evaluation using the “MalMemAnalysis” datasets, which focus on simulating real-world privacy-related obfuscated malware. We simulate several ML algorithms to prove their capabilities in detecting malicious attacks against privacy. The experimental analysis showcases the high accuracy and effectiveness of the proposed approach in detecting obfuscated and concealed malware, outperforming state-of-the-art methods by 99.50%, and would be helpful in safeguarding an IoT network from malware. Experimental analysis and results are provided in detail

An innovative approach for the wormhole attack detection and prevention in wireless ad hoc networks

[abstract not available

IoT Vulnerabilities and Attacks: SILEX Malware Case Study

The Internet of Things (IoT) is rapidly growing and is projected to develop in future years. The IoT connects everything from Closed Circuit Television (CCTV) cameras to medical equipment to smart home appliances to smart automobiles and many more gadgets. Connecting these gadgets is revolutionizing our lives today by offering higher efficiency, better customer service, and more effective goods and services in a variety of industries and sectors. With this anticipated expansion, many challenges arise. Recent research ranked IP cameras as the 2nd highest target for IoT attacks. IoT security exhibits an inherent asymmetry where resource-constrained devices face attackers with greater resources and time, creating an imbalanced power dynamic. In cybersecurity, there is a symmetrical aspect where defenders implement security measures while attackers seek symmetrical weaknesses. The SILEX malware case highlights this asymmetry, demonstrating how IoT devices’ limited security made them susceptible to a relatively simple yet destructive attack. These insights underscore the need for robust, proactive IoT security measures to address the asymmetrical risks posed by adversaries and safeguard IoT ecosystems effectively. In this paper, we present the IoT vulnerabilities, their causes, and how to detect them. We focus on SILEX, one of the famous malware that targets IoT, as a case study and present the lessons learned from this malware

Intrusion Detection for Electric Vehicle Charging Systems (EVCS)

The market for Electric Vehicles (EVs) has expanded tremendously as seen in the recent Conference of the Parties 27 (COP27) held at Sharm El Sheikh, Egypt in November 2022. This needs the creation of an ecosystem that is user-friendly and secure. Internet-connected Electric Vehicle Charging Stations (EVCSs) provide a rich user experience and add-on services. Eventually, the EVCSs are connected to a management system, which is the Electric Vehicle Charging Station Management System (EVCSMS). Attacking the EVCS ecosystem remotely via cyberattacks is rising at the same rate as physical attacks and vandalism happening on the physical EVCSs. The cyberattack is more severe than the physical attack as it may affect thousands of EVCSs at the same time. Intrusion Detection is vital in defending against diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System’s (IDS) problem is a classification problem. The IDS tries to determine if each traffic stream is legitimate or malicious, that is, binary classification. Furthermore, the IDS can identify the type of malicious traffic, which is called multiclass classification. In this paper, we address IoT security issues in EVCS by using different machine learning techniques and using the native IoT dataset to discover fraudulent traffic in EVCSs, which has not been performed in any previous research. We also compare different machine learning classifier algorithms for detecting Distributed Denial of Service (DDoS) attacks in the EVCS network environment. A typical Internet of Things (IoT) dataset obtained from actual IoT traffic is used in the paper. We compare classification algorithms that are placed in line with the traffic and contain DDoS attacks targeting the EVCS network. The results obtained from this research improve the stability of the EVCS system and significantly reduce the number of cyberattacks that could disrupt the daily life activities associated with the EVCS ecosystem

A New Scheme for Ransomware Classification and Clustering Using Static Features

Ransomware is a strain of malware that disables access to the user’s resources after infiltrating a victim’s system. Ransomware is one of the most dangerous malware organizations face by blocking data access or publishing private data over the internet. The major challenge of any entity is how to decrypt the files encrypted by ransomware. Ransomware’s binary analysis can provide a means to characterize the relationships between different features used by ransomware families to track the ransomware encryption mechanism routine. In this paper, we compare the different ransomware detection approaches and techniques. We investigate the criteria, parameters, and tools used in the ransomware detection ecosystem. We present the main recommendations and best practices for ransomware mitigation. In addition, we propose an efficient ransomware indexing system that provides search functionalities, similarity checking, sample classification, and clustering. The new system scheme mainly targets native ransomware binaries, and the indexing engine depends on hybrid data from the static analyzer system. Our scheme tracks and classifies ransomware based on static features to find the similarity between different ransomware samples. This is done by calculating the absolute Jaccard index. Results have shown that Import Address Table (IAT) feature can be used to classify different ransomware more accurately than the Strings feature

NG-MVEE: A New Proposed Hybrid Technique for Enhanced Mitigation of Code Re-Use Attack

Code-Reuse Attacks (CRAs) are solid mechanisms to bypass advanced software and hardware defenses because they use the software’s own code and they are very hard to be detected without significant overhead. Numerous methods have been proposed to protect against memory-based attacks that result from reusing parts of the attacked binary code. In this paper, two problems were tackled. the first problem is the lack of a categorized survey, analysis, and evaluation of the different CRAs proposed in the literature. The second problem is the inherent vulnerability that exists in protection techniques that are based on Multi-Variant Execution Environment (MVEE) since they are using shared Linux libraries with gadget-prone codes. In the paper a novel framework of CRA mitigation is introduced; fusing the two different prominent techniques of control flow integrity and multi-variant execution environment. The novel mitigation technique, named Next Generation MVEE (NG-MVEE), was built upon an existing generic CRA detection system (GHUMVEE) and complemented with a different CRA detection technique (G-Free) in order to provide comprehensive protection against code-reuse attacks. The outcome of the hybrid system is an optimized hybrid version of an MVEE technique, with minimal performance overhead increase due to the added protection layer of the G-Free technique. A median of 7% performance overhead resulted from the proposed protection system

Intrusion Detection for Electric Vehicle Charging Systems (EVCS)

The market for Electric Vehicles (EVs) has expanded tremendously as seen in the recent Conference of the Parties 27 (COP27) held at Sharm El Sheikh, Egypt in November 2022. This needs the creation of an ecosystem that is user-friendly and secure. Internet-connected Electric Vehicle Charging Stations (EVCSs) provide a rich user experience and add-on services. Eventually, the EVCSs are connected to a management system, which is the Electric Vehicle Charging Station Management System (EVCSMS). Attacking the EVCS ecosystem remotely via cyberattacks is rising at the same rate as physical attacks and vandalism happening on the physical EVCSs. The cyberattack is more severe than the physical attack as it may affect thousands of EVCSs at the same time. Intrusion Detection is vital in defending against diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System’s (IDS) problem is a classification problem. The IDS tries to determine if each traffic stream is legitimate or malicious, that is, binary classification. Furthermore, the IDS can identify the type of malicious traffic, which is called multiclass classification. In this paper, we address IoT security issues in EVCS by using different machine learning techniques and using the native IoT dataset to discover fraudulent traffic in EVCSs, which has not been performed in any previous research. We also compare different machine learning classifier algorithms for detecting Distributed Denial of Service (DDoS) attacks in the EVCS network environment. A typical Internet of Things (IoT) dataset obtained from actual IoT traffic is used in the paper. We compare classification algorithms that are placed in line with the traffic and contain DDoS attacks targeting the EVCS network. The results obtained from this research improve the stability of the EVCS system and significantly reduce the number of cyberattacks that could disrupt the daily life activities associated with the EVCS ecosystem

A Survey on Parameters Affecting MANET Performance

A mobile ad hoc network (MANET) is an infrastructure-less network where mobile nodes can share information through wireless links without dedicated hardware that handles the network routing. MANETs’ nodes create on-the-fly connections with each other to share information, and they frequently join and leave MANET during run time. Therefore, flexibility in MANETs is needed to be able to handle variations in the number of existing network nodes. An effective routing protocol should be used to be able to route data packets within this dynamic network. Lacking centralized infrastructure in MANETs makes it harder to secure communication between network nodes, and this lack of infrastructure makes network nodes vulnerable to harmful attacks. Testbeds might be used to test MANETs under specific conditions, but researchers prefer to use simulators to obtain more flexibility and less cost during MANETs’ environment setup and testing. A MANET’s environment is dependent on the required scenario, and an appropriate choice of the used simulator that fulfills the researcher’s needs is very important. Furthermore, researchers need to define the simulation parameters and the other parameters required by the used routing protocol. In addition, if the MANET’s environment handles some conditions where malicious nodes perform network attacks, the parameters affecting the MANET from the attack perspective need to be understood. This paper collects environmental parameters that might be needed to be able to set up the required environment. To be able to evaluate the network’s performance under attack, different environmental parameters that evaluate the overall performance are also collected. A survey of the literature contribution is performed based on 50 recent papers. Comparison tables and statistical charts are created to show the literature contribution and the used parameters within the scope of the collected papers of our survey. Results show that the NS-2 simulator is the most popular simulator used in MANETs