Biblio

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2021-05-13
Feng, Xiaohua, Feng, Yunzhong, Dawam, Edward Swarlat.  2020.  Artificial Intelligence Cyber Security Strategy. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :328—333.
Nowadays, STEM (science, technology, engineering and mathematics) have never been treated so seriously before. Artificial Intelligence (AI) has played an important role currently in STEM. Under the 2020 COVID-19 pandemic crisis, coronavirus disease across over the world we are living in. Every government seek advices from scientist before making their strategic plan. Most of countries collect data from hospitals (and care home and so on in the society), carried out data analysis, using formula to make some AI models, to predict the potential development patterns, in order to make their government strategy. AI security become essential. If a security attack make the pattern wrong, the model is not a true prediction, that could result in thousands life loss. The potential consequence of this non-accurate forecast would be even worse. Therefore, take security into account during the forecast AI modelling, step-by-step data governance, will be significant. Cyber security should be applied during this kind of prediction process using AI deep learning technology and so on. Some in-depth discussion will follow.AI security impact is a principle concern in the world. It is also significant for both nature science and social science researchers to consider in the future. In particular, because many services are running on online devices, security defenses are essential. The results should have properly data governance with security. AI security strategy should be up to the top priority to influence governments and their citizens in the world. AI security will help governments' strategy makers to work reasonably balancing between technologies, socially and politics. In this paper, strategy related challenges of AI and Security will be discussed, along with suggestions AI cyber security and politics trade-off consideration from an initial planning stage to its near future further development.
2021-02-10
Tanana, D..  2020.  Behavior-Based Detection of Cryptojacking Malware. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :0543—0545.
With rise of cryptocurrency popularity and value, more and more cybercriminals seek to profit using that new technology. Most common ways to obtain illegitimate profit using cryptocurrencies are ransomware and cryptojacking also known as malicious mining. And while ransomware is well-known and well-studied threat which is obvious by design, cryptojacking is often neglected because it's less harmful and much harder to detect. This article considers question of cryptojacking detection. Brief history and definition of cryptojacking are described as well as reasons for designing custom detection technique. We also propose complex detection technique based on CPU load by an application, which can be applied to both browser-based and executable-type cryptojacking samples. Prototype detection program based on our technique was designed using decision tree algorithm. The program was tested in a controlled virtual machine environment and achieved 82% success rate against selected number of cryptojacking samples. Finally, we'll discuss generalization of proposed technique for future work.
2021-05-13
Ho, Tsung-Yu, Chen, Wei-An, Huang, Chiung-Ying.  2020.  The Burden of Artificial Intelligence on Internal Security Detection. 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET). :148—150.
Our research team have devoted to extract internal malicious behavior by monitoring the network traffic for many years. We applied the deep learning approach to recognize the malicious patterns within network, but this methodology may lead to more works to examine the results from AI models production. Hence, this paper addressed the scenario to consider the burden of AI, and proposed an idea for long-term reliable detection in the future work.
2021-09-21
Barr, Joseph R., Shaw, Peter, Abu-Khzam, Faisal N., Yu, Sheng, Yin, Heng, Thatcher, Tyler.  2020.  Combinatorial Code Classification Amp; Vulnerability Rating. 2020 Second International Conference on Transdisciplinary AI (TransAI). :80–83.
Empirical analysis of source code of Android Fluoride Bluetooth stack demonstrates a novel approach of classification of source code and rating for vulnerability. A workflow that combines deep learning and combinatorial techniques with a straightforward random forest regression is presented. Two kinds of embedding are used: code2vec and LSTM, resulting in a distance matrix that is interpreted as a (combinatorial) graph whose vertices represent code components, functions and methods. Cluster Editing is then applied to partition the vertex set of the graph into subsets representing nearly complete subgraphs. Finally, the vectors representing the components are used as features to model the components for vulnerability risk.
2021-02-16
Saxena, U., Sodhi, J., Singh, Y..  2020.  A Comprehensive Approach for DDoS Attack Detection in Smart Home Network Using Shortest Path Algorithm. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). :392—395.
A Distributed Denial of Service (DDoS) attack is an attack that compromised the bandwidth of the whole network by choking down all the available network resources which are publically available, thus makes access to that resource unavailable. The DDoS attack is more vulnerable than a normal DoS attack because here the sources of attack origin are more than one, so users cannot even estimate how to detect and where to take actions so that attacks can be dissolved. This paper proposed a unique approach for DDoS detection using the shortest path algorithm. This Paper suggests that the remedy that must be taken in order to counter-affect the DDoS attack in a smart home network.
2021-02-10
Varlioglu, S., Gonen, B., Ozer, M., Bastug, M..  2020.  Is Cryptojacking Dead After Coinhive Shutdown? 2020 3rd International Conference on Information and Computer Technologies (ICICT). :385—389.
Cryptojacking is the exploitation of victims' computer resources to mine for cryptocurrency using malicious scripts. It had become popular after 2017 when attackers started to exploit legal mining scripts, especially Coinhive scripts. Coinhive was actually a legal mining service that provided scripts and servers for in-browser mining activities. Nevertheless, over 10 million web users had been victims every month before the Coinhive shutdown that happened in Mar 2019. This paper explores the new era of the cryptojacking world after Coinhive discontinued its service. We aimed to see whether and how attackers continue cryptojacking, generate new malicious scripts, and developed new methods. We used a capable cryptojacking detector named CMTracker that proposed by Hong et al. in 2018. We automatically and manually examined 2770 websites that had been detected by CMTracker before the Coinhive shutdown. The results revealed that 99% of sites no longer continue cryptojacking. 1% of websites still run 8 unique mining scripts. By tracking these mining scripts, we detected 632 unique cryptojacking websites. Moreover, open-source investigations (OSINT) demonstrated that attackers still use the same methods. Therefore, we listed the typical patterns of cryptojacking. We concluded that cryptojacking is not dead after the Coinhive shutdown. It is still alive, but not as attractive as it used to be.
2021-08-11
Lau, Pikkin, Wei, Wei, Wang, Lingfeng, Liu, Zhaoxi, Ten, Chee-Wooi.  2020.  A Cybersecurity Insurance Model for Power System Reliability Considering Optimal Defense Resource Allocation. IEEE Transactions on Smart Grid. 11:4403–4414.
With the increasing application of Information and Communication Technologies (ICTs), cyberattacks have become more prevalent against Cyber-Physical Systems (CPSs) such as the modern power grids. Various methods have been proposed to model the cybersecurity threats, but so far limited studies have been focused on the defensive strategies subject to the limited security budget. In this paper, the power supply reliability is evaluated considering the strategic allocation of defense resources. Specifically, the optimal mixed strategies are formulated by the Stackelberg Security Game (SSG) to allocate the defense resources on multiple targets subject to cyberattacks. The cyberattacks against the intrusion-tolerant Supervisory Control and Data Acquisition (SCADA) system are mathematically modeled by Semi-Markov Process (SMP) kernel. The intrusion tolerance capability of the SCADA system provides buffered residence time before the substation failure to enhance the network robustness against cyberattacks. Case studies of the cyberattack scenarios are carried out to demonstrate the intrusion tolerance capability. Depending on the defense resource allocation scheme, the intrusion-tolerant SCADA system possesses varying degrees of self-healing capability to restore to the good state and prevent the substations from failure. If more defense resources are invested on the substations, the intrusion tolerant capability can be further enhanced for protecting the substations. Finally, the actuarial insurance principle is designed to estimate transmission companies' individual premiums considering correlated cybersecurity risks. The proposed insurance premium principle is designed to provide incentive for investments on enhancing the intrusion tolerance capability, which is verified by the results of case studies.
2021-01-15
Rana, M. S., Sung, A. H..  2020.  DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection. 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :70—75.
Recent advances in technology have made the deep learning (DL) models available for use in a wide variety of novel applications; for example, generative adversarial network (GAN) models are capable of producing hyper-realistic images, speech, and even videos, such as the so-called “Deepfake” produced by GANs with manipulated audio and/or video clips, which are so realistic as to be indistinguishable from the real ones in human perception. Aside from innovative and legitimate applications, there are numerous nefarious or unlawful ways to use such counterfeit contents in propaganda, political campaigns, cybercrimes, extortion, etc. To meet the challenges posed by Deepfake multimedia, we propose a deep ensemble learning technique called DeepfakeStack for detecting such manipulated videos. The proposed technique combines a series of DL based state-of-art classification models and creates an improved composite classifier. Based on our experiments, it is shown that DeepfakeStack outperforms other classifiers by achieving an accuracy of 99.65% and AUROC of 1.0 score in detecting Deepfake. Therefore, our method provides a solid basis for building a Realtime Deepfake detector.
2021-02-16
Zhai, P., Song, Y., Zhu, X., Cao, L., Zhang, J., Yang, C..  2020.  Distributed Denial of Service Defense in Software Defined Network Using OpenFlow. 2020 IEEE/CIC International Conference on Communications in China (ICCC). :1274—1279.
Software Defined Network (SDN) is a new type of network architecture solution, and its innovation lies in decoupling traditional network system into a control plane, a data plane, and an application plane. It logically implements centralized control and management of the network, and SDN is considered to represent the development trend of the network in the future. However, SDN still faces many security challenges. Currently, the number of insecure devices is huge. Distributed Denial of Service (DDoS) attacks are one of the major network security threats.This paper focuses on the detection and mitigation of DDoS attacks in SDN. Firstly, we explore a solution to detect DDoS using Renyi entropy, and we use exponentially weighted moving average algorithm to set a dynamic threshold to adapt to changes of the network. Second, to mitigate this threat, we analyze the historical behavior of each source IP address and score it to determine the malicious source IP address, and use OpenFlow protocol to block attack source.The experimental results show that the scheme studied in this paper can effectively detect and mitigate DDoS attacks.
2021-09-16
Alshawi, Amany, Satam, Pratik, Almoualem, Firas, Hariri, Salim.  2020.  Effective Wireless Communication Architecture for Resisting Jamming Attacks. IEEE Access. 8:176691–176703.
Over time, the use of wireless technologies has significantly increased due to bandwidth improvements, cost-effectiveness, and ease of deployment. Owing to the ease of access to the communication medium, wireless communications and technologies are inherently vulnerable to attacks. These attacks include brute force attacks such as jamming attacks and those that target the communication protocol (Wi-Fi and Bluetooth protocols). Thus, there is a need to make wireless communication resilient and secure against attacks. Existing wireless protocols and applications have attempted to address the need to improve systems security as well as privacy. They have been highly effective in addressing privacy issues, but ineffective in addressing security threats like jamming and session hijacking attacks and other types of Denial of Service Attacks. In this article, we present an ``architecture for resilient wireless communications'' based on the concept of Moving Target Defense. To increase the difficulty of launching successful attacks and achieve resilient operation, we changed the runtime characteristics of wireless links, such as the modulation type, network address, packet size, and channel operating frequency. The architecture reduces the overhead resulting from changing channel configurations using two communication channels, in which one is used for communication, while the other acts as a standby channel. A prototype was built using Software Defined Radio to test the performance of the architecture. Experimental evaluations showed that the approach was resilient against jamming attacks. We also present a mathematical analysis to demonstrate the difficulty of performing a successful attack against our proposed architecture.
Conference Name: IEEE Access
2021-05-05
Lee, Jae-Myeong, Hong, Sugwon.  2020.  Host-Oriented Approach to Cyber Security for the SCADA Systems. 2020 6th IEEE Congress on Information Science and Technology (CiSt). :151—155.
Recent cyberattacks targeting Supervisory Control and Data Acquisition (SCADA)/Industrial Control System(ICS) exploit weaknesses of host system software environment and take over the control of host processes in the host of the station network. We analyze the attack path of these attacks, which features how the attack hijacks the host in the network and compromises the operations of field device controllers. The paper proposes a host-based protection method, which can prevent malware penetration into the process memory by code injection attacks. The method consists of two protection schemes. One is to prevent file-based code injection such as DLL injection. The other is to prevent fileless code injection. The method traces changes in memory regions and determine whether the newly allocated memory is written with malicious codes. For this method, we show how a machine learning method can be adopted.
2021-08-11
Xi, Bowei, Kamhoua, Charles A..  2020.  A Hypergame‐Based Defense Strategy Toward Cyber Deception in Internet of Battlefield Things (IoBT). Modeling and Design of Secure Internet of Things. :59–77.
In this chapter, we develop a defense strategy to secure Internet of Battlefield Things (IoBT) based on a hypergame employing deceptive techniques. The hypergame is played multiple rounds. At each round, the adversary updates its perception of the attack graph and chooses the next node to compromise. The defender updates its perceived list of compromised nodes and actively feeds false signals to the adversary to create deception. The hypergame developed in this chapter provides an important theoretical framework for us to model how a cyberattack spreads on a network and the interaction between the adversary and the defender. It also provides quantitative metrics such as the time it takes the adversary to explore the network and compromise the target nodes. Based on these metrics, the defender can reboot the network devices and reset the network topology in time to clean up all potentially compromised devices and to protect the critical nodes. The hypergame provides useful guidance on how to create cyber deceptions so that the adversary cannot obtain information about the correct network topology and can be deterred from reaching the target critical nodes on a military network while it is in service.
2021-02-23
Liu, J., Xiao, K., Luo, L., Li, Y., Chen, L..  2020.  An intrusion detection system integrating network-level intrusion detection and host-level intrusion detection. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). :122—129.
With the rapid development of Internet, the issue of cyber security has increasingly gained more attention. An intrusion Detection System (IDS) is an effective technique to defend cyber-attacks and reduce security losses. However, the challenge of IDS lies in the diversity of cyber-attackers and the frequently-changing data requiring a flexible and efficient solution. To address this problem, machine learning approaches are being applied in the IDS field. In this paper, we propose an efficient scalable neural-network-based hybrid IDS framework with the combination of Host-level IDS (HIDS) and Network-level IDS (NIDS). We applied the autoencoders (AE) to NIDS and designed HIDS using word embedding and convolutional neural network. To evaluate the IDS, many experiments are performed on the public datasets NSL-KDD and ADFA. It can detect many attacks and reduce the security risk with high efficiency and excellent scalability.
2021-02-16
Lotfalizadeh, H., Kim, D. S..  2020.  Investigating Real-Time Entropy Features of DDoS Attack Based on Categorized Partial-Flows. 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM). :1—6.
With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect initiation and termination of DDoS attacks are investigated. The method to extract these features is devised with respect to some openflowbased switch capabilities. These features provide us with a higher resolution to view and process packet count entropies, thus improving DDoS attack detection capabilities. Although some of the technical assumptions are based on SDN technology and openflow protocol, the methodology can be applied in other networking paradigms as well.
2020-12-14
Kyaw, A. T., Oo, M. Zin, Khin, C. S..  2020.  Machine-Learning Based DDOS Attack Classifier in Software Defined Network. 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). :431–434.
Due to centralized control and programmable capability of the SDN architecture, network administrators can easily manage and control the whole network through the centralized controller. According to the SDN architecture, the SDN controller is vulnerable to distributed denial of service (DDOS) attacks. Thus, a failure of SDN controller is a major leak for security concern. The objectives of paper is therefore to detect the DDOS attacks and classify the normal or attack traffic in SDN network using machine learning algorithms. In this proposed system, polynomial SVM is applied to compare to existing linear SVM by using scapy, which is packet generation tool and RYU SDN controller. According to the experimental result, polynomial SVM achieves 3% better accuracy and 34% lower false alarm rate compared to Linear SVM.
2021-05-03
Raj A.G.R., Rahul, Sunitha, R., Prasad, H.B..  2020.  Mitigating DDoS Flooding Attacks with Dynamic Path Identifiers in Wireless Network. 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). :869–874.
The usage of wireless devices is increased from last decade due to its reliable, fast and easy transfer of data. Ensuring the security to these networks is a crucial thing. There are several types of network attacks, in this paper, DDoS attacks on networks and techniques, consequences, effects and prevention methods are focused on. The DDoS attack is carried out by multiple attackers on a system which floods the system with a greater number of incoming requests to the system. The destination system cannot immediately respond to the huge requests, due to this server crashes or halts. To detect, or to avoid such scenarios Intrusion prevention system is designed. The IPS block the network attacker at its first hop and thus reduce the malicious traffic near its source. Intrusion detection system prevents the attack without the prior knowledge of the attacker. The attack is detected at the router side and path is changed to transfer the files. The proposed model is designed to obtain the dynamic path for efficient transmission in wireless neworks.
2021-03-17
Lee, Y., Woo, S., Song, Y., Lee, J., Lee, D. H..  2020.  Practical Vulnerability-Information-Sharing Architecture for Automotive Security-Risk Analysis. IEEE Access. 8:120009—120018.
Emerging trends that are shaping the future of the automotive industry include electrification, autonomous driving, sharing, and connectivity, and these trends keep changing annually. Thus, the automotive industry is shifting from mechanical devices to electronic control devices, and is not moving to Internet of Things devices connected to 5G networks. Owing to the convergence of automobile-information and communication technology (ICT), the safety and convenience features of automobiles have improved significantly. However, cyberattacks that occur in the existing ICT environment and can occur in the upcoming 5G network are being replicated in the automobile environment. In a hyper-connected society where 5G networks are commercially available, automotive security is extremely important, as vehicles become the center of vehicle to everything (V2X) communication connected to everything around them. Designing, developing, and deploying information security techniques for vehicles require a systematic security-risk-assessment and management process throughout the vehicle's lifecycle. To do this, a security risk analysis (SRA) must be performed, which requires an analysis of cyber threats on automotive vehicles. In this study, we introduce a cyber kill chain-based cyberattack analysis method to create a formal vulnerability-analysis system. We can also analyze car-hacking studies that were conducted on real cars to identify the characteristics of the attack stages of existing car-hacking techniques and propose the minimum but essential measures for defense. Finally, we propose an automotive common-vulnerabilities-and-exposure system to manage and share evolving vehicle-related cyberattacks, threats, and vulnerabilities.
Bajpai, P., Enbody, R..  2020.  Attacking Key Management in Ransomware. IT Professional. 22:21—27.

Ransomware have observed a steady growth over the years with several concerning trends that indicate efficient, targeted attacks against organizations and individuals alike. These opportunistic attackers indiscriminately target both public and private sector entities to maximize gain. In this article, we highlight the criticality of key management in ransomware's cryptosystem in order to facilitate building effective solutions against this threat. We introduce the ransomware kill chain to elucidate the path our adversaries must take to attain their malicious objective. We examine current solutions presented against ransomware in light of this kill chain and specify which constraints on ransomware are being violated by the existing solutions. Finally, we present the notion of memory attacks against ransomware's key management and present our initial experiments with dynamically extracting decryption keys from real-world ransomware. Results of our preliminary research are promising and the extracted keys were successfully deployed in subsequent data decryption.

2021-02-16
Sumantra, I., Gandhi, S. Indira.  2020.  DDoS attack Detection and Mitigation in Software Defined Networks. 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). :1—5.
This work aims to formulate an effective scheme which can detect and mitigate of Distributed Denial of Service (DDoS) attack in Software Defined Networks. Distributed Denial of Service attacks are one of the most destructive attacks in the internet. Whenever you heard of a website being hacked, it would have probably been a victim of a DDoS attack. A DDoS attack is aimed at disrupting the normal operation of a system by making service and resources unavailable to legitimate users by overloading the system with excessive superfluous traffic from distributed source. These distributed set of compromised hosts that performs the attack are referred as Botnet. Software Defined Networking being an emerging technology, offers a solution to reduce network management complexity. It separates the Control plane and the data plane. This decoupling provides centralized control of the network with programmability and flexibility. This work harness this programming ability and centralized control of SDN to obtain the randomness of the network flow data. This statistical approach utilizes the source IP in the network and various attributes of TCP flags and calculates entropy from them. The proposed technique can detect volume based and application based DDoS attacks like TCP SYN flood, Ping flood and Slow HTTP attacks. The methodology is evaluated through emulation using Mininet and Detection and mitigation strategies are implemented in POX controller. The experimental results show the proposed method have improved performance evaluation parameters including the Attack detection time, Delay to serve a legitimate request in the presence of attacker and overall CPU utilization.
2021-03-04
Moustafa, N., Keshky, M., Debiez, E., Janicke, H..  2020.  Federated TONİoT Windows Datasets for Evaluating AI-Based Security Applications. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :848—855.

Existing cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intelligence (AI), especially Deep Learning (DL) algorithms, those security solutions have been plugged-in with AI models to discover, trace, mitigate or respond to incidents of new security events. The algorithms demand a large number of heterogeneous data sources to train and validate new security systems. This paper presents the description of new datasets, the so-called ToNİoT, which involve federated data sources collected from Telemetry datasets of IoT services, Operating system datasets of Windows and Linux, and datasets of Network traffic. The paper introduces the testbed and description of TONİoT datasets for Windows operating systems. The testbed was implemented in three layers: edge, fog and cloud. The edge layer involves IoT and network devices, the fog layer contains virtual machines and gateways, and the cloud layer involves cloud services, such as data analytics, linked to the other two layers. These layers were dynamically managed using the platforms of software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Windows datasets were collected from audit traces of memories, processors, networks, processes and hard disks. The datasets would be used to evaluate various AI-based cyber security solutions, including intrusion detection, threat intelligence and hunting, privacy preservation and digital forensics. This is because the datasets have a wide range of recent normal and attack features and observations, as well as authentic ground truth events. The datasets can be publicly accessed from this link [1].

2021-02-03
Aliman, N.-M., Kester, L..  2020.  Malicious Design in AIVR, Falsehood and Cybersecurity-oriented Immersive Defenses. 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). :130—137.

Advancements in the AI field unfold tremendous opportunities for society. Simultaneously, it becomes increasingly important to address emerging ramifications. Thereby, the focus is often set on ethical and safe design forestalling unintentional failures. However, cybersecurity-oriented approaches to AI safety additionally consider instantiations of intentional malice – including unethical malevolent AI design. Recently, an analogous emphasis on malicious actors has been expressed regarding security and safety for virtual reality (VR). In this vein, while the intersection of AI and VR (AIVR) offers a wide array of beneficial cross-fertilization possibilities, it is responsible to anticipate future malicious AIVR design from the onset on given the potential socio-psycho-technological impacts. For a simplified illustration, this paper analyzes the conceivable use case of Generative AI (here deepfake techniques) utilized for disinformation in immersive journalism. In our view, defenses against such future AIVR safety risks related to falsehood in immersive settings should be transdisciplinarily conceived from an immersive co-creation stance. As a first step, we motivate a cybersecurity-oriented procedure to generate defenses via immersive design fictions. Overall, there may be no panacea but updatable transdisciplinary tools including AIVR itself could be used to incrementally defend against malicious actors in AIVR.

2020-12-28
Zhang, C., Shahriar, H., Riad, A. B. M. K..  2020.  Security and Privacy Analysis of Wearable Health Device. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :1767—1772.

Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.

2021-03-30
Shah, P. R., Agarwal, A..  2020.  Cybersecurity Behaviour of Smartphone Users Through the Lens of Fogg Behaviour Model. 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA). :79—82.

It is now a fact that human is the weakest link in the cybersecurity chain. Many theories from behavioural science like the theory of planned behaviour and protection motivation theory have been used to investigate the factors that affect the cybersecurity behaviour and practices of the end-user. In this paper, the researchers have used Fogg behaviour model (FBM) to study factors affecting the cybersecurity behaviour and practices of smartphone users. This study found that the odds of secure behaviour and practices by respondents with high motivation and high ability were 4.64 times more than the respondents with low motivation and low ability. This study describes how FBM may be used in the design and development of cybersecurity awareness program leading to a behaviour change.

2021-08-12
Karie, Nickson M., Sahri, Nor Masri, Haskell-Dowland, Paul.  2020.  IoT Threat Detection Advances, Challenges and Future Directions. 2020 Workshop on Emerging Technologies for Security in IoT (ETSecIoT). :22—29.
It is predicted that, the number of connected Internet of Things (IoT) devices will rise to 38.6 billion by 2025 and an estimated 50 billion by 2030. The increased deployment of IoT devices into diverse areas of our life has provided us with significant benefits such as improved quality of life and task automation. However, each time a new IoT device is deployed, new and unique security threats emerge or are introduced into the environment under which the device must operate. Instantaneous detection and mitigation of every security threat introduced by different IoT devices deployed can be very challenging. This is because many of the IoT devices are manufactured with no consideration of their security implications. In this paper therefore, we review existing literature and present IoT threat detection research advances with a focus on the various IoT security challenges as well as the current developments towards combating cyber security threats in IoT networks. However, this paper also highlights several future research directions in the IoT domain.
2021-02-03
Ceron, J. M., Scholten, C., Pras, A., Santanna, J..  2020.  MikroTik Devices Landscape, Realistic Honeypots, and Automated Attack Classification. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1—9.

In 2018, several malware campaigns targeted and succeed to infect millions of low-cost routers (malwares e.g., VPN-Filter, Navidade, and SonarDNS). These routers were used, then, for all sort of cybercrimes: from DDoS attacks to ransomware. MikroTik routers are a peculiar example of low-cost routers. These routers are used to provide both last mile access to home users and are used in core network infrastructure. Half of the core routers used in one of the biggest Internet exchanges in the world are MikroTik devices. The problem is that vulnerable firmwares (RouterOS) used in homeusers houses are also used in core networks. In this paper, we are the first to quantify the problem that infecting MikroTik devices would pose to the Internet. Based on more than 4 TB of data, we reveal more than 4 million MikroTik devices in the world. Then, we propose an easy-to-deploy MikroTik honeypot and collect more than 17 millions packets, in 45 days, from sensors deployed in Australia, Brazil, China, India, Netherlands, and the United States. Finally, we use the collected data from our honeypots to automatically classify and assess attacks tailored to MikroTik devices. All our source-codes and analysis are publicly available. We believe that our honeypots and our findings in this paper foster security improvements in MikroTik devices worldwide.