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Latest Research Topics in Cybersecurity

Cybersecurity Research Topics

Latest Research Topics in Cybersecurity

Cybersecurity aims at protecting the internet-connected computers, servers, mobile devices, and electronic systems, including software and data from Cyberthreats. To ensure Cybersecurity, the coordination of application security, information security, network security, business continuity plan, operational security, and end-user education is required. Application security protects the applications from external threats using software, hardware, and procedural security methods. Information security is a subset of Cybersecurity, and it maintains data confidentiality, integrity, and availability. The business continuity plan is the step-by-step plan to minimize the effects of a disaster and to resume the mission-critical functions. Cyber threats are broadly categorized as cybercrimes, cyber attacks, and cyber-terrorism.
Cyber Attacks and Countermeasures: Technology is growing rapidly and enforces online data storage on servers. Evolving technologies open up doors for cybercrimes. Hackers are continually developing new attacks to deface online systems. To avoid such cyber attacks, different cyber security solutions have been developed to protect organizations against cyber attacks. These are critical infrastructure security, application security, network security, cloud security, and Internet of Things (IoT) security.
Cryptography for Cyber Security Applications: Cryptography assists in securely maintaining sensitive data, such as private passwords, PINs, and private information online. Cybersecurity explores cryptography algorithms to design security methods, ciphers, and other security measures to protect organization and customer data.
Vulnerability Discovery Techniques: Vulnerability discovery techniques attempt to discover software vulnerabilities. The large size of codebases makes it challenging for software vulnerability discovery. To ease this difficulty, many different types of techniques have been suggested, such as systematic and exploratory manual penetration testing, static analysis, and automated penetration testing.
Cyber Security for Cloud-based Systems and Applications: Recently, cloud-based systems have allowed organizations to expand and grow their business. However, they may not offer sufficient protection over online data. It necessitates the cyber security implementation in cloud systems.
Cyber Security for Wireless Networks and Applications: The basic principle of network security is to protect data in network layers, and such cyber security solutions verify the bedding of rules and regulations before performing any communication activities on the data.
Cyber Security for IoT-based Smart Applications: Since an IoT includes millions of smart devices, it increases the cyber-attack vulnerabilities. Once a single device is compromised, an entire network is down. It makes cyber-security a critical factor in IoT-based smart networks.
Cyber Security for Cyber-Physical Systems: The Cyber-Physical Systems (CPS) includes many distinct sectors, such as IoT, smart city, and so on. The Cyber security for CPS addresses security concerns for CPS and internet of things devices. There are various types of attacks, such as insider, spoofing, and ransomware attacks.
Cyber Security for Critical Infrastructure: Several security schemes have been developed for infrastructures. Currently, most of the protection programs only address physical threats, but not the cyber threats ranging from service disruption to public safety concerns.
Cybersecurity Data Analytics: Data analytics tools are more widely adopted with Big data applications, increasing security vulnerability. Thus, cybersecurity analytics is seen as a more advanced field to provide cyber-security for data. Recently, Cyber security techniques have to identify the behavior of new attacks instead of monitoring the data only for known attacks.
Machine Learning and Deep Learning for Cyber Security: Machine and deep learning algorithms play a vital role in cyber security. Most cyber ML and DL applications are intrusion detection systems, malware analysis, and spam detection. While applying the learning models for cyber security, significant improvements are needed to avoid continuous re-training and appropriate parameter tuning.
Cyber Security for Software Systems: Cyber security protects systems or software connected to the internet from different attackers over cyberspace. It prevents cybercriminals from accessing the data from the software. The security provisioning to software follows different ways, such as firewall, Antivirus, and spyware detection.
Malware Analysis: Malware analysis is the process of analyzing the behavior and identifying the suspicious file or URL accurately. The output of the malware analysis is the detection and mitigation of the potential threat. There are static and dynamic malware analysis techniques presented. The former examines the malware piece without running the program, whereas the last one carries the malware analysis in a controlled environment.
Intrusion Detection Systems: An Intrusion Detection System (IDS) is a monitoring system that helps identify suspicious activities and inform others by creating alerts when they are detected. There are anomaly and signature-based intrusion detection systems available. The signature-based IDS identifies the attackers based on specific attack patterns. The anomaly-based IDS helps in identifying the unknown attacks.
Internet Security Threats and Defense Mechanisms: Security plays a vital role in internet-based applications. Recently, mobiles/computers have faced a surplus amount of security challenges. Thus, internet security deals with preventing unauthorized access, misuse, and denial of service issues. The term internet security covers confidentiality, integrity, and availability.
Behavioral Aspects of Cybersecurity: To utilize the advantages of cyber security completely, consumers need support to develop and adopt security schemes. A habit is an important factor in accepting and motivating cyber security behaviors. Thus, it is essential to analyze cybersecurity behavior, such as the intention to comply with security policies, to take security actions, to use protective technologies, and so on.
Cybersecurity Modeling and Simulation: The simulation tools mimic the complex interrelation between the consumers, social, software, and cyber security schemes and examine the complex interactions. In modeling and simulation, the threats are modeled to identify the impact of attacks and the efficiency of cyber security schemes before implementing them in real-time.
Cyber security on Intelligent Transportation Systems: Intelligent transport systems utilize to control and information systems. Integrated communications and data processing technologies help improve the mobility of people and vehicles without affecting their safety.
Adversarial Attacks on Machine Learning Algorithms: Machine learning algorithms play an important role in data analytics. It is widely applied from environmental monitoring to healthcare emergency services. Thus, the reliability of machine learning models is crucial. The most common attacks on machine learning models are Black Box, white box, data poisoning, spoofing, and modification attacks. Adversarial Reasoning: Adversarial reasoning is a computational approach to inferring and anticipating an attackā€™s perceptions, intents, and actions. It is implemented with the assistance of game-theoretic and cognitive models.
Anonymity and Privacy Preservation: The anonymity defies the action of keeping the user identity private, but not their actions. For example, with the support of pseudonyms, users can share or post messages on social media. However, the term privacy defines the action of hiding some important data from others. For example, messaging friends individually on social media, and others cannot read such private messages.

CyberSecurity -Topics Coverage

Cyber Attacks and Countermeasures - Cryptography for Cyber Security Applications - Vulnerability Discovery Techniques - Cyber Security for Cloud-based Systems and Applications - Cyber Security for Wireless Networks and Applications - Cyber Security for IoT based Smart Applications - Cyber Security for Cyber-Physical Systems - Cyber Security for Critical Infrastructure - Cybersecurity Data Analytics - Machine Learning and Deep Learning for Cyber Security - Cyber Security for Software Systems - Malware Analysis - Intrusion Detection Systems - Internet Security Threats and Defense Mechanisms - Behavioral Aspects of Cybersecurity - Cybersecurity Modeling and Simulation - Cybersecurity on Intelligent Transportation Systems- Adversarial Attacks on Machine Learning Algorithms - Adversarial Reasoning- Anonymity and Privacy Preservation