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Network Threats
Published in Nicholas Kolokotronis, Stavros Shiaeles, Cyber-Security Threats, Actors, and Dynamic Mitigation, 2021
Panagiotis Radoglou Grammatikis, Panagiotis Sarigiannidis
Session hijacking is a term that can be used for describing many attacks. In general, any attack aiming to exploit a particular session between two devices is called session hijacking. This section focuses mainly on HTTP session hijacking; however, similarly, this method can be performed with other protocols. In particular, session hijacking refers to the malicious activities that allow a potential attacker to impersonate a party of a session by sniffing the network traffic behind it. Focusing on HTTP, when a client enters with his/her credentials a website, an HTTP session is created between the user and web server. Typically, the web servers utilize a cookie in order to track the session and check that they are active and the client has still the permissions to access specific resources. When the cookie expires, the session is terminated, and the credentials are cleared. Therefore, in this case, a potential attacker could capture the cookie of a session and sent it to the web server, thus imitating the one endpoint of the session.
A systematic classification scheme for cyber-attack taxonomy
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
S. Kim, J. Shin, G. Heo, J.G. Song
Foot printing & scanning is the preliminary task of gathering information about the system to be attacked. Password cracking is the attack that extracts passwords through various methods and means, and can gain full access rights to the system through password cracking. Spoofing is the word meaning cheat, which allows spoofing on any connection that exists on the Internet or locally. Sniffing is an act of peeping packets exchanged by other parties on the network, similar to the dictionary meaning of ‘sniff’. Session hijacking is a cyberattack technique that steals and accesses another person’s session state. MITM is the way of intercepting and exchanging information between two parties communicating with each other. A computer virus is a type of malicious software program that can replicate itself by modifying other computer programs and inserting its own code.
Security of IoT in Healthcare
Published in Naveen Chilamkurti, T. Poongodi, Balamurugan Balusamy, Blockchain, Internet of Things, and Artificial Intelligence, 2021
Supriya Khaitan, Rashi Agarwal, T. Poongodi, R. Indrakumari, A. Ilavendhan
Session hijacking is an attack commonly referred to as ‘tampering with’ and ‘exploitation of’ a legitimate communication session (known as a session key) to obtain the unauthorized access on the available resources or services of a particular system. More specifically, the session hijacking of a set of TCP messages significantly affects and creates more trouble in the IoT network.
Cybersecurity for children: an investigation into the application of social media
Published in Enterprise Information Systems, 2023
Victor Chang, Lewis Golightly, Qianwen Ariel Xu, Thanaporn Boonmee, Ben S. Liu
A popular method of hacking is a session hijacking attack, which enables the hacker to take over the victim’s network session(s). Such a network session is typically on the victim’s social media account, giving the hacker access to the victim’s social media account without even needing to know the login credentials. However, there are prevention techniques to mitigate this kind of attack, which can be found in Cashion and Bassiouni (Cashion and Bassiouni 2011).
Optimising technological literacy acquirement to protect privacy and security
Published in Behaviour & Information Technology, 2022
Ron S. Hirschprung, Shay Tayro, Eti Reznik
Privacy and security threats in the cyberspace world can be classified as follows: (a) Social engineering: attacks that are based on psychological manipulations to divert people from their interest (Hatfield 2018). For example, a user may be tempted to enter his bank account user ID and password in a site redirected from a mail (an attack known as phishing, and considered the oldest and simplest, yet prevalent) in an attempt to steal information (Parulekar 2019); (b) Data collection: the digital era is characterised by an enormous amount of technological devices that systematically collect and store sensitive data. For example, surveillance cameras may document places we have been to and actions we have taken, information that can be de-anonymised by using facial recognition algorithms (Wilkinson 2020); (c) Security breaches: usually ‘holes’ in the security layout which were not considered by their designers. For example, installing an access point on a Wi-Fi secured network, known as ‘rough access point’ (Jang et al. 2019); (d) Big Data: today, a massive amount of data is stored, and considering the availability of high computational power, sensitive information can be mined. For example, anonymised data release can be de-anonymised by crossing it with auxiliary data sources (Salas and Domingo-Ferrer 2018); and (e) Naivety: in many cases, users ‘donate’ their precious sensitive data due to naivety. For example, data published in online social networks can be unknowingly disclosed to third parties for profiling purposes (Bhagat et al. 2018). Notably, a threat to privacy and security can be a combination of two or more of the above classes. For example, XSS reflected attack is mainly based on social engineering aimed at tempting a user to browse an unsafe webpage, and a security breach in the trusted website server by exploiting a vulnerability of ignoring an injected code. This may result in session-hijacking, cookie-stealing, malicious redirection and malware-spreading (Shrivastava, Choudhary, and Kumar 2016).
An Automated Detection of DDoS Attack in Cloud Using Optimized Weighted Fused Features and Hybrid DBN-GRU Architecture
Published in Cybernetics and Systems, 2022
Ahamed Ali Samsu Aliar, Moorthy Agoramoorthy, Justindhas Y.
In recent days, the rapid growth in cloud computing is very useful in all business sectors throughout the world. It gives reliable, scalable, and on-demand computing resources for both organizational and individual users. The cloud environment contains three types of environment that are Infrastructure as a Service (IaaS) is the first type, Platform as a Service (PaaS) is the second type, and Software as a Service (SaaS) (Bhushan and Gupta 2018) is the third type. The important features to be provided by the cloud environment are elastic, resource pooling, on-demand service, self-service, and measured services for the fully managed system resources and services. These essential features in cloud computing are well as suitable for the applications like retail, healthcare, manufacturing, entertainment, etc (Bhushan and Gupta 2019). The clouds environments are highly affected by distinct security threats such as malicious attacks, DoS attacks, and general attacks. The security issues that arise due to the presence of the cloud environment are data shared technology vulnerabilities, loss or leakage, hesitant Application Programming Interfaces (APIs), malicious attacks, common attacks, abuse, and nefarious use of cloud computing (Somani et al. 2018), unknown risk profile, and account or service hijacking. The identity of appropriate users is steeled by security attacks over cyber espionage, session hijacking, phishing, and software vulnerabilities (Harikrishna and Amuthan 2021). Most commonly, the cloud environment is highly affected by Distributed DDoS attacks, and that has aroused many times due to the simple attack. Hence, it is moderated to identify whether it is the simplest attack or a DDoS attack, and the removal of these attacks is a challenging task when it is presented with simple attacks (Choi et al. 2014). Generally, DDoS attacks are distributed and coordinated attacks by the availability of the host server and the network resource constraints in the network. The detection of DDoS attacks is the most significant task in cloud computing because by connecting huge amounts of devices in the network, it is difficult to preserve the data from hackers, and this produces heavy congestion in the network (Gupta and Badve 2017). In addition, DDoS attacks are highly volumetric, and unreachable, and they can affect the host server in the network.