proposed methodology for cyber criminal profiling

Proposed Methodology for Cyber Criminal Profiling

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• Meland P Nesheim D Bernsmed K Sindre G (2022) Assessing cyber threats for storyless systems Journal of Information Security and Applications 10.1016/j.jisa.2021.103050 64 :C Online publication date: 1-Feb-2022 https://dl.acm.org/doi/10.1016/j.jisa.2021.103050
• Haga K Meland P Sindre G (2020) Breaking the Cyber Kill Chain by Modelling Resource Costs Graphical Models for Security 10.1007/978-3-030-62230-5_6 (111-126) Online publication date: 22-Jun-2020 https://dl.acm.org/doi/10.1007/978-3-030-62230-5_6

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Proposed Methodology for Cyber Criminal Profiling

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Author: Warikoo

Source: Information Security Journal: A Global Perspective , Volume 23, Numbers 4-6, 4 July 2014, pp. 172-178(7)

Publisher: Taylor and Francis Ltd

DOI: https://doi.org/10.1080/19393555.2014.931491

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Keywords: cyber attacks ; cyber criminal profiling ; forensics ; profiling framework

Document Type: Research Article

Affiliations: Jersey City, New Jersey, USA

Publication date: July 4, 2014

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Profiling the Cybercriminal: A Systematic Review of Research

As cybercrime becomes one of the most significant threats facing society today, it is of utmost importance to better understand the perpetrators behind such attacks. In this article, we seek to advance research and practitioner understanding of the cybercriminal (cyber-offender) profiling domain by conducting a rigorous systematic review. This work investigates the aforementioned domain to answer the question: what is the state-of-the-art in the academic field of understanding, characterising and profiling cybercriminals. Through the application of the PRISMA systematic literature review technique, we identify 39 works from the last 14 years (2006-2020). Our findings demonstrate that overall, there is lack of a common definition of profiling for cyber-offenders. The review found that one of the primary types of cybercriminals that studies have focused on is hackers and the majority of papers used the deductive approach as a preferred one. This article produces an up-to-date characte...

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• DOI: 10.1109/HICSS.2008.460
• Corpus ID: 16823461

Towards a Methodology for Profiling Cyber Criminals

• Leonard Kwan , P. Ray , Greg Stephens
• Published in Proceedings of the 41st… 7 January 2008
• Computer Science, Law
• Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008)

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DOI: 10.1080/19393555.2014.931491

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Reinforcing Digital Forensics Through Intelligent Behavioural Evidence Analysis: Social Media Hate Speech Profiling

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• Barkhashree 8 &
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Cumulative boom in the use of social media for crime incidents creates a need to achieve improvement in overall efficiency by developing tools and techniques. The present manuscript discusses this issue by proposing a model which offer a feasible solution to escalate the criminal investigation by performing digital criminal profiling using social media hate speeches content. The proposed expert system will consider the hate speech content analysis along with other digital footprints of the suspects. The achieved analysis along with the proofs collected manually will be fed into the knowledge base of expert system. The system will automatically process the dataset to lower down the list of suspects using intelligent machine learning algorithms. Hate speech content analysis achieved using latest intelligent mechanisms, can perform in the boundaries of Behavioural Evidence Analysis-Standardised (BEA-S) model to create criminal profiling which again act as a base for future investigations. The whole concept and the model are discussed in the paper which surely will upgrade the current investigation process to an innovative stature of digital forensics.

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Barkhashree, Dhaliwal, P. (2022). Reinforcing Digital Forensics Through Intelligent Behavioural Evidence Analysis: Social Media Hate Speech Profiling. In: Dev, A., Agrawal, S.S., Sharma, A. (eds) Artificial Intelligence and Speech Technology. AIST 2021. Communications in Computer and Information Science, vol 1546. Springer, Cham. https://doi.org/10.1007/978-3-030-95711-7_52

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A comprehensive methodology for profiling cyber-criminals

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Slacpss: secure lightweight authentication for cyber–physical–social systems.

1. Introduction

Click here to enlarge figure

2. Metaverse and Security Threats

2.1. metaverse architecture, 2.1.1. human society, 2.1.2. physical infrastructures, 2.1.3. interconnection of virtual worlds, 2.1.4. metaverse engine, 2.1.5. in-world information flow.

• In the physical world, sensing and control infrastructure driven by IoT technologies is a key element in the digital transformation of the physical world, utilizing pervasive sensors and actuators. The resulting massive data from IoT devices are transmitted and processed through network and computing infrastructures.
• Within the digital realm, the metaverse engine efficiently processes and organizes digital information gathered from both the physical and human spheres, enabling the construction and presentation of vast metaverses while offering a selection of metaverse services.

2.1.6. Information Flow across Worlds

• By employing human–computer interface (HCI) technologies, humans can engage with physical objects, and XR technologies allow them to immerse themselves in virtually augmented reality, such as holographic telepresence.
• The connection between the human world and the digital world is facilitated by the Internet, which is the largest computer network globally. Through smart devices like smartphones, wearable sensors, and VR helmets, users can engage with the digital realm for purposes such as knowledge creation, sharing, and acquisition.
• The interconnection of smart devices in the IoT infrastructure facilitates the seamless exchange of information between the physical and digital realms, enabling effective digitalization [ 26 ].

2.2. Security Threats to the Metaverse

2.2.1. threats to authentication in the metaverse.

• Identity Theft: If a user’s identity is stolen in the metaverse, their avatars, digital belongings, social connections, and digital life may be at risk of being exposed and lost, posing a more significant threat than in traditional information systems.
• Impersonation Attack: Within the metaverse, an impersonation attack can be executed when an attacker pretends to be an authorized entity, allowing the attacker to gain access to services or systems without proper authorization [ 28 ].
• Avatar Authentication Issue: Verifying the authenticity of avatars, such as confirming friends’ avatars, presents a more complex task in the metaverse than real-world identity authentication. This complexity arises from the necessity to validate facial features, voice, video recordings, and similar aspects.
• Trusted and Interoperable Authentication: To achieve the security, efficiency, and reliability of diverse service domains and virtual worlds in the metaverse, users and avatars must promptly establish a robust cross-platform and cross-domain identity verification system. This system should be able to operate seamlessly across various platforms, including blockchains.

2.2.2. Threats to Data Management in the Metaverse

• Data Tampering Attacks: Integrity characteristics play a crucial role in ensuring the efficient monitoring and identification of changes that occur during data exchange across ternary worlds and diverse sub-metaverses. Adversaries can manipulate, counterfeit, substitute, and eliminate unprocessed data throughout the metaverse data services’ lifecycle to disrupt the normal activities of users, avatars, or physical entities [ 29 ].
• False Data Injection Attacks: Attackers can inject falsified information, including false messages and incorrect instructions, to mislead metaverse systems [ 30 ]. For example, the use of AI-aided content creation can contribute to an enhanced user experience during the initial phase of the metaverse. However, adversaries can exploit this by injecting adversary training samples or poisoned gradients into centralized or distributed AI training, thereby generating biased AI models.
• Issues in Managing New Types of Metaverse Data: When examining the metaverse in relation to the existing Internet, it becomes evident that new hardware and devices are necessary to collect diverse forms of data (e.g., eye movement, facial expression, and head movement) that were previously uncollected. Data collection is vital for enabling fully immersive user experiences [ 31 ].
• Threats to the Data Quality of User-Generated Content (UGC) and Physical Input: In the metaverse, self-centered users or avatars might upload low-quality content in UGC mode to reduce expenses, consequently impacting user experience by creating an artificial environment.
• Threats to User-Generated Content (UGC) Ownership and Provenance: Contrary to the government’s regulated asset registration process in the physical realm, the metaverse exists as an open and fully autonomous domain with no centralized authority in place.
• Threats to Intellectual Property Protection: In contrast to the real world, the definition of intellectual property in the metaverse should be modified to establish clear licensing boundaries and usage rights for owners as the metaverse expands [ 32 ].

2.2.3. Privacy Threats in the Metaverse

• Pervasive Data Collection: For a truly immersive experience with an avatar, it is essential to conduct comprehensive user profiling at an exceedingly granular level [ 33 ], which includes facial expressions, eye and hand movements, speech patterns, biometric features, and even brainwave patterns.
• Privacy Leakage in Data Transmission: Personally identifiable information obtained from wearables such as Head-Mounted Displays (HMDs) is extensively gathered in metaverse systems and then transmitted through wired and wireless means, with strict measures in place to safeguard the confidentiality of these data from unauthorized parties [ 34 ].
• Privacy Leakage in Data Processing: Metaverse services rely on the collection and analysis of large amounts of data from people and their environments to develop avatars and virtual settings, posing a risk of sensitive information exposure [ 35 ].
• Privacy Leakage in Cloud/Edge Storage: Storing private and sensitive information, such as user profiling, for a significant number of users on cloud servers or edge devices can give rise to privacy disclosure concerns. Hackers can potentially deduce users’ privacy information by leveraging frequent queries through differential attacks [ 36 ].
• Rogue or Compromised End Devices: In the metaverse, an increased number of wearable sensors will be utilized on human bodies and their surroundings to enable avatars to establish natural eye contact, interpret hand gestures, mirror facial expressions, and more in real-time.
• Threats to Digital Footprints: Avatars in the metaverse can exhibit behavior patterns, preferences, habits, and activities that mirror those of their physical counterparts, enabling attackers to gather digital footprints and exploit the similarity to real users for accurate user profiling and potentially illegal activities [ 37 ].
• Identity Linkability in Ternary Worlds: As the metaverse incorporates reality within itself, the seamless integration of the human, physical, and virtual worlds gives rise to concerns regarding identity linkability across these ternary realms [ 32 ].
• Threats to Accountability: XR and HCI devices inherently capture a higher degree of sensitive data, such as user locations, behavior patterns, and surroundings, than traditional smart devices.
• Threats to Customized Privacy: Different users within specific sub-metaverses [ 38 ] tend to have unique privacy preferences for various services or interaction objects, similar to what is seen on other service platforms on the Internet.

2.2.4. Threats to Metaverse Network

2.2.5. threats to the metaverse economy.

• Service Trust Issues in UGC and Virtual Object Trading: Avatars in the open metaverse marketplace can be considered untrustworthy entities due to the absence of prior interactions. This poses inherent risks of fraud, such as repudiation and refusal to pay, during user-generated content and virtual object trading among various stakeholders in the metaverse. Additionally, when constructing virtual objects using digital twin technologies, the metaverse must ensure the authenticity and trustworthiness of the produced and deployed digital copies [ 39 ].
• Threats to Digital Asset Ownership: The distributed metaverse system, lacking a central authority and featuring intricate circulation and ownership structures such as collective ownership and shared ownership [ 40 ], poses substantial challenges in the lifecycle of digital assets within the creator economy. These challenges encompass the generation, pricing, trusted trading, and ownership traceability of such assets.

2.2.6. Threats to the Physical World and Human Society

• Threats to Personal Safety: In the metaverse, hackers can exploit wearable devices, XR helmets, and indoor sensors like cameras to gather information on users’ daily routines and monitor their live locations, ultimately aiding in criminal activities such as burglary and endangering their safety [ 42 ].
• Threats to Infrastructure Safety: The identification of software or system vulnerabilities within the complex metaverse allows hackers to use compromised devices as entry points for launching APT attacks on critical national infrastructures like power grid systems and high-speed rail systems.
• Social Effects: Despite the appeal of the metaverse as a digital society, it can lead to severe side effects in human society, such as addiction, the spread of rumors, child exploitation, biased outcomes, extortion, cyberbullying, cyberstalking, and even simulated terrorist activities [ 43 ].

2.2.7. Threats to Metaverse Governance

3. related works, 4. preliminaries, 4.1. elliptic curve cryptography (ecc), 4.2. biohashing.

• A vector, V € R n , is used to represent a biometric characteristic derived from the fingerprint.
• Using the Blum–Blum–Shub method, a set of r i € Rn (i = 1···n) pseudo-random numbers is generated.
• The Gram–Schmidt procedure is used to change the basis r i into an or i € Rn (i = 1, ···n) using generated pseudo-random numbers.
• The inner product between V and ori is obtained, followed by the calculation for the biohash code bi:

5. Proposed Secure Lightweight Authentication for Cyber–Physical–Social System (SLACPSS)

5.1. system model.

• Certificate authority (CA): The certificate authority is a completely reliable entity that sets the initial system parameters and shares public information. The certificate authority obtains the user’s pseudo-identity, public key, and personal details from the user, which will be used to verify the user’s identity and are then stored in the database. Moreover, the certificate authority generates user credentials that need to be authenticated by the user and the platform servers. These credentials are then delivered to the user.
• User: The user submits their pseudo-identity, public key, and personal details to the certificate authority for verification of their identity in order to join the metaverse. Subsequently, the user can interact with different platform servers by undergoing an authentication procedure that relies on the user’s pseudo-identity and credential information. Following that, the user can design an avatar and enter different virtual environments overseen by the platform servers. Moreover, the user can verify their identity with other avatars by utilizing the pseudo-identity and public key saved in the database, ensuring secure interactions between avatars in virtual spaces.
• Platform Server: Each platform server offers a range of immersive services, including education and gaming, to users within virtual spaces. When a user tries to log into the platform server, their credentials and pseudo-identity are verified using the database and the public key of the certificate authority.
• The certificate authority requires users to share their pseudo-identity, public key, and personal information to verify their identity and grant access credentials for interacting with metaverse environments.
• On each platform server, an avatar can be created using the user’s pseudo-identity, public key, and credential information. The user then sends an authentication message to the suitable platform server to gain access to the pertinent virtual spaces.
• If the authentication process goes well, the platform server will provide the user with a session key.
• The session key will then be used to establish a secure connection between the user and the platform server.
• A user can communicate with other avatars after entering a virtual environment using an avatar. The avatar authentication phase can be handled by the user for safe interactions between avatars.

5.1.1. Initialization Step

5.1.2. user setup step.

• ID i, PW i, and B i are entered by the Ui into SDi, which then makes a random integer, RN i, and a private key, ki. The public key (PK i = k i · P) and pseudo-identity (PID i = h (ID i || RN i)) are then calculated by Ui. Then, the Ui transmits the message {PID i, PK i, info i } to CA across a trust line, where info (i) is the Ui’s private information.
• CA verifies the information and examines the database’s (PID I; PK i) uniqueness. If this process is finished successfully, CA makes a random number, xi, and calculates X i = x i · p, Sig i_ca = x i + h (PID i || PK i || X i) · k ca, where Sig i_ca is the signature value used to verify that Ui has been approved by the CA. Afterward, the CA transmits V i = (X i, Sig i-ca) and saves (PID i, PK i) in the database.
• The CA transmits {Vi} to U i by the trusted line.
• U i calculates Z = Vi ⊕ h (ID i || PW i|| RN i) and then saves Z in SD.

5.1.3. Creating an Avatar

• To enter the virtual environment managed by the St, the Ui creates an avatar using SDi during the creating an avatar stage, and the avatar making process is shown in Table 4 , which is detailed below.
• ID i, PW i, and B i are entered by the U i into SDi and then they create and calculate a random number, RN I, and a private key (k i) and public key (PK i = k i · P). Then, Vi* = Z + h (IDi ||PWi||RNi) is calculated, and the user creates a random number (n i) and calculates Ni = ni · P and EM i = (N i||Sig i_ca) ⊕ h (avatar i||PID i||RN i).
• Through a secure channel, the Ui transmits {avatar i, PID i, EM i } to the St.
• The St retrieves PK i after checking PID i in the database, the St confirms the database’s uniqueness of (avatar i, PK i), and the St calculates (Ni ||Sig i_ca) = EMi ⊕ h (avatar i || PID i|| RN i) and Sig i_ca · P = x i · P + h (PID i ||PK i || X i) · PK ca.
• The St saves (avatar i, PK i) in the database and publishes (avatar i, PK i).

5.1.4. Login and Authentication Step

• ID i, PW i, and B i are entered by the U i into SDi, and then a random number (n 1 and T1) is created and calculated.
• The U i calculates N 1 = n 1 · P, Ver i - st = h (avatar i||PID i||T1) · K i, and EM 1 = (avatar i|| PID i|| Ver i - st) + h (N1||T1).
• The U i transmits {EM 1, T 1} to St by a public channel.
• Then, St receives {EM 1, T 1} from U i.
• T1 is checked by the condition |T1* − T1|.
• St calculates Ver i - st · P = ? h (avatar i|| PID i||T1) · PK i.
• St creates T2 and n 2 and calculates N 2 = n 2 ·P.
• SKi- st = h (avatar i||N2||T2) ·K st.
• St calculates EM 2 = h (avatar i||PID i||N 2||T 2) ⊕ (SKi- st||T 2).
• St transmits {EM 2, T 2} to U i by a public channel.
• As soon as the U i receives {EM 2, T 2} from St, T2 is checked by the condition |T2* − T2|, and the user calculates SKi- st · P = h (avatar i||N2||T2) · PK st and EM*2 = h (a vatar i||PID i || N2||T 2) ⊕ (SKi- st||T 2) and verifies EM*2 =? EM 2.

5.1.5. Avatar Authentication Step

• U i creates a random number (n 3 and T3) and calculates N 3 = n 3 · P.
• Ui calculates Ver i = h (avatar i|| avatar j || PID i|| PID j ||T3) · K i.
• EM 3 = (PID i|| Ver i) ⊕ h (N3|| T3) is calculated.
• U i calculates Req = SYE SK i-st (avatar j, EM 3, T3).
• U i transmits {Req} to St by a public channel.
• St calculates (avatar j, EM 3, T3) = SYD SKi-st (Req).
• St calculates Req i j = SYE SK j-st (EM 3, T3).
• St transmits {Req ij} to U j by a public channel.
• U j calculates (EM 3, T3) = SYD SKj-st (Req i j).
• PK i is retrieved by Uj after U j verifies PID in database.
• U j verifies Ver i · P = ? h (avatar i ||avatar j ||PID i || PID j ||T3) · PK i.
• U j creates T4 and n 4 and calculates N 4 = n 4 · P.
• ver j = h (avatar j||avatar i||PID j ||PID i||T4) · K j.
• EM 4 = (PID j || Ver j) h (N4|| T4).
• Res = SYE SK j – st (avatar i, EM 4, N4, T4).
• U j transmits {Res} to St by a public channel.
• St calculates (avatar i, EM 4, N4, T4) = SYD SK j – st (Res).
• Res i j = SYE SK i-st (EM 4, N4, T4).
• St transmits {Res i j} to U i.
• User i calculates (EM 4, N4, T4) = SYD SK i- st (Res i j).
• (PID j ||Ver j) = EM 4 ⊕ h (N4||T4).
• PK j is retrieved by Ui after Ui verifies PID j in database.
• ver j · P = ? h (avatar j ||avatar i ||PID j||PID i||T4) · PK j is verified.
• If every stage is successfully performed, the U i and U j can demonstrate their ownership of avatar i and avatar j.

6. Security Analysis

6.1. the theft of a smart device, 6.2. offline guessing of passwords, 6.3. impersonation attacks, 6.4. platform server spoofing attacks, 6.5. attacks by replay and mitm, 6.6. forward secrecy, 6.7. insider attacks, 6.8. superior insiders attacks, 6.9. temporary or ephemeral secret leakage attack.

• An adversary obtains the ephemeral secret values n1 and n2 to compute SKi-st.
• Assume that the adversary captures the long-part secret values Xi, ki, and K st to compute SKi-st.
• Without knowing the ephemeral numbers n1 and n2, S1 cannot be obtained.

6.10. User Anonymity

6.11. mutual authentication, 7. conclusions, author contributions, data availability statement, conflicts of interest.

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Share and Cite

Abed, A.Z.M.; Abdelkader, T.; Hashem, M. SLACPSS: Secure Lightweight Authentication for Cyber–Physical–Social Systems. Computers 2024 , 13 , 225. https://doi.org/10.3390/computers13090225

Abed AZM, Abdelkader T, Hashem M. SLACPSS: Secure Lightweight Authentication for Cyber–Physical–Social Systems. Computers . 2024; 13(9):225. https://doi.org/10.3390/computers13090225

Abed, Ahmed Zedaan M., Tamer Abdelkader, and Mohamed Hashem. 2024. "SLACPSS: Secure Lightweight Authentication for Cyber–Physical–Social Systems" Computers 13, no. 9: 225. https://doi.org/10.3390/computers13090225

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