Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item An improved approach towards network forensic investigation of HTTP and FTP protocols(2011) Manesh, T.; Brijith, B.; Singh, M.P.Network packet analysis and reconstruction of network sessions are more sophisticated processes in any network forensic and analysis system. Here we introduce an integrated technique which can be used for inspecting, reordering and reconstructing the contents of packets in a network session as part of forensic investigation. Network analysts should be able to observe the stored packet information when a suspicious activity is reported and should collect adequate supporting evidences from stored packet information by recreating the original data/files/messages sent/received by each user. Thus suspicious user activities can be found by monitoring the packets in offline. So we need an efficient method for reordering packets and reconstructing the files or documents to execute forensic investigation and to create necessary evidence against any network crime. The proposed technique can be used for content level analysis of packets passing through the network based on HTTP and FTP protocols and reports deceptive network activities in the enterprise for forensic analysis. © 2011 Springer-Verlag.Item A novel reversible data embedding method for source authentication and tamper detection of H.264/AVC video(2011) Maiti, S.; Singh, M.P.Authentication of multimedia content is required to proof the data integrity and establish the identity of the content creator. In this paper, we propose a source authentication and tamper detection scheme under the framework of H.264/AVC. To overcome the burden of sending authentication information separately, a novel reversible data embedding method is proposed. In this scheme, content based digital signature is generated and embedded in each frame of the video. Since human eyes are more sensitive to luminance than chrominance components of an image, only luminance components are used in this scheme to produce the digital signature. Proposed scheme is a hard authentication method, which is robust to luminance components of the video sequence. © Springer-Verlag 2011.Item Remote scan using secure automated client server model(2011) Gogikaru, V.; Singh, M.P.In recent years, attackers gain entry into computer systems frequently with the help of Rootkit's. Detection of these Rootkits is not a simple task in early days. To detect Rootkits we need to run many scanning tools manually. This is not feasible many times and it is time consuming process for each client. We propose a secure automated client/server model to scan remote clients present in local area network. This model allows us to run the scanning tools automatically and periodically, to know the Rootkits present in the client system. For our experiment purpose we automated RootkitRevealer tool. © Springer-Verlag 2011.Item Secure similarity based document retrieval system in cloud(2012) Gopal, G.N.; Singh, M.P.The introduction of Cloud computing concept has been instrumental in reducing resource unavailability in cyber world. Privacy of data stored in cloud resources though is a debatable subject and a matter of concern. As the number of documents stored on cloud resources increase, a search engine will have to be employed to search for information. Due to increased concern on these search engines itself misusing data, users are apprehensive about treating cloud resources as a data storage medium. Through this project we try to improve the security in cloud computing by introducing encryption of count list, as well as documents. The search is performed with the hashed keywords. Limitations in cloud computing are addressed by maximizing the utilization of cloud computing resources. © 2012 IEEE.Item AHCSABAC: Attribute value hierarchies and constraints specification in attribute-based access control(Institute of Electrical and Electronics Engineers Inc., 2016) Singh, M.P.Attribute-based access control (ABAC) is well known for flexible policy specification and dynamic decision-making capabilities. Unlike the other access control models, in ABAC, privileges are granted on the basis of the values (e.g., manager, deputy manager, secret, etc.) of the attributes (e.g., role, security level, etc.) of various entities (e.g., users, objects, environment, etc.). Therefore, it is imperative to associate the appropriate values of attributes to entities for ensuring proper access. Although ample amount of research has been done on ABAC, there is a lack of literature that addresses both constraints and the hierarchies of the values of the attributes of users and objects. This paper explores a family of ABAC models, which presents the multiple variations of ABAC, and also proposes a consolidated ABAC model, known as AHCSABAC, which specifies the hierarchies of the values of the attributes of users and various constraints on the values of the attributes of users. The usefulness of AHCSABAC model is then demonstrated. © 2016 IEEE.Item Network Dominating Attack Detection and Mitigation(Institute of Electrical and Electronics Engineers Inc., 2018) Johny, J.; Singh, M.P.In this paper, we address the issue of network dominating attack through which a malicious user tries to grab an unfair share of bandwidth. In this attack, malicious senders on receiving the congestion indication, increase their congestion window instead of decreasing. Random Early Detection (RED), which is one of the current Active Queue Management (AQM) schemes, fails to identify such flow that leads to unfair sharing of bandwidth. Therefore, we present a unique solution which works on top of RED to detect and mitigate the anomalous flows by monitoring the packets present in the gateway buffer. © 2018 IEEE.Item ARBAC: Attribute-enabled role based access control model(Springer Verlag service@springer.de, 2019) Singh, M.P.; Sudharsan, S.; Vani, M.Role Based Access Control (RBAC) is well-known for ease of policy administration, whereas Attribute Based Access Control (ABAC) is renowned for flexible policy specification and dynamic decision making capability. However, they both have some well-known limitations. In this paper, we present an approach that uniquely combines the benefits of RBAC and ABAC. Specifically, our approach associates attribute based rules with roles and permissions that enables the specification of multi-dimensional fine-grained attribute enabled role-based policies. These policies along with rules are also stored as in-memory data, which helps in minimizing the execution time of access requests. Experiments on a wide range of policy data sets demonstrate feasibility and scalability of the proposed approach. © Springer Nature Singapore Pte Ltd. 2019.Item Portable Executable Header Based Ransomware Detection using Power Iteration and Artificial Neural Network(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Karkhur, Y.In the present world, the dependency on different devices connected to the internet is increasing at a rapid rate day by day. Devices like smart watches, mobile phones, personal computers, etc., are part of our day-to-day life. We rely on them for most of our daily tasks. Since these devices are frequently used, they contain users' personal information and other essential data. More and more people use the internet due to emerging technology and intelligent devices, increasing the risk of misusing confidential information and other user-specific crucial data. With the development of cryptocurrency, Ransomware is one of the emerging attacks that prevent authentic users from accessing systems, resources, or data and enables adversaries to control access to such information. This paper presents an Artificial Neural Network (ANN) based model that uses the 'Power Iteration' method and Portable Executable (PE) Headers to detect various types of Ransomware. We analyze the performance of the proposed model by experimenting with a dataset created using the PE files collected from multiple sources and demonstrate its better detection capability. . © 2023 IEEE.Item Artificial Intelligence-Based Model for Detecting Inappropriate Content on the Fly(Springer Science and Business Media Deutschland GmbH, 2023) Ranjan, A.; Pintu; Kumar, V.; Singh, M.P.Social media made it convenient for users to express, communicate, discuss, and exchange their opinions on various issues in recent years. For example, Twitter, YouTube, Facebook, and News portals allow users to express themselves through comments. However, such platforms are being misused in the name of freedom of speech. Numerous improper messages towards specific persons or communities can be found in them that use abusive, vulgar, hostile, or harsh words. Moreover, bots are also involved in exchanging such messages nowadays. As a result, user experiences are sometimes ruined on social media. Therefore, automatic identification and filtering of such offensive messages is a significant issue for improving user experience. This paper proposes a heterogeneous ensemble-based machine learning (ML) model powered by artificial intelligence (AI) that can classify messages into Threat, Obscenity, Insult, Identity Hate, Toxic, and Severe Toxic categories. The experimental evaluation of the proposed model on a standard dataset demonstrates the accuracy and adaptability of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Malware Detection in Android Applications Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Khan, H.K.Android is a widely used smartphone operating system. Because of its open-source architecture, it is becoming increasingly important in our lives. Android applications are now commonly used in several devices like smartphones, smart tv, etc. Due to many different applications and fundamental features, users often trust Android to protect data. However, research has shown that Android is prone to security issues such as malware. Android malware detection is a hot research topic and requires immediate attention and resolution. This research examines the numerous factors of the Android Application Package (APK) and presents a machine learning-based model for detecting malware in Android applications. Experimental analysis of the proposed model using a standard dataset shows that it can be a viable solution in the future. © 2023 IEEE.