Post-quantum cryptography Algorithm's standardization and performance analysis (International Journal Array, Elsevier)
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Manish Kumar holds a Ph.D. in Computer Science from Bangalore University, Bangalore. Before joining RV University, he had worked for 16 years at M S Ramaiah Institute of Technology in the Computer Applications Department. His areas of specialization include Information Security and Digital Forensics. He is also a Subject Matter Expert (Cyber Security), IBM-Coursera, Edx. He has published many research papers in reputed conferences and journals. In addition to his academic role, he is actively involved in research and consultancy. He regularly conducts hands-on workshops, technical talks, and training for engineering institutions, researchers, faculty members, law enforcement agencies, and the judiciary. He serves as a technical expert on various committees formed by the Karnataka State Government to establish cyber forensics labs for law enforcement agencies. He is a life member of the Computer Society of India (CSI), the Indian Society for Technical Education (ISTE), and the Indian Science Congress Association (ISCA). He is also a member of the International Association of Engineers (IAE) and a senior member of the ACM.
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Abstract:- -Quantum computer is no longer a hypothetical idea. It is the world's most important technology and there is a race among countries to get supremacy in quantum technology. It is the technology that will reduce the computing time from years to hours or even minutes. The power of quantum computing will be a great support for the scientific community. However, it raises serious threats to cybersecurity. Theoretically, all the cryptography algorithms are vulnerable to attack. The practical quantum computers, when available with millions of qubits capacity, will be able to break nearly all modern public-key cryptographic systems. Before the quantum computers arrive with sufficient ‘qubit’ capacity, we must be ready with quantum-safe cryptographic algorithms, tools, techniques, and deployment strategies to protect the ICT infrastructure. This paper discusses in detail the global effort for the design, development, and standardization of various quantum-safe cryptography algorithms along with the performance analysis of some of the potential quantum-safe algorithms. Most quantum-safe algorithms need more CPU cycles, higher runtime memory, and a large key size. The objective of the paper is to analyze the feasibility of the various quantum-safe cryptography algorithms.
Abstract:- The number of complex and novel malware attacks is increasing exponentially in the cyberworld. Malware detection systems are facing new challenges due to the volume, velocity, and complexity of malware. The current malware detection system relies on a time-consuming, resource-intensive, and knowledge-intensive classification approach. Most of the existing malware detection system is ineffective in detecting novel malware attacks. A deep learning approach can be used to build a malware detection system that can effectively detect novel malware attacks without much human intervention. The current circumstance necessitates not just a malware system with excellent accuracy, but also one that can serve a large volume of demand in near real-time. A scalable malware detection system capable of detecting complex attacks is the need of time. This article discusses a scalable and distributed deep learning approach for malware detection using convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM). The deep learning approach has been used to make the system learn and make predictive decisions without human intervention. The performance of the deep learning approach depends on various parameters and training data sets. Hence, different combinations of deep learning algorithms have been used to design and test the models to achieve the desired result. The experimental results show that the double layer of CNN and BiLSTM has better performance than single-layer CNN.
Abstract:- Collection and analysis of digital evidence from mobile phones plays vital role to solve many civil and criminal cases. This evidence is a potential source of information which helps the prosecutors win more conviction. Sometime it also helps to crack organised crime and terrorist activities. Digital forensics expert need specialised tools and techniques to extract the evidence from mobile phones for analysis. Extracting the evidence from mobile phones in forensically sound manner is never been an easy task, as the entire process must ensure the integrity of evidence and its admissibility in the court of law. Rapid advancement in technology and frequent release of latest make and models always poses new challenges for the investigator. There are various tools and techniques available for mobile forensics, which are classified based on its complexity and its physical characteristics. Forensics examiner need to assess the complexity of the case and select the tools accordingly. This paper discusses in-detail about the systematic approach, which can be used for mobile forensics. Each approach has its own advantages, disadvantages, cost and complexities which is highlighted in the paper along with the list of standard tools and their key features.
Abstract:- Computer, Internet, and Smartphone have changed our life as never before. Today, we cannot even imagine our life without these technologies. If we look around, we find everything, everywhere connected and controlled by system and software. We find amazing software and mobile applications which have become nerve of our daily life. Our dependency on this software and systems is so and so much that it is scary even to imagine, what if this system fails at any point in time. There is always a threat surrounded by various types of cyber-attacks. Every day cybercriminals are evolving their attacking strategy. Cyber-attacks using ever-more sophisticated malware are the major cause of concern for all types of users. Cyber-world has witnessed rapid changes in malware attacking strategy in the recent past. The volume, velocity, and complexity of malware are posing new challenges for malware detection systems. A scalable malware detection system with the capability to detect complex attacks is the time of need. In this paper, we have proposed a scalable malware detection system using big data and a machine learning approach. The machine learning model proposed in the system is implemented using Apache Spark which supports distributed learning. Locality-sensitive hashing is used for malware detection, which significantly reduces the malware detection time. A five-stage iterative process has been used to carry out the implementation and experimental analysis. The proposed model shown in the paper has achieved 99.8% accuracy. The proposed model has also significantly reduced the learning and malware detection time compared to models proposed by other researchers.
Abstract:- Solid state drive (SSD) is rapidly replacing hard disk drive (HDD) in almost all computing devices. With the exponential growth in SSD technologies, it is quite possible that very soon HDD will become obsolete. It is good news for the end-users, but may not be so pleasant for the digital forensics examiners. It has been a challenge for the cyber-crime investigator ever since the evolution of SSD technology. The intrinsic characteristics of SSD are not very supportive for forensic examiners. The TRIM function and background garbage process make it difficult to retrieve deleted artifacts from the SSD. The traditional disk write blocker cannot stop the background process. There is a lot of uncertainty involved in SSD data acquisition. Sometimes it is also difficult to prove the integrity of SSD in the court of law which makes the SSD's legal admissibility questionable. The objective of this article is to examine the uncertainty involved in SSD forensics with experimental analysis. This article discusses in detail the different components of SSD and its working principle followed by experimental forensics analysis, critical observations, guidelines, and recommendations.
