Visible to the public Biblio

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Mishra, A., Yadav, P..  2020.  Anomaly-based IDS to Detect Attack Using Various Artificial Intelligence Machine Learning Algorithms: A Review. 2nd International Conference on Data, Engineering and Applications (IDEA). :1—7.
Cyber-attacks are becoming more complex & increasing tasks in accurate intrusion detection (ID). Failure to avoid intrusion can reduce the reliability of security services, for example, integrity, Privacy & availability of data. The rapid proliferation of computer networks (CNs) has reformed the perception of network security. Easily accessible circumstances affect computer networks from many threats by hackers. Threats to a network are many & hypothetically devastating. Researchers have recognized an Intrusion Detection System (IDS) up to identifying attacks into a wide variety of environments. Several approaches to intrusion detection, usually identified as Signature-based Intrusion Detection Systems (SIDS) & Anomaly-based Intrusion Detection Systems (AIDS), were proposed in the literature to address computer safety hazards. This survey paper grants a review of current IDS, complete analysis of prominent new works & generally utilized dataset to evaluation determinations. It also introduces avoidance techniques utilized by attackers to avoid detection. This paper delivers a description of AIDS for attack detection. IDS is an applied research area in artificial intelligence (AI) that uses multiple machine learning algorithms.
Kadhim, Y., Mishra, A..  2019.  Radial Basis Function (RBF) Based on Multistage Autoencoders for Intrusion Detection system (IDS). 2019 1st International Informatics and Software Engineering Conference (UBMYK). :1—4.

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to detect IDS attacks with 98.80% accuracy when validated using UNSW-NB15 dataset. The experimental results show the proposed method presents satisfactory results when compared with those obtained in this field.

Mishra, A., Dixit, A..  2018.  Resolving Threats in IoT: ID Spoofing to DDoS. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–7.

Internet-of-Things (IoT) is a resource-constrained network with machines low on power, processing and memory capabilities. Resource constraints in IoT impact the adoption of protocols for design and validation of unique identity (ID) for every machine. Malicious machines spoof ID to pose as administrative machines and program their neighbour systems in the network with malware. The cycle of ID spoofing and infecting the IP-enabled devices with malware creates an entire network popularly termed as the Botnet. In this paper, we study 6LoWPAN and ZigBee for DDoS and ID spoofing vulnerabilities. We propose a design for generation and validation of ID on such systems called Pseudo Random Identity Generator (PRIG). We compare the performance of PRIG-adapted 6LoWPAN with 6LoWPAN in a simulated personal area network (PAN) model under DDoS stress and demonstrate a 93% reduction in ID validation time as well as an improvement of 67% in overall throughput.

Sachdeva, A., Kapoor, R., Sharma, A., Mishra, A..  2017.  Categorical Classification and Deletion of Spam Images on Smartphones Using Image Processing and Machine Learning. 2017 International Conference on Machine Learning and Data Science (MLDS). :23–30.

We regularly use communication apps like Facebook and WhatsApp on our smartphones, and the exchange of media, particularly images, has grown at an exponential rate. There are over 3 billion images shared every day on Whatsapp alone. In such a scenario, the management of images on a mobile device has become highly inefficient, and this leads to problems like low storage, manual deletion of images, disorganization etc. In this paper, we present a solution to tackle these issues by automatically classifying every image on a smartphone into a set of predefined categories, thereby segregating spam images from them, allowing the user to delete them seamlessly.

Mishra, A., Mathuria, M..  2017.  Multilevel security feature for online transaction using QR code digital watermarking. 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA). 2:48–51.

The utilization of the online services especially the access to Internet Banking services has grown rapidly from last five years. The Internet Banking services provide the customers with the secure and reliable environment to deal with. But with the technology advancement, it is mandatory for the banks to put into practice the ideal technologies or the best security strategies and procedures to authorize or validate the originality of the customers. This must be done to ensure that the data or the information being transmitted during any kind of transaction is safe and no kind of leakage or modification of the information is possible for the intruder. This paper presents a digital watermark method for the QR Code (Quick Response Code) In this, a visible watermark is embedded in the QR Code image using the watermark technology (DCT) and describes the functioning feature of a secure authorization system by means of QR codes & the digital watermark for Internet Banking.

Mishra, A., Kumar, K., Rai, S. N., Mittal, V. K..  2015.  Multi-stage face recognition for biometric access. 2015 Annual IEEE India Conference (INDICON). :1–6.

Protecting the privacy of user-identification data is fundamental to protect the information systems from attacks and vulnerabilities. Providing access to such data only to the limited and legitimate users is the key motivation for `Biometrics'. In `Biometric Systems' confirming a user's claim of his/her identity reliably, is more important than focusing on `what he/she really possesses' or `what he/she remembers'. In this paper the use of face image for biometric access is proposed using two multistage face recognition algorithms that employ biometric facial features to validate the user's claim. The proposed algorithms use standard algorithms and classifiers such as EigenFaces, PCA and LDA in stages. Performance evaluation of both proposed algorithms is carried out using two standard datasets, the Extended Yale database and AT&T database. Results using the proposed multi-stage algorithms are better than those using other standard algorithms. Current limitations and possible applications of the proposed algorithms are also discussed along, with further scope of making these robust to pose, illumination and noise variations.