Mamoona Naveed Asghar

Mugheez Asif, Abdul Manan, Abdul Moiz ur Rehman et al.

In today's contemporary digital landscape, chatbots have become indispensable tools across various sectors, streamlining customer service, providing personal assistance, automating routine tasks, and offering health advice. However, their potential remains underexplored in the realm of network security, particularly for intrusion detection. To bridge this gap, we propose an architecture chatbot specifically designed to enhance security within edge networks specifically for intrusion detection. Leveraging advanced machine learning algorithms, this chatbot will monitor network traffic to identify and mitigate potential intrusions. By securing the network environment using an edge network managed by a Raspberry Pi module and ensuring ethical user consent promoting transparency and trust, this innovative solution aims to safeguard sensitive data and maintain a secure workplace, thereby addressing the growing need for robust network security measures in the digital age.

Syed Sahil Abbas Zaidi, Mohammad Samar Ansari, Asra Aslam et al.

Object Detection is the task of classification and localization of objects in an image or video. It has gained prominence in recent years due to its widespread applications. This article surveys recent developments in deep learning based object detectors. Concise overview of benchmark datasets and evaluation metrics used in detection is also provided along with some of the prominent backbone architectures used in recognition tasks. It also covers contemporary lightweight classification models used on edge devices. Lastly, we compare the performances of these architectures on multiple metrics.

Rizwan A. Shah, Mamoona N. Asghar, Saima Abdullah et al.

To avoid delays arising from a need to decrypt a video prior to transcoding and then re-encrypt it afterwards, this paper assesses a selective encryption (SE) content protection scheme. The scheme is suited to both recent standardized codecs, namely H.264/Advanced Video Coding (AVC) and High Efficiency Video Coding (HEVC). Specifically, the paper outlines a joint crypto-transcoding scheme for secure transrating of a video bitstream. That is to say it generates new video bitrates, possibly as part of an HTTP Adaptive Streaming (HAS) content delivery network. The scheme will reduce the bitrate to one or more lower desired bit-rate without consuming time in the encryption/decryption process, which would be the case when full encryption is used. In addition, the decryption key no longer needs to be exposed at intermediate middleboxes, including when transrating is performed in a cloud datacenter. The effectiveness of the scheme is variously evaluated: by examination of the SE generated visual distortion; by the extent of computational and bitrate overheads; and by choice of cipher when encrypting the selected elements within the bitstream. Results indicate that there remains: a content; quantization level (after transrating of an encrypted video); and codec-type dependency to any distortion introduced. A further recommendation is that the Advanced Encryption Standard (AES) is preferred for SE to lightweight XOR encryption, despite it being taken up elsewhere as a real-time encryption method.

Amna Shifa, Mamoona Naveed Asghar, Naila Batool et al.

The future generation networks: Internet of things (IoT), in combination with the advanced computer vision techniques poses new challenges for securing videos for end-users. The visual devices generally have constrained resources in respects to their low computation power, small memory with limited power supply. Therefore, to facilitate the video security in smart environment, lightweight security schemes are required instead of inefficient existing traditional cryptography algorithms. This research paper provides the solution to overcome such problems. A novel lightweight cipher algorithm is proposed here which targets multimedia in IoT with an in-house name EXPer i.e. Extended permutation with eXclusive OR (XOR). EXPer is a symmetric stream cipher that consists of simple XOR and left shift operations with three keys of 128 bits. The proposed cipher algorithm has been tested on various sample videos. Comparison of proposed algorithm has been made with the traditional cipher algorithms XOR and Advanced Encryption Standard (AES). Visual results confirm that EXPer provides security level equivalent to the AES algorithm with less computational cost than AES. Therefore, it can easily be perceived that the EXPer is a better replacement of AES for securing real-time video applications in IoT.