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Robust malware defense in industrial IoT applications using machine learning with selective adversarial samples

- Khoda, Mahbub, Imam, Tasadduq, Kamruzzaman, Joarder, Gondal, Iqbal, Rahman, Ashfaqur

  • Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industry Applications Vol.56, no 4. (2020), p. 4415-4424
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  • Description: Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.

Provisioning delay sensitive service in cognitive radio networks with multiple radio interfaces

- Hasan, Rashidul, Murshed, Manzur

  • Authors: Hasan, Rashidul , Murshed, Manzur
  • Date: 2011
  • Type: Text , Conference proceedings
  • Relation: Proceedings of the 2011 IEEEE Wireless Communications and Networking Conference (WCNC 2011), 28th-31st March, 2011, New York p 162-167
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  • Description: Cognitive radio network (CRN) users are inherently expected to experience widely-varied delays due to the uncertainty in wireless channel availability. Supporting delay sensitive real-time services through CRNs, so that visitors are allowed to experience full-scale networking services by opportunistically sharing the spectrum from a number of existing networks without impacting on the primary users, thus remains a challenging task. This paper presents a novel technique to provision QoS guarantee for delay-sensitive services in CRNs having secondary users equipped with multiple radio interfaces. The technique relies on modeling spectrums holes from multiple primary networks through a resultant channel to enable implementing a single server queuing model with random service interruption. Simulation results using ns-2.33 show that using multiple radio interfaces has sheer strength to reduce CRN delay with fewer number of primary channel sensing.

Predicting Hot-Spots in distributed cloud databases using association rule mining

- Kaml, Joarder, Murshed, Manzur, Gaber, Mohamed

  • Authors: Kaml, Joarder , Murshed, Manzur , Gaber, Mohamed
  • Date: 2014
  • Type: Text , Conference paper
  • Relation: IEEE/ACM 7th International Conference Utility and Cloud Computing (UCC), 2014; London; 8-11th December, 2014 p. 800-805
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  • Description: Data partitioning is a popular technique to horizontally or vertically split table attributes of a Cloud database cluster to evenly distribute increasing workloads. However, hot-spots can be created due to inappropriate partitioning scheme and static partition management without considering the dynamic workload characteristics. In this paper, an automatic database partition management scheme - APM - is proposed which periodically analyses workload logs to predict the formation of any potential hot-spot using association rule mining. A detailed illustration of the proposed scheme is presented with examples along with a cost model following by experimental observations from running a HBase cluster with YCSB workloads in AWS.

Frame interpolation for cloud-based mobile video streaming

- Usman, Muhammad, Xiangjian, He, Kin-Man, Lam, Min, Xu, Bokhari, Syed, Jinjun, Chen

  • Authors: Usman, Muhammad , Xiangjian, He , Kin-Man, Lam , Min, Xu , Bokhari, Syed , Jinjun, Chen
  • Date: 2016
  • Type: Text , Journal article
  • Relation: IEEE transactions on multimedia Vol. 18, no. 5 (2016), p. 831-839
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  • Description: Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds.

Anti-analysis trends in banking malware

- Black, Paul, Opacki, Joseph

  • Authors: Black, Paul , Opacki, Joseph
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 2016 11th International Conference on Malicious and Unwanted Software (MALWARE), Fajardo, PR, USA, 18-21 October 2016 p. 1-7
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  • Description: Banking Malware, has become a popular and ever more prevalent mechanism to monetise malware development. Since the development of the Zeus malware kit in 2007, the frequency and complexity of banking malware has been increasing. Developing a good understanding of the operation of a malware family is a first step in the reverse engineering required to create tools to extract the malware configuration, which is used in the remediation of malware infrastructure. This reverse engineering process in recent years has become increasingly challenging. This manuscript provides a brief summary of the reverse engineering of banking malware families over a two year period and emphasises the anti-analysis techniques employed by the authors of six families of banking malware. The manuscript presents this analysis, and examines trends in the development of these anti-analysis techniques.

P2DCA: A Privacy-preserving-based data collection and analysis framework for IoMT applications

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Chen, Jinjun

  • Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE journal on selected areas in communications Vol. 37, no. 6 (2019), p. 1222-1230
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  • Description: The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.

Error concealment for cloud-based and scalable video coding of hd videos

- Usman, Muhammad, He, Xiangjian, Lam, Kin-Man, Xu, Min, Bokhari, Syed, Chen, Jinjun, Jan, Mian

  • Authors: Usman, Muhammad , He, Xiangjian , Lam, Kin-Man , Xu, Min , Bokhari, Syed , Chen, Jinjun , Jan, Mian
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE transactions on cloud computing Vol. 7, no. 4 (2019), p. 975-987
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  • Description: The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.
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