Publications by authors named "Nir Nissim"

In today's email dependent world, cyber criminals often target organizations using a variety of social engineering techniques and specially crafted malicious emails. When successful, such attacks can result in significant harm to physical and digital systems and assets, the leakage of sensitive information, reputation damage, and financial loss. Despite the plethora of studies on the detection of phishing attacks and malicious links in emails, there are no solutions capable of effectively, quickly, and accurately coping with more complex email-based attacks, such as malicious email attachments.

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Many patients with diabetes are currently being treated with insulin pumps and other diabetes devices which improve their quality of life and enable effective treatment of diabetes. These devices are connected wirelessly and thus, are vulnerable to cyber-attacks which have already been proven feasible. In this paper, we focus on two types of cyber-attacks on insulin pump systems: an overdose of insulin, which can cause hypoglycemia, and an underdose of insulin, which can cause hyperglycemia.

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Article Synopsis
  • - Patients with life-threatening arrhythmias often rely on cardiac implantable electronic devices (CIEDs), such as pacemakers and ICDs, but these devices are increasingly susceptible to cyber-attacks due to their advanced features and connectivity.
  • - A serious type of threat is data manipulation attacks from compromised programmer devices that can alter the functioning of CIEDs, potentially jeopardizing patients' lives and quality of life.
  • - The paper introduces Cardio-ML, a machine learning-based system designed to detect both known and unknown attacks on ICDs, demonstrating high detection capabilities with a perfect classification rate for known attacks and an 80% success rate for zero-day attacks, outperforming existing methods.
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Since the beginning of the 21st century, the use of cloud computing has increased rapidly, and it currently plays a significant role among most organizations' information technology (IT) infrastructure. Virtualization technologies, particularly virtual machines (VMs), are widely used and lie at the core of cloud computing. While different operating systems can run on top of VM instances, in public cloud environments the Linux operating system is used 90% of the time.

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Steganography is the art of embedding a confidential message within a host message. Modern steganography is focused on widely used multimedia file formats, such as images, video files, and Internet protocols. Recently, cyber attackers have begun to include steganography (for communication purposes) in their arsenal of tools for evading detection.

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This paper presents TrustSign, a novel, trusted automatic malware signature generation method based on high-level deep features transferred from a VGG-19 neural network model pretrained on the ImageNet dataset. While traditional automatic malware signature generation techniques rely on static or dynamic analysis of the malware's executable, our method overcomes the limitations associated with these techniques by producing signatures based on the presence of the malicious process in the volatile memory. By leveraging the cloud's virtualization technology, TrustSign analyzes the malicious process in a trusted manner, since the malware is unaware and cannot interfere with the inspection procedure.

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Today, personal medical devices (PMDs) play an increasingly important role in healthcare ecosystems as patient life support equipment. As a result of technological advances, PMDs now encompass many components and functionalities that open the door to a variety of cyber-attacks. In this paper we present a taxonomy of ten widely-used PMDs based on the five diseases they were designed to treat.

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Background And Objectives: Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning.

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Classification of condition severity can be useful for discriminating among sets of conditions or phenotypes, for example when prioritizing patient care or for other healthcare purposes. Electronic Health Records (EHRs) represent a rich source of labeled information that can be harnessed for severity classification. The labeling of EHRs is expensive and in many cases requires employing professionals with high level of expertise.

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