Truth and Regret in Online Scheduling

We consider a scheduling problem where a cloud service provider has multiple units of a resource available over time. Selfish clients submit jobs, each with an arrival time, deadline, length, and value. The service provider's goal is to implement a truthful online mechanism for scheduling jobs so as to maximize the social welfare of the schedule. Recent work shows that under a stochastic assumption on job arrivals, there is a single-parameter family of mechanisms that achieves near-optimal social welfare. We show that given any such family of near-optimal online mechanisms, there exists an online mechanism that in the worst case performs nearly as well as the best of the given mechanisms. Our mechanism is truthful whenever the mechanisms in the given family are truthful and prompt, and achieves optimal (within constant factors) regret. We model the problem of competing against a family of online scheduling mechanisms as one of learning from expert advice. A primary challenge is that any scheduling decisions we make affect not only the payoff at the current step, but also the resource availability and payoffs in future steps. Furthermore, switching from one algorithm (a.k.a. expert) to another in an online fashion is challenging both because it requires synchronization with the state of the latter algorithm as well as because it affects the incentive structure of the algorithms. We further show how to adapt our algorithm to a non-clairvoyant setting where job lengths are unknown until jobs are run to completion. Once again, in this setting, we obtain truthfulness along with asymptotically optimal regret (within poly-logarithmic factors)

Machine Learning Methodologies For Low-Level Hardware-Based Malware Detection

Malicious software continues to be a pertinent threat to the security of critical infrastructures harboring sensitive information. The abundance in malware samples and the disclosure of newer vulnerability paths for exploitation necessitates intelligent machine learning techniques for effective and efficient malware detection and analysis. Software-based methods are suitable for in-depth forensic analysis, but their on-device implementations are slower and resource hungry. Alternatively, hardware-based approaches are emerging as an alternative approach against malware threats because of their trustworthiness, difficult evasion, and lower implementation costs. Modern processors have numerous hardware events such as power domains, voltage, frequency, accessible through software interfaces for performance monitoring and debugging. But, information leakage from these events are not explored for defenses against malware threats. This thesis demonstrates approach towards malware detection and analysis by leveraging low-level hardware signatures. The proposed research aims to develop machine learning methodology for detecting malware applications, classifying malware family and detecting shellcode exploits from low-level power signatures and electromagnetic emissions. This includes 1) developing a signature based detector by extracting features from DVFS states and using ML model to distinguish malware application from benign. 2) developing ML model operating on frequency and wavelet features to classify malware behaviors using EM emissions. 3) developing an Restricted Boltzmann Machine (RBM) model to detect anomalies in energy telemetry register values of malware infected application resulting from shellcode exploits. The evaluation of the proposed ML methodology on malware datasets indicate architecture-agnostic, pervasive, platform independent detectors that distinguishes malware against benign using DVFS signatures, classifies detected malware to characteristic family using EM signatures, and detect shellcode exploits on browser applications by identifying anomalies in energy telemetry register values using energy-based RBM model.Ph.D

Stability of Service under Time-of-Use Pricing

We consider "time-of-use" pricing as a technique for matching supply and demand of temporal resources with the goal of maximizing social welfare. Relevant examples include energy, computing resources on a cloud computing platform, and charging stations for electric vehicles, among many others. A client/job in this setting has a window of time during which he needs service, and a particular value for obtaining it. We assume a stochastic model for demand, where each job materializes with some probability via an independent Bernoulli trial. Given a per-time-unit pricing of resources, any realized job will first try to get served by the cheapest available resource in its window and, failing that, will try to find service at the next cheapest available resource, and so on. Thus, the natural stochastic fluctuations in demand have the potential to lead to cascading overload events. Our main result shows that setting prices so as to optimally handle the {\em expected} demand works well: with high probability, when the actual demand is instantiated, the system is stable and the expected value of the jobs served is very close to that of the optimal offline algorithm.Comment: To appear in STOC'1

Visceral leishmaniasis escaping the diagnosis and withstanding treatment in a case of recurrent pyrexia

Though visceral leishmaniasis (VL) is the leading parasitic infection causing deatharound the world after malaria, it is a less suspected cause of pyrexia of unknown origin (PUO). We present a case of a middle aged man who was diagnosed with VL only months later owing to the stealthily masquerading disease as also to a generally low index of suspicion for it. A 59-year-old from Uttarakhand presented to us with complaint of fever of a few weeks duration. He was found to have a bicytopenia with elevated liver enzymes. Routine imaging studies were non-contributory. Cultures revealed candidemia while tests for viral and other atypical infections were negative. A bone marrow examination (BME) revealed haemophagocytosis. Positron emission tomography–computed tomography (PET-CT) showed mildly FDG avid hepatosplenomegaly. He was treated as a case of candidiasis with secondary hemophagocytic lymphohistiocytosis (HLH) and was discharged. He was readmitted months later with recurring fever. Repeat investigations revealed pancytopenia with marked hepatosplenomegaly. A repeat BME, however, revealed Leishmania donovani (LD) bodies. Patient was treated with liposomal amphotericin B (LAmB) and discharged. Though the patient’s symptoms improved soon after, he was again admitted a couple of months later and found to have VL persisting in the BM aspirate. This report underscores the need to extensively evaluate cases of PUO rather than summarily dismissing them as routine. VL is one of the less suspected etiologies despite being the second largest parasitic killer

Circulating Antimicrobial Peptide LL-37 Status in Type 1 Diabetes Mellitus and its Relation with Glycemic Control

Antimicrobial-peptides are important molecules of constitutive innate immunity. Though patients with diabetes mellitus are generally prone to infections, there is limited information on their antimicrobialpeptide status. We assessed the circulating LL-37 antimicrobial peptide (also referred as cathelicidin) levels in patients with type 1 diabetes mellitus and its relation with their glycemic status. The LL-37 mRNA expression was assessed in the peripheral blood mononuclear cells (PBMC) by quantitative RTPCR using ß-actin and cytochrome-C1 as the reference genes in 154 subjects (Type 1 diabetes, n=111 and healthy subjects, n=43).  Serum LL-37 was quantified using sandwich-ELISA. Average HbA1c over last 2 years and current HbA1c were used to determine long-term and short-term glycemic status. LL-37 mRNA expression and serum LL-37 levels were correlated with the glycemic status. The LL-37 mRNA copies were comparable between type 1 diabetes and healthy subjects [median (IQR) = 6.7 (1.8–15.28) vs. 7.2 (2.23–21.86), respectively, P = 0.42]. There was no significant difference in serum LL-37 levels between the two groups [median (IQR) = 3.9 (2.88–7.52) vs. 5.0 (3.19–9.05) ng/ml, respectively, P = 0.52]. The LL-37 mRNA and its protein concentration showed no significant correlation with the average or current HbA1c values. The constitutive circulating antimicrobial peptide LL-37 status is not significantly altered in patients with type 1 diabetes mellitus and also not affected by their glycemic status

The Power of Telemetry: Uncovering Software-Based Side-Channel Attacks on Apple M1/M2 Systems

Power analysis is a class of side-channel attacks, where power consumption data is used to infer sensitive information and extract secrets from a system. Traditionally, such attacks required physical access to the target, as well as specialized devices to measure the power consumption with enough precision. The PLATYPUS attack has shown that on-chip power meter capabilities exposed to a software interface might form a new class of power side-channel attacks. This paper presents a software-based power side-channel attack on Apple Silicon M1/M2 platforms, exploiting the System Management Controller (SMC) and its power-related keys, which provides access to the on-chip power meters through a software interface to user space software. We observed data-dependent power consumption reporting from such keys and analyzed the correlations between the power consumption and the processed data. Our work also demonstrated how an unprivileged user mode application successfully recovers bytes from an AES encryption key from a cryptographic service supported by a kernel mode driver in macOS. Furthermore, we discuss the impact of software-based power side-channels in the industry, possible countermeasures, and the overall implications of software interfaces for modern on-chip power management systems.Comment: 6 pages, 4 figures, 5 table